Latest papers

$\Lambda$ effect in rotating hydrodynamic convection

This study uses 3D simulations of rotating hydrodynamic convection to demonstrate that thermal Rossby waves drive outward radial angular momentum transport at rapid rotation rates, revealing a significant discrepancy between these findings and the assumptions underlying current mean-field theories of solar differential rotation.

$\mathcal{R}^2$-corrected Tachyon Scalar Field Inflation, the ACT Data, and Phantom Transition

This paper demonstrates that an $\mathcal{R}^2$-corrected tachyonic scalar field inflation model with a rescaled Einstein-Hilbert term can successfully achieve a phantom divide line crossing and remain compatible with ACT data, provided the effective gravitational constant during inflation is stronger than standard Einstein-Hilbert gravity.

16 new quasars at the end of the reionization unveiled by self-supervised learning

By applying self-supervised contrastive learning and spectral energy distribution fitting to DESI Legacy Survey images, researchers overcame the challenge of foreground contamination to identify and spectroscopically confirm 16 new high-redshift quasars ($z=5.94$–6.45), including three missed by traditional methods, thereby demonstrating a scalable framework for uncovering rare early-universe sources.

A Modified Conveyor Belt Model: Implications for Surface Density Thresholds for Massive Star Formation

This paper utilizes a modified conveyor belt model to demonstrate that standard surface density thresholds can misclassify early-stage high-mass clumps, revealing that the total cumulative mass accreted over time is the critical factor in distinguishing high-mass from intermediate-mass star-forming regions.

A Multi-Level Parallel Pipeline for SPHERE-3 Detector Simulation: From EAS Generation to Image Approximation

This paper presents a multi-level parallel software pipeline that achieves linear scaling for SPHERE-3 detector simulations by leveraging the natural atomicity of independent event processing across CORSIKA, Geant4, and Python-based image approximation stages without requiring locks on hot paths.

A Robust Geometric Distortion Solution for Main Survey Camera of CSST

This paper presents a robust Weighted Polynomial Distortion Correction in 2-Phase (WPDC-2P) method that significantly enhances astrometric precision for the Chinese Space Station Survey Telescope (CSST) by combining distance-based weighting, look-up tables, and polynomial fitting to effectively correct geometric distortions up to 200 pixels, achieving sub-pixel accuracy in both simulated and real-world observations.

A second visit to Eps Ind Ab with JWST: new photometry confirms ammonia and suggests thick clouds in the exoplanet atmosphere of the closest super-Jupiter

New JWST/MIRI observations of the cold super-Jupiter Eps Ind Ab confirm the presence of ammonia while revealing a suppressed feature and fainter-than-expected near-infrared emission, which the authors attribute to the presence of thick water-ice clouds in its atmosphere.

Accretion Disk Evolution in GX 339-4 Across Spectral States Using NuSTAR, NICER, and Insight-HXMT Observations

This study utilizes simultaneous NuSTAR, NICER, and Insight-HXMT observations of GX 339-4's 2021 outburst to demonstrate that incorporating a warm Comptonization component in the hard state resolves discrepancies in disk normalization, thereby supporting a dual-corona accretion geometry where the disk is physically truncated and cooler compared to the soft state.

Accretion Geometry of Black Hole X-ray Binaries: Insights from X-ray Observations

This short review summarizes X-ray techniques for measuring accretion geometry in black hole X-ray binaries, highlighting findings that accretion disks can extend close to the innermost stable circular orbit in the bright hard state and discussing the connections between disks, coronas, and jets.

Accurate spectroscopic redshift estimation using non-negative matrix factorization: application to MUSE spectra

This paper presents a data-driven method using Non-negative Matrix Factorization to accurately estimate spectroscopic redshifts for MUSE galaxy spectra with a 93.7% success rate, while also enabling the separation of true sources and detection of blended objects.

Analysis of Tidal Perturbations Due to Asymmetric Response of LARES 2 and LAGEOS

This study quantitatively analyzes asymmetric tidal perturbations on the LARES 2 and LAGEOS satellites by identifying 83 significant Earth tide constituents and demonstrating that the cumulative effect of the remaining minor constituents is non-negligible, thereby providing essential parameters for high-precision orbital dynamics and fundamental physics tests like the Lense-Thirring effect.

Atmospheric Collapse and Habitability on Tidally-Locked Exoplanets

Using a three-dimensional global climate model, this study reveals that atmospheric collapse on tidally-locked exoplanets can paradoxically sustain dayside surface liquid water by weakening day-night heat transport, thereby preventing the loss of dayside insolation to the nightside despite the reduction in greenhouse warming.

Beyond Fermi-II: Intermittent Particle Acceleration by Relativistic Turbulence in Astrophysical Plasmas

This paper introduces the STRIPE Monte Carlo framework to demonstrate that relativistic, high-amplitude turbulence drives intermittent particle acceleration capable of producing the hard TeV-PeV gamma-ray spectra observed in LHAASO-detected microquasars, offering a more realistic alternative to traditional Fermi-II models.

Binary Black Hole inspirals cannot hide their eccentricity

This paper introduces a fast, phenomenological method using time-frequency domain energy analysis and Bayesian-inspired sampling to rapidly constrain orbital eccentricity in binary black hole inspirals, demonstrating the ability to achieve estimates within 0.2 of the true value in just five minutes.

Black Hole Properties of Type-1 Active Galactic Nuclei in the North Ecliptic Pole Wide Field: I. Mid-infrared Sources with Optical Counterparts

This paper presents reliable black hole property estimates for approximately 450 Type-1 active galactic nuclei in the North Ecliptic Pole Wide field by utilizing mid-infrared continuum luminosities and optical line widths to effectively mitigate dust extinction effects, thereby providing crucial fiducial data for future infrared spectroscopic missions and multi-wavelength AGN studies.

Characterizing the 3D evolution of two successive CMEs heading for Mercury

This study utilizes multi-view observations and a revised cone model to characterize the three-dimensional geometry and kinematics of two successive coronal mass ejections from active region 12994, revealing their large angular extents and propagation paths toward Mercury to improve future impact predictions for solar planets.

Characterizing the Instrumental Profile of LAMOST

This paper presents a multi-layer perceptron model based on The Payne to characterize the instrumental profile of the LAMOST telescope, demonstrating that applying this derived profile to stellar radial velocity measurements reduces dispersion by approximately 3 km/s and thereby enhancing the search for long-period binary stars.

Comprehensive neutrino light curves and spectra: from pre-supernova evolution to early supernova phase

This paper presents the first systematic study of neutrino emissions from massive stars (10–40 $M_\odot$) spanning pre-supernova evolution to the early core-collapse phase, revealing that neutrino luminosities and spectra strongly correlate with progenitor compactness and core mass, thereby offering a robust method to infer internal stellar structure through joint observational analysis.

Constraints on Neutrino Mass with Void Weak Lensing Effect

This study demonstrates that void weak lensing, derived from the cross-correlation of cosmic voids and galaxy shear, provides a promising and independent constraint on the total neutrino mass with a linear signal-mass relationship, achieving a precision of $\sigma(M_{\nu}) \approx 0.096$ eV in ideal conditions and $0.340$ eV under Stage-III-like noise levels.

Correcting Ionospheric Faraday Rotation for the VLA and MeerKAT

This paper demonstrates that while traditional global VTEC maps significantly overestimate Ionospheric Faraday Rotation for the VLA and MeerKAT, utilizing the ALBUS software with local GNSS station data yields highly accurate corrections, while also establishing the intrinsic electric vector position angles of standard calibrators 3C286 and 3C138 across a broad frequency range.

Cosmological Spacetimes with Sign-Changing Spatial Curvature and Topological Transitions

This paper investigates cosmological spacetimes where the spatial curvature $k$ is promoted to a time-dependent function, demonstrating that such sign-changing transitions—which allow for topological changes from closed to open universes while avoiding infinite initial energy densities—are consistent with global hyperbolicity under specific conditions and constructing three distinct geometries that exhibit these properties.

CubeSats Reach the Millisecond X-Ray Domain: Crab Pulsar Timing with SpIRIT/HERMES

The SpIRIT CubeSat's HERMES instrument successfully demonstrated millisecond-level X-ray timing capabilities by detecting the double-peaked pulse profile of the Crab pulsar, proving that compact CubeSat payloads can achieve high-energy observational performance previously reserved for flagship space observatories.

Data reduction method for OPTICAM multiband time series of transiting exoplanets

This paper presents a data reduction methodology for the OPTICAM instrument that utilizes a 3×3-pixel median filter to effectively mitigate unpredictable warm pixels in sCMOS detectors, thereby improving photometric precision and Bayesian evidence for multiband transiting exoplanet light curves.

Deep Learning Search for Gravitational Waves from Compact Binary Coalescence

This paper proposes a hybrid deep learning approach that combines matched-filtering concepts with Convolutional Neural Networks to achieve detection efficiency comparable to standard methods while significantly reducing computational costs for future gravitational-wave searches.

Detection and Astrometry of the Ba-Bb Subsystem in $\alpha$ Piscium: First Dual-Field Interferometry at the CHARA Array

This paper reports the first on-sky demonstration of dual-field interferometry at the CHARA Array, which successfully resolved the inner Ba-Bb subsystem of $\alpha$ Piscium to determine precise dynamical masses for the near-twin F-type stars and validated the facility's capability for sub-mas astrometry on arcsecond-scale binaries.

Detection of afterglow emission up to 100 GeV through a stacking analysis of gamma-ray bursts

This paper reports the detection of high-energy gamma-ray emission up to 100 GeV from a stacked sample of 330 gamma-ray bursts using Fermi-LAT data, revealing that while individually detected bursts align with standard afterglow models, individually undetected bursts suggest the presence of a previously unobserved energy injection effect in the GeV band.

Discovery of a compact hierarchical triple main-sequence star system while searching for binary stars with compact objects

This paper reports the discovery and characterization of G1010, a compact hierarchical triple main-sequence star system identified through a combination of Gaia data, multi-epoch spectroscopy, and TESS light curve analysis, which initially appeared to host a massive compact object but was confirmed to consist of a primary star and an eclipsing inner binary.

Do the Amati and Yonetoku Relations Evolve with Redshift for Swift GRBs?

By analyzing a sample of 241 Swift long gamma-ray bursts, this study demonstrates that the Amati and Yonetoku relations exhibit no significant redshift evolution and actually yield better fits at high redshifts, confirming their reliability as cosmological probes.

Dual Cutler-Vallisneri Corrections: Mitigating PSD Drift in Zero-Latency Gravitational-Wave Searches

This paper develops a perturbative framework extending the Cutler-Vallisneri formalism to derive analytic corrections for timing, phase, and SNR biases caused by power spectral density drift in zero-latency gravitational-wave searches, demonstrating that these corrections are essential to prevent significant sky-localization errors and detection losses when using minimum-phase whitening.

Dynamics of thin accretion disks and accretion around a charged-PFDM black hole

This paper investigates the dynamical behavior of steady spherical accretion and thin accretion disks around a magnetically charged black hole embedded in perfect fluid dark matter, using M87* shadow observations to constrain parameters and revealing that while local radiative flux and temperature are reduced, the overall radiative efficiency and luminosity are enhanced compared to a Schwarzschild black hole.

Electron densities and filling factors of extragalactic HII regions: NGC 2403 and NGC 628

This paper presents a new image-segmentation methodology to construct homogeneous HII region catalogues in NGC 2403 and NGC 628, revealing low volume filling factors and tentative scaling relations between electron densities and galaxy properties that offer new constraints for massive cluster formation models and high-redshift interpretations.

Empirical flare energy limits for the largest historical sunspots

By extrapolating statistical relationships between flare ribbon areas and energy from modern observations to the largest recorded sunspot group (April 1947), this study empirically estimates that the Sun is capable of producing flares with bolometric energies reaching a few times 10^34 erg, comparable to superflares observed on other Sun-like stars.

Epicyclic Density Variations in the Indus Stellar Stream

This study analyzes the Indus stellar stream using Gaia data and N-body simulations to demonstrate that its observed longitudinal density fluctuations are primarily caused by natural epicyclic motions from tidal disruption rather than dark matter subhalo interactions, with the moderate sharpness of these peaks suggesting the progenitor dwarf galaxy originally possessed a cuspy dark matter halo.

Euclid Quick Data Release (Q1) -- Characteristics and limitations of the spectroscopic measurements

This paper evaluates the performance of the Euclid Quick Data Release (Q1) spectroscopic processing function by comparing it with DESI survey data, demonstrating high redshift accuracy and precision while emphasizing the necessity of strict quality criteria to ensure an 89% success rate for cosmological targets in the $0.9 < z < 1.8$ range.

Exploring the $S_8$ Tension: Insights from the CatNorth 1.5-Million Quasar Candidates

Using a machine-learning-enhanced CatNorth quasar catalog and Planck CMB lensing data, this study derives low-redshift $S_8$ measurements that are generally consistent with the standard $\Lambda$CDM model, thereby showing reduced evidence for the persistent $S_8$ tension compared to previous weak lensing results.

FEAST: a NIRSpec/MOS survey of emerging young star clusters in NGC 628

This paper presents the first JWST/NIRSpec multiplex spectroscopy results from the FEAST program in NGC 628, demonstrating the telescope's ability to resolve the spectral properties of emerging young star clusters and their surrounding interstellar medium, thereby revealing a photoionization-dominated feedback regime driven by massive stars before supernovae occur.

Far-infrared Polarization Properties of Nearby Star-forming Regions: A New Compendium of SOFIA/HAWC+ Observations

This paper presents a comprehensive polarimetric study of 26 nearby molecular clouds using SOFIA/HAWC+ archival data, revealing that far-infrared dust polarization spectra depend strongly on column density and observational resolution, while indicating that magnetic fields in these parsec-scale regions are decoupled from the large-scale Galactic field.

Fast X-ray Transients produced by Off-axis Jet-Cocoons from Long Gamma-Ray Bursts

This paper proposes that fast X-ray transients (FXTs) are produced by off-axis jet-cocoons from long gamma-ray bursts, demonstrating through numerical simulations that viewing angles of 10°–20° naturally explain the observed X-ray luminosity, duration, and soft spectra, while also predicting a simultaneous UV flash and a subsequent bright optical plateau preceding the supernova emission.

Forward-modelling Milky Way Cepheids: selection effects and physical priors in the Gaia-HST calibration

This paper presents a fully forward-modelled Bayesian framework for calibrating Milky Way Cepheids using Gaia EDR3 data, demonstrating that explicitly accounting for Galactic geometry and survey selection effects is essential to avoid biases in the period-luminosity relation and robustly support the local distance-ladder determination of $H_0$.

Freezing of the renormalized one-loop primordial scalar power spectrum

Using the effective field theory of inflationary fluctuations, this paper explicitly proves for the first time that the renormalized one-loop power spectrum of the primordial curvature perturbation freezes exactly on super-horizon scales, thereby confirming the quantum-level validity of the conservation law essential for inflationary predictions.

From planetesimals to planets with N-body simulations in the giant-planet formation region

Using GPU-accelerated N-body simulations with a new pebble accretion module, the authors demonstrate that wide-orbit giant planets can form from streaming-instability-derived planetesimals regardless of their initial radial distribution, as rapid dynamical scattering and pebble flux filtering drive core growth while naturally producing scattered discs and rare giant impacts within the first 100 Myr.

GRB 221009A: Observations with LST-1 of CTAO and Implications for Structured Jets in Long Gamma-Ray Bursts

Using the LST-1 telescope of the Cherenkov Telescope Array, researchers detected a statistically significant excess of very-high-energy gamma rays from GRB 221009A approximately 1.33 days after the burst, a finding that constrains theoretical models of structured jets by disfavoring those predicting flux levels significantly above $10^{-11}$erg cm$^{-2}$s$^{-1}$ at that epoch.

Gamma-ray Signatures of r-Process Radioactivity from the Collapse of Magnetized White Dwarfs

This paper predicts that the accretion-induced collapse of magnetized white dwarfs produces distinctive gamma-ray signatures from both r-process and iron-peak nuclei, which planned MeV gamma-ray telescopes could detect out to distances of approximately 30 Mpc, thereby offering a unique observational method to distinguish these events from binary neutron star mergers.

Going Wide and Deep with Roman: The z~6-9 UV luminosity function in a Roman Deep Field

This paper presents a trade study for the Nancy Grace Roman Space Telescope, recommending an ultra-deep survey covering at least 0.56 square degrees with all six filters to optimize the measurement of the high-redshift (z~6–9) UV luminosity function and significantly reduce uncertainties in rest-UV luminosity density compared to existing JWST programs.

HAWC J0630+186 Could Not Be Powered by PSR J0630+19

Using follow-up timing observations from the FAST telescope, this study determines that the pulsar PSR J0630+19 is too old and lacks sufficient spin-down luminosity to power the very-high-energy gamma-ray emissions of the source 3HWC J0630+186, thereby ruling out an association between them.

HI Observations of Giant Low Surface Brightness Galaxies

This study presents HI observations of 19 giant low surface brightness galaxies, revealing asymmetric gas disks and low gas content that, when compared with NIHAO simulations showing similar features in merger remnants, suggests these galaxies' extended optical disks may have formed following recent major mergers.

Half-year Evolution of a Decaying Solar Active Region and Peripheral Dimming Regions

This study utilizes multi-wavelength SDO observations to track the six-month decay of solar active region NOAA AR 12738, revealing that a peripheral dimming region's continuous areal decrease is driven by a distinct thermal deficit in the 10$^{5.5}$–10$^{5.9}$ K range rather than merely a lack of plasma, thereby offering new insights into active region thermal evolution and magnetic restructuring.

Hidden Vela Supercluster Revealed by First Hybrid Redshift & Peculiar Velocity Reconstruction

By combining a hybrid reconstruction of 65,518 galaxy peculiar velocities with 8,283 new redshifts—including 2,176 high-sensitivity HI measurements from the SARAO MeerKAT telescope to penetrate the southern Zone of Avoidance—this study reveals the Vela Supercluster as a dominant mass concentration rivaling the Shapley Concentration, thereby providing the most complete and dynamically consistent picture of the southern extragalactic sky to date.

Joint Bayesian Source and Lens Reconstruction for Multi-messenger Binary Black Holes

This paper introduces *silmarel*, the first software package designed to jointly reconstruct gravitational wave sources and their lensing galaxies by integrating data from gravitational wave detectors with electromagnetic surveys like Euclid and Hubble, thereby enabling the identification of lensed binary black hole events in the era of multi-messenger astronomy.

Joint Diagnostics of Circumsolar Sky Brightness Using Coronagraphic Measurements and Aerosol Optical Inversions at Mauna Loa

This study validates a method for estimating circumsolar sky brightness by demonstrating quantitative agreement between direct coronagraphic measurements and aerosol-inferred radiance at Mauna Loa, thereby enabling multi-decadal analysis of daytime coronal observing conditions using AERONET data.

Lyman-$\alpha$ Escape through Anisotropic Media

This paper employs Monte Carlo radiative transfer simulations to demonstrate that Lyman-$\alpha$ photons escaping through anisotropic, porous neutral gas traverse channels of substantial optical depth rather than the lowest-density paths, leading to suppressed central flux and complex spectral features that challenge the use of Ly$\alpha$ as a direct proxy for ionizing photon escape.

Mass Production of 2023 KMTNet Microlensing Planets I: Low Mass Ratio

This paper presents the first systematic search for low-mass-ratio planets ($q<2\times 10^{-4}$) in the re-reduced 2023 KMTNet microlensing data, identifying three strong planet candidates including KMT-2023-BLG-0164, whose host system was characterized via spectroscopy despite being projected on a bright foreground star.

Mass regulates the emerging timescale of young star clusters

Based on HST and JWST observations of thousands of young star clusters in four nearby galaxies, this study reveals that the timescale for clusters to emerge from their natal gas is strongly correlated with stellar mass, with massive clusters dispersing their surroundings more rapidly, a finding that challenges current simulations and has significant implications for galactic feedback and planet formation.

Meta-learning for cosmological emulation: Rapid adaptation to new lensing kernels

This paper demonstrates that Model-Agnostic Meta-Learning (MAML) enables a cosmological emulator to rapidly adapt to new redshift distributions with minimal fine-tuning data, significantly outperforming standard single-task and non-pre-trained emulators in both accuracy and the fidelity of cosmological inference constraints.

Mock Catalogs of Strongly Lensed Gravitational Waves via A Halo Model Approach with Ground-based Detectors

This paper presents the Gravitational Waves-Lensing Mock Catalog (GW-LMC), a comprehensive suite of simulated strongly lensed gravitational wave events derived from a composite halo model, which forecasts hundreds of annual detections (including doublets, quadruplets, subhalo-lensed systems, and central images) for future third-generation ground-based detector networks and provides essential statistical priors for their identification.

Modern Rate-of-Decline Relations for Novae

This paper analyzes a large sample of nova decline times to establish bidirectional power-law relationships between the $t_2$ and $t_3$ parameters, ultimately finding that $t_2$ is approximately half of $t_3$ within the derived uncertainties.

Multi-spacecraft constraints on relativistic solar energetic particle transport in the widespread 28 October 2021 event

This study utilizes multi-spacecraft observations and numerical simulations to demonstrate that the widespread 28 October 2021 relativistic solar energetic particle event was governed by a narrow injection region coupled with efficient cross-field diffusion, resulting in parallel mean free paths within the Palmer consensus range and perpendicular mean free paths of approximately 1–10% of the parallel values.

Numerical effects on the stripping of dark matter and stars in IllustrisTNG galaxy groups and clusters

This study of IllustrisTNG simulations reveals that while dark matter stripping from satellites is largely resolution-independent, stellar mass stripping is strongly resolution-dependent, with higher resolutions delaying disruption and producing more compact satellites that generate more concentrated stellar haloes, potentially explaining the overprediction of stellar halo mass at large radii.

ODIN: Confirmation and 3D Reconstruction of Six Massive Protoclusters at Cosmic Noon

The ODIN survey combines wide-field Ly$\alpha$ imaging with extensive spectroscopy to confirm and reconstruct the 3D structures of six massive protoclusters at cosmic noon ($z\approx 2.4$ and $3.1$), revealing that galaxies in these dense cores exhibit enhanced emission and early quenching, with environmental effects appearing stronger at higher redshifts.

ODIN: Spectroscopic Validation of Ly$\alpha$-Emitting Galaxy Samples with DESI

The ODIN survey successfully validated its narrow-band selection of Lyman-alpha emitting galaxies at redshifts 2.4, 3.1, and 4.5 using DESI spectroscopy, achieving confirmation rates of 92–96% while identifying active galactic nuclei and lower-redshift emission lines as primary contaminants.

Obscured Star Formation in the Dwarf Galaxy DDO 43? A Comparative UV-IR Analysis

This study utilizes GALEX UV and WISE infrared imaging to analyze star formation in the dwarf galaxy DDO 43, revealing a general correlation between UV and IR fluxes indicative of coherent star formation while identifying specific regions with elevated IR but low UV emission that suggest localized, dust-obscured star formation.

On the Influence of Pluto on Twotino Dynamics Through Their Mutual 4:3 Mean Motion Resonance

This study demonstrates that Pluto significantly influences the long-term stability of Twotinos by trapping them in a 4:3 mean motion resonance, a finding that necessitates including Pluto in simulations of the trans-Neptunian region rather than excluding it.

On the modeling and mitigation of interference fringes in polarimetric instrumentation

This paper presents an approximate yet agile modeling framework for analyzing and mitigating spectral and spatial interference fringes in polarimetric instruments, specifically focusing on isotropic materials and uniaxial crystals under the assumption of small birefringence, while validating its accuracy against rigorous methods like Berreman calculus through extensive design examples.

One-loop renormalization of the effective field theory of inflationary fluctuations from gravitational interactions

This paper demonstrates that within the Effective Field Theory of inflation, one-loop gravitational corrections to primordial power spectra can be fully renormalized using dimensional regularization, resulting in exactly conserved scalar and tensor spectra on super-horizon scales and proving that propagation speeds remain immune to radiative corrections from gravitational nonlinearities.

Optical QPOs with different periodicities in CSS and ZTF light curves of the quasar 4C 50.43

This study reveals that optical quasi-periodic oscillations (QPOs) in the quasar 4C 50.43 exhibit distinct periodicities of 1124 and 513 days in different survey light curves, a discrepancy attributed to red noise and observational factors that cautions against the uncritical interpretation of optical QPOs as definitive indicators of binary black holes in active galactic nuclei.

Optical calibration systems of the Pacific Ocean Neutrino Experiment

This paper presents the design, production, and comprehensive optical characterization of novel light-pulse driver circuits and self-monitoring calibration instruments (both directional pulsers and isotropic P-CAL modules) developed for the Pacific Ocean Neutrino Experiment, demonstrating their high-intensity, nanosecond-scale performance and near-perfect optical isotropy through simulations and experimental validation in air and water.

Optimising the global detection of solar-like oscillations. Tuning the frequency range for asteroseismic detection predictions and searches

This paper demonstrates that the commonly used frequency range of $W \simeq 2\Gamma_{\rm env}$ for predicting solar-like oscillation detectability is suboptimal, and recommends adopting a narrower range of $W \simeq 1.2\Gamma_{\rm env}$ to maximize detection probabilities and yields for asteroseismic surveys.

POLAR-II: modeling star formation history of galaxies on the 21-cm signal from Epoch of Reionization

This study utilizes the Jiutian-300 simulation and L-Galaxies 2020 model to demonstrate that incorporating realistic, time-varying star formation histories—specifically those dependent on stellar age and redshift—significantly alters the topology, timing, and thermal state of the Intergalactic Medium during the Epoch of Reionization, thereby producing distinct 21-cm global signals and power spectra compared to models assuming constant star formation rates.

POLISH'ing the Sky: Wide-Field and High-Dynamic Range Interferometric Image Reconstruction with Application to Strong Lens Discovery

This paper presents an enhanced deep learning framework, POLISH, which utilizes patch-wise training and nonlinear intensity transformations to achieve robust, high-dynamic-range, wide-field radio interferometric imaging, demonstrating its ability to significantly increase the discovery rate of strong gravitational lenses compared to traditional methods.

Parallel Version of CORSIKA Code with Cherenkov Option for SPHERE-3 Project

To overcome queue time limits on the Lomonosov-2 supercomputer that caused premature termination of extensive air shower simulations with Cherenkov light for the SPHERE-3 project, the authors developed and validated a multithreaded parallel version of the CORSIKA code.

Perturbative unitarity bounds on field-space curvature in de Sitter spacetime: purity vs scattering amplitude

This paper establishes that perturbative unitarity in de Sitter spacetime, analyzed via momentum-space entanglement and purity in two-scalar models, imposes an upper bound on field-space curvature of the order of the Hubble scale—a constraint arising from the spacetime's thermal nature that complements traditional flat-space bounds.

Possible Extragalactic Origins of Five LMC Globular Clusters: Proper Motion Deviations in Gaia DR3

Using Gaia DR3 proper motion data, this study identifies five Large Magellanic Cloud globular clusters with significant kinematic deviations from local stellar populations, suggesting they may have extragalactic origins resulting from past merger events or accretion from the north-eastern region, potentially triggering star formation in the Tarantula Nebula.

Probing Physics Beyond the Standard Model through Combined Analyses of Next-Generation Type Ia Supernova, CMB, and BAO Surveys

This paper forecasts that combining next-generation Type Ia supernova data from the Vera C. Rubin Observatory with BAO measurements from DESI and CMB data from advanced surveys will significantly improve constraints on dark energy parameters and enable a potential 2–3σ detection of the sum of neutrino masses, offering a powerful probe for physics beyond the standard cosmological model.

Quantifying the Milky Way, LMC and their interaction using all-sky kinematics of outer halo stars

By analyzing all-sky kinematics of outer halo stars out to 160 kpc using Simulation Based Inference on 32,000 rigid MW-LMC simulations, this study quantifies the Milky Way's and LMC's masses while characterizing the reflex motion induced by the LMC's recent pericentric passage, revealing that the LMC's mass is at least 20% of the Milky Way's and that neglecting this interaction biases mass estimates.

Radio Spectral Energy Distribution of Low-$z$ Metal Poor Extreme Starburst Galaxies: Novel insights on the escape of ionizing photons

This study presents new multi-frequency radio observations and modeling of low-redshift, metal-poor extreme starburst galaxies, revealing their flat high-frequency spectral indices and free-free absorption features to provide novel insights into dust content and the correlation between radio spectral properties and the escape of ionizing photons.

Recalibration of Pre-SM4 STIS Echelle Throughputs

This paper presents a straightforward scaling method, derived from updated CALSPEC stellar models, to recalibrate pre-SM4 STIS echelle throughputs for eight modes, achieving typical flux accuracy improvements of 0.5–2.4% in the FUV and NUV.

Reconstructing Gamma Ray Burst Energy Relations with Observational H(z) data in Neural Network Framework

This paper employs Artificial Neural Networks and Bayesian Neural Networks to perform a model-independent calibration of Gamma-Ray Burst luminosity relations using observational Hubble parameter data, successfully resolving the circularity problem and yielding consistent Amati relation slopes that align with previous low-redshift calibrations.

Recovering the infall mass for Milky Way satellite galaxy Sextans

Using tailored N-body simulations across various Milky Way mass models, this study reconstructs the infall mass of the Sextans dwarf spheroidal galaxy to range between $1.22$and$3.14\times10^9\rm\,M_\odot$, demonstrating that its current stellar kinematics provide robust dynamical constraints while its dark matter tidal loss is primarily driven by the host galaxy's mass.

Red-Giant Asteroseismology of Low-Mass Population III Stars

This study demonstrates that asteroseismic analysis of low-mass red giants, specifically through the comparison of seismic signatures like the $\psi$ diagnostic and $\Delta\nu$ ratios, provides a powerful method for distinguishing primordial, metal-free Population III stars from metal-enriched analogues despite surface pollution.

Rederivation of STIS Secondary Echelle Mode Traces

This paper introduces a new method using Gaussian process regression to define curved STIS secondary echelle mode traces, which improves flux throughput by approximately 4% and has been used to update reference files for nine different modes across pre- and post-Servicing Mission 4 observations.

Relativistic Corrections to the Formation Rate of Extreme Mass-Ratio Inspirals

This paper presents a relativistically self-consistent analytic framework for estimating Extreme Mass-Ratio Inspirals (EMRIs) that, by generalizing the loss-cone angular momentum and revising the plunge pericenter in Schwarzschild spacetime, reveals that relativistic effects increase predicted event rates by approximately a factor of eight compared to Newtonian treatments, thereby underscoring their critical importance for space-based gravitational-wave detectors like LISA and Taiji.

Reliable Tests of Faint-end UV Luminosity Functions in Strong Lensing Fields

This thesis utilizes strong gravitational lensing in the MACS J0416 field, combined with deep HST/JWST observations and machine learning to mitigate low-redshift interlopers, to test ultralight boson dark matter models and find no evidence for a faint-end turnover, thereby constraining the particle mass to be greater than $2.97\times10^{-22}$eV at 95% confidence.

Resolved molecular gas and star-formation in massive unquenched spirals : I. UGC 8179

This study presents the first resolved molecular gas and star-formation analysis of the massive unquenched spiral UGC 8179, revealing that while it sustains standard local star-formation processes across its extended disc, its central region exhibits suppressed specific star formation rates likely driven by bulge-induced dynamical regulation.

Scalar field dark matter and stepped dark radiation in an extended Wess-Zumino dark radiation model

This paper proposes and constrains a novel cosmological model where scalar field dark matter interacts with stepped dark radiation via pure momentum coupling, finding that while it offers marginal improvements over the original Wess-Zumino dark radiation model in alleviating the Hubble and $S_8$ tensions, it does not fully resolve these cosmological discrepancies.

Scalar shortcut to beyond-Kerr ringdown tests and their complementarity with black-hole shadow observations

This paper proposes a scalar shortcut method that uses exact scalar field quasinormal mode deviations as an accurate proxy for gravitational corrections in beyond-Kerr scenarios, demonstrating that current ringdown constraints can be comparable to or more stringent than black hole shadow observations while offering complementary tests of gravity.

Scattering from compact objects: Debye series and Regge-Debye poles

This paper investigates elastic scattering of massless scalar waves by horizonless compact objects in curved spacetime by introducing an exact Debye-series decomposition of the scattering matrix, which reveals how Regge-Debye pole spectra and branch-cut contributions govern scattering phenomena differently in neutron-star-like versus ultracompact regimes.

Searching for Black Hole Candidates in Quiescence by Using Multi-band Observations in Globular Cluster M22 (NGC6656)

This paper presents a multi-wavelength study of radio sources in the globular cluster M22, identifying VLA22 as a promising quiescent stellar-mass black hole candidate that aligns with retention models, while also highlighting other potential black hole and jet-emitting sources requiring further confirmation.

Shaken, not stirred: inefficient mixing of CM- and CI-like materials

N-body simulations demonstrate that Saturn's growth and migration are inefficient at scattering CM-like planetesimals into the Uranus-Neptune region due to gas drag and migration effects, resulting in negligible contamination of the distant CI reservoir and confirming the isolation of carbonaceous asteroid populations.

Simulation-Based Prediction of Black Hole Spectra: From $10M_\odot$to$10^8 M_\odot$

This paper extends a comprehensive post-processing method for GRMHD simulations to black holes ranging from $10M_\odot$to$10^8 M_\odot$, demonstrating that standard radiation physics can successfully reproduce observed spectral properties—including state-dependent shapes in stellar-mass systems and soft X-ray excesses in massive black holes—across the entire mass spectrum.

Spectral characteristics of fast rotating metal-poor massive stars

This paper combines advanced stellar evolution and atmosphere modeling to predict the synthetic spectral characteristics of fast-rotating, very metal-poor massive stars, revealing a transition from early-O type giants/supergiants to WO-type spectral classes as they evolve, with the goal of validating these predictions against upcoming Hubble Space Telescope observations.

Spitzer + HST parallaxes of 13 late T and Y dwarfs

This paper presents new astrometric measurements for 13 nearby cold brown dwarfs using combined Spitzer and HST data, revealing significant intrinsic scatter in their photometric properties that renders distance estimates unreliable and underscores the necessity of direct parallax measurements for accurate characterization.

Statistical consistency of sign-switching vacuum energy with cosmological observations

Using exact non-Gaussian consistency diagnostics on CMB, BAO, and supernova data, this study finds that while Gaussian metrics may overstate tensions, both the standard $\Lambda$CDM and its sign-switching extension $\Lambda_{\rm s}$CDM show excellent consistency between observations, with the latter offering modest geometric improvements at intermediate redshifts.

Stellar age determination using deep neural networks: Isochrone ages for 1.3 million stars, based on BaSTI, MIST, PARSEC, Dartmouth and SYCLIST evolutionary grids

This paper introduces NEST, a model-driven deep learning framework that rapidly estimates stellar ages for over 1.3 million stars across multiple evolutionary grids with high accuracy and a 60,000-fold speedup compared to traditional Bayesian methods, thereby enabling large-scale galactic archeology studies.

Stellar halos of bright central galaxies II: Scaling relations, colors and metallicity evolution with redshift

Using the updated FEGA25 semi-analytic model, this study reveals that stellar halos around bright central galaxies act as dynamically and chemically coupled transition regions to the intracluster light, with their scaling relations, colors, and metallicity evolution across cosmic time showing strong agreement with observed colors but suggesting a larger contribution from disrupted dwarf galaxies than currently predicted.

Stochastic modelling of cosmic-ray sources for Galactic diffuse emissions

This paper employs stochastic Monte Carlo simulations to demonstrate that the discreteness of supernova remnant cosmic-ray sources introduces significant, energy-dependent uncertainties in Galactic diffuse gamma-ray and neutrino emission predictions, particularly in time-dependent diffusion scenarios, which may help reconcile theoretical models with high-energy observations from LHAASO and future experiments.

Supernova scores for active anomaly detection

This paper presents a hybrid active anomaly detection framework that integrates a supervised supernova probability score into the PineForest algorithm to significantly enhance the discovery efficiency of supernovae and other rare transients in large-scale time-domain surveys like the Zwicky Transient Facility.

Taking a Break at Cosmic Noon: Continuum-selected Low-mass Galaxies Require Long Burst Cycles

This study utilizes JWST observations of 43 low-mass galaxies at cosmic noon to demonstrate that their star formation is governed by long-duration burst cycles with significant quiescent phases, challenging the notion that these systems should be classified solely by their instantaneous star formation rates.

Temporal Variation of the Coronal Parameter in a Jetted Tidal Disruption Event: Swift J1644+57

This paper analyzes long-term archival X-ray data of the jetted Tidal Disruption Event Swift J1644+57 to demonstrate that its soft and hard X-ray emissions originate from a single coronal source, revealing a temporal evolution where the corona rapidly expands during the initial jet-launching phase before settling into a saturated state with minor fluctuations.

Testing Screened Modified Gravity with Strongly Lensed Gravitational Waves

This paper develops a refined theoretical and statistical framework to test screened modified gravity theories using strongly lensed gravitational waves, demonstrating that future next-generation detectors can provide stringent constraints on the post-Newtonian parameter $\gamma_{\text{PN}}$ and detect deviations from General Relativity on kpc-Mpc scales by leveraging precise time delay measurements to resolve mass-sheet degeneracy.

The Formulation of Scaling Expansion in an Euler-Poisson Dark-fluid Model

This paper presents a dark fluid model described as a non-viscous, non-relativistic, rotating, and self-gravitating fluid with spherical symmetry and a polytropic equation of state, which is solved using a self-similar time-dependent ansatz to derive new solutions consistent with the Newtonian cosmological framework that can describe the transition from normal matter to dark energy on cosmological scales.

The Influence of Clouds and Deuterium-Burning on Brown Dwarf Habitable Zones

By applying modern brown dwarf evolution models that account for cloud formation and deuterium burning, this study reveals that these factors significantly extend the duration planets can remain in the habitable zone and create specific "sweet spots" near the deuterium-burning limit, correcting previous estimates based on simpler analytic approximations.

The Key to Unlocking Exoplanet Biosignatures: a UK-led IR Spectrograph for the Habitable Worlds Observatory Coronagraph

This paper proposes a UK-led near-infrared Integral Field Spectrograph to complement the US-led optical arm of the Habitable Worlds Observatory, enabling the comprehensive spectral analysis required to detect unambiguous biosignatures on habitable exoplanets.

The Response of Planetary Atmospheres to the Impact of Icy Comets III: Impact Driven Atmospheric Escape

By coupling cometary impact and planetary atmospheric models, this study demonstrates that while global circulation on tidally-locked exoplanets generally enhances hydrogen escape compared to Earth-analogue atmospheres, the location of an icy comet impact significantly modulates escape rates, with day-side impacts driving an order-of-magnitude higher loss than night-side impacts due to differences in vertical transport efficiency.

The Salamander: A case study of the magnetic field and peculiar morphology of G309.8-2.6 through radio polarimetry

This paper utilizes new ASKAP radio polarimetry data alongside archival multiwavelength observations to characterize the complex morphology and highly ordered magnetic field of the supernova remnant G309.8-2.6, revealing an extended relic pulsar wind nebula and proposing scenarios to explain its peculiar S-shaped structure.

The detection of high X-ray polarization from an accretion disc corona source and its modelling via Monte Carlo radiation transfer simulation

This paper reports a significant detection of high X-ray polarization from the neutron star system 2S 0921-630 using IXPE, revealing distinct polarization properties during eclipse versus out-of-eclipse phases and demonstrating that a Monte Carlo simulation of scattering in a thermal-radiative wind can reproduce the observed polarization degree and its weak energy dependence, though it fails to fully account for the marginal energy-dependent changes in polarization angle.

The period clustering of magnetars and X-ray dim isolated neutron stars

By applying point-likelihood analysis to the spin periods of 38 magnetars and X-ray dim isolated neutron stars, this study reveals a significant clustering near 12–15 seconds that supports a common physical mechanism, likely involving magnetic field decay or fallback disc torques, which terminates the observable phase of these neutron stars at a specific final period.

The role of mass loss in constraining quenching time in dwarf galaxies from AGB and RGB star counts

This study utilizes updated stellar models incorporating dust-driven mass loss to demonstrate that the mass lost by low-mass stars during the RGB phase is the critical factor in calibrating the AGB-to-RGB star ratio, thereby enabling the reconstruction of dwarf galaxy star formation histories with an uncertainty of approximately 1 Gyr.

The rotational and magnetic properties of Polaris from long-term spectropolarimetric monitoring

Based on five years of spectropolarimetric monitoring, this study reports the first direct measurement of Polaris's rotation period (100.29 days) and reveals a remarkably stable surface magnetic field, providing critical constraints for understanding the star's unusual evolutionary history and potential merger origin.

The statistics and structure of dissipation in subsonic and supersonic turbulence

Using high-resolution simulations, this study reveals that kinetic energy dissipation in subsonic turbulence is vorticity-dominated, localized on small scales, and lags energy injection by approximately 1.64 turnover times, whereas supersonic dissipation is density-correlated, spans multiple scales via shocks and vorticity, and lags by only 0.48 turnover times, with distinct fractal structures identified in both regimes.

Time-dependent photospheric radiative transfer in structured GRB jets: spectral evolution and polarization diagnostics

This paper presents a time-dependent photospheric radiative transfer model coupling 2D SRHD simulations with Monte Carlo photon propagation to demonstrate how jet angular structure, pair loading, and dissipation depth jointly regulate the spectral evolution and polarization signatures of gamma-ray bursts, offering testable predictions for high-energy polarimeters.

Two Low Mass-Ratio Microlensing Planets and Two Types of Central-Resonant Degeneracy

This paper reports the discovery of two low-mass-ratio microlensing planets and analyzes a "central-resonant" degeneracy in high-magnification events, categorizing it into two distinct types based on mass ratio and source radius to guide future solution identification.

Understanding the impact of binary mass transfer in the accretor's measurable parameters

This paper presents a novel analytical model demonstrating that direct mass transfer via a stream is inefficient at spinning up an accretor to critical rotation, thereby allowing the star to gain significant mass while remaining stable, with mass conservation being highest in tighter orbits or systems with faster donor rotation.

Variable magnetic field and adaptive mixing-length: reproducing Li abundances and constraining rotational evolution of solar-type stars in clusters

This study employs rotating stellar models with dynamically varying magnetic field strength and mixing-length parameters to successfully reproduce observed lithium abundances and general rotational trends in solar-type stars, though it currently overestimates the Sun's present-day rotation rate and magnetic field, highlighting the need for additional angular momentum loss mechanisms.

Weighing Hidden Companions of Compact Object Candidates via Rotational Broadening

This study utilizes rotational broadening measurements from LAMOST spectra to determine orbital inclinations and companion masses for ten compact object candidates, revealing five systems with unseen companions likely to be neutron stars or massive white dwarfs, including two potential Type Ia supernova progenitors.

Winding, Unwinding, Rewinding the Gaia Phase Spiral

This paper summarizes the outcomes of a workshop held at the Lorentz Center seven years after the discovery of the Gaia Phase Spiral, aiming to consolidate current knowledge, identify open questions regarding the Galactic disk's response to perturbations, and invite the broader community to collaborate on ongoing research projects.

Worlds Next Door. IV. Mapping the Late Stages of Giant Planet Evolution with a Precise Dynamical Mass and Luminosity for $\epsilon$ Ind Ab

This paper presents new JWST mid-infrared detections of the nearby cold gas giant $\epsilon$ Ind Ab, combining them with three decades of astrometric data to precisely determine its dynamical mass and construct its first full 4–25 $\mu$m spectral energy distribution, thereby establishing it as a benchmark system that validates evolutionary models for low-mass, old exoplanets.

XSNAP: An X-ray Supernova Analysis Pipeline with Application to the Type II Supernova 2024ggi

This paper introduces XSNAP, a new open-source Python pipeline for standardized X-ray analysis of supernovae, and applies it to multi-epoch observations of SN 2024ggi to derive a steady progenitor mass-loss rate of $(6.2\pm0.2)\times10^{-5}\,M_{\odot}\,\mathrm{yr^{-1}}$.

unxt: A Python package for unit-aware computing with JAX

The paper introduces **unxt**, a Python package built on the **quax** framework that integrates **astropy.units** with **JAX** to enable seamless, high-performance, unit-aware scientific computing.

Analytic treatment of a polaron in a nonparabolic conduction band

This paper develops and benchmarks a modified Feynman variational method alongside other generalized analytical approaches to accurately describe lattice polarons in non-parabolic conduction bands across all coupling regimes, demonstrating superior agreement with numerically exact results compared to traditional continuum-based models.

Boundary critical behavior of the Gross-Neveu-Yukawa model

This paper investigates the boundary critical behavior of the semi-infinite Gross-Neveu-Yukawa model by analyzing its phase diagram, identifying distinct universality classes under various unitary and conformal boundary conditions, and computing the associated boundary critical exponents to one-loop order.

Capillary filling of star polymer melts in nanopores

This study utilizes molecular dynamics simulations to demonstrate that the capillary filling dynamics and post-imbibition relaxation of star polymer melts are profoundly governed by their topology, where arm length and functionality dictate deviations from the Lucas-Washburn equation, induce arm orientation and disentanglement, and significantly enhance adsorption and friction effects.

Critical behavior of the thermal phase transition of U(1) lattice gauge systems

This paper employs Monte Carlo simulations of a U(1) lattice gauge system to demonstrate that the thermal phase transition of a superconductor belongs to the XY universality class, exhibiting critical exponents consistent with Bose-Einstein condensation and neutral boson transitions.

Critical point of the transition between $s_\pm$ and $s_{++}$ states of a two-band superconductor with nonmagnetic impurities

This paper demonstrates that the transition between $s_\pm$ and $s_{++}$ superconducting states in a two-band model with nonmagnetic impurities evolves from a smooth crossover at high temperatures to a first-order phase transition at low temperatures, thereby establishing a critical end point on the temperature-impurity scattering rate phase diagram that suggests the possibility of a quantum phase transition.

Diffusive flux into a stochastically gated tube

This paper extends the theoretical framework for estimating diffusive flux into stochastically gated tubes by deriving an explicit formula that remains accurate for non-narrow geometries and varying diffusivities, overcoming challenges related to complex 3D geometry and multiplicative noise.

Dynamics of viscous liquids and the Random Barrier Model

This study demonstrates that the parameter-free Random Barrier Model outperforms the von Schweidler law in fitting the inherent dynamics of a viscous ternary Lennard-Jones liquid and accurately predicting its diffusion coefficient, despite the model's unrealistic assumption of identical energy minima.

Effect of Cylindrical Confinement on the Collapse Dynamics of a Polymer

Using molecular dynamics simulations, this study reveals that cylindrical confinement induces a two-stage collapse of homopolymers from a good to a poor solvent—characterized by the formation of pearl-necklace clusters followed by their coalescence into a spherical globule—wherein the relaxation dynamics and activation energies exhibit distinct dependencies on confinement radius and temperature, despite a universal power law governing cluster growth at fixed confinement.

Effect of Pressure and Oxygen-Isotope Substitution on Density-Wave Transitions in La$_4$Ni$_3$O$_{10}$

This study utilizes muon-spin rotation and resistivity measurements to reveal that in the trilayer nickelate La$_4$Ni$_3$O$_{10}$, pressure suppresses intertwined spin- and charge-density-wave transitions at a uniform rate while oxygen-isotope substitution selectively enhances the charge-density-wave transition, highlighting a distinct coupling between charge and spin orders that differs from bilayer nickelates.

Entanglement Measure Response to Modular Flow and Chiral Topological Phases

This paper demonstrates that the real-time dynamics of entanglement measures under modular flow are governed by a unified generating function that uniquely determines chiral topological invariants, specifically the chiral central charge and Hall conductance, as validated by both free fermion models and chiral conformal field theory.

Entropic Clustering of Stickers Induces Aging in Biocondensates

This paper proposes a minimal model demonstrating that entropy maximization of spacers induces an attractive force between stickers, leading to sticker clustering and a slow, glassy relaxation process that explains the long-term aging and solidification of biomolecular condensates.

Evidence of universal spectral collapse at a marginal dynamical regime

This paper demonstrates that incoherent electronic spectra across diverse strongly correlated materials arise from self-generated dynamical disorder and collapse onto a single universal curve described by a parabolic cylinder function, revealing a marginal dynamical regime where microscopic details become irrelevant at low energies.

Heat-dissipation decomposition and free-energy generation in a non-equilibrium dot with multi-electron states

This paper experimentally demonstrates the quantitative decomposition of heat dissipation into housekeeping and excess components in a non-equilibrium nanoscale dot with multi-electron states, revealing a direct correlation between these thermal processes and free-energy generation that achieves an efficiency of 0.25 under driven conditions.

Hopfield model for patterns with internal structure

This paper analytically derives the free energy and phase diagram of a spherical Hopfield model with Gaussian-correlated pattern structures, revealing that a spin glass phase emerges at high temperatures while a glass phase containing both patterns and correlations appears at lower temperatures under low loading capacity.

Interplay of Rashba spin-orbit coupling and Coulomb interaction in topological spin-triplet excitonic condensates

This study demonstrates that the cooperative effect of Rashba spin-orbit coupling and Coulomb attraction stabilizes topological spin-triplet excitonic condensates in two-dimensional electron-hole systems, driving a transition from trivial to topological states with a quantized Chern number of $C=2$ and identifying soft spin-up triplet modes as precursors to condensation.

Interplay of local and global quantum geometry in the stability of flat-band superfluids

This paper demonstrates that the stability of flat-band superfluidity in two-dimensional systems depends critically on the specific distribution of the quantum metric within the Brillouin zone rather than just its integrated value, revealing that at least three bands are required for stable condensation and that the superfluid weight is significantly influenced by the condensate quantum metric.

Ionic-Bond-Driven Atom-Bridged Room-Temperature Cooper Pairing in Cuprates and Nickelates: a Theoretical Framework Supported by 32 Experimental Evidences

This paper proposes a theoretical framework asserting that high-temperature superconductivity in cuprates and nickelates is driven by ionic-bond-mediated electron pairing bridged by oxygen or metal atoms, a mechanism supported by 32 experimental evidences and offering a potential path toward room-temperature superconductivity.

Non-equilibrium generalized Langevin equation for multi-dimensional observables

This paper derives a non-equilibrium generalized Langevin equation for multi-dimensional observables using the Mori-Zwanzig formalism, revealing a unique instantaneous friction contribution that vanishes only for uncorrelated components, and demonstrates its application to modeling coupled protein folding kinetics in human islet amyloid polypeptide fibril formation.

Oxygen-isotope effect on density wave transitions in La$_3$Ni$_2$O$_{7}$

This study demonstrates that oxygen isotope substitution ($^{16}\text{O} \rightarrow ^{18}\text{O}$) significantly increases the charge-density wave transition temperature in La$_3$Ni$_2$O$_7$ while leaving the spin-density wave transition unaffected, indicating that lattice vibrations drive charge ordering whereas spin ordering is primarily electronic in origin.

Phase diagram and Ashkin-Teller universality in the classical square-lattice Heisenberg-compass model

Using large-scale Monte Carlo simulations, this study maps the finite-temperature phase diagram of the classical square-lattice Heisenberg-compass model, identifying six ordered phases and demonstrating that transitions between four symmetry-broken phases belong to the Ashkin-Teller universality class terminating at four-state Potts points, while transitions involving $z$-polarized phases exhibit conventional 2D Ising criticality.

Phase diagrams of S=1/2 bilayer Models of SU(2) symmetric antiferromagnets

This paper investigates the zero-temperature phase diagrams of two distinct families of $S=1/2$ bilayer antiferromagnets with SU(2) symmetry, revealing that while both exhibit first-order Néel-to-valence bond solid transitions, their topological differences—specifically the ability to exchange spin versus only energy—lead to the presence or absence of a dimer phase and distinct finite-size scaling behaviors.

Predictive first-principles simulations for co-designing next-generation energy-efficient AI systems

This Perspectives article argues that predictive, first-principles simulations spanning materials to architectures are essential for co-designing specialized hardware capable of achieving orders-of-magnitude improvements in the energy efficiency of next-generation AI systems.

Proximate Spin Liquid Ground State Arising from Competing Stripy and 120$^{\circ}$ Spin Correlations in the Triangular Quantum Antiferromagnet ErMgGaO$_4$

This study reports that the triangular quantum antiferromagnet ErMgGaO$_4$ exhibits a spin glass transition and a low-energy dynamic magnetic continuum that, when analyzed via inelastic neutron scattering and linear spin wave theory, places the material near the theoretical phase boundary between stripy and 120$^{\circ}$ ordered states, suggesting a proximate quantum spin liquid ground state.

Quantum spin ladder with ferromagnetic rungs in Bi$_2$CuO$_3$(SO$_4$)

This paper identifies Bi$_2$CuO$_3$(SO$_4$) as a rare two-leg quantum spin ladder featuring ferromagnetic rungs and exceptionally strong antiferromagnetic legs, a unique magnetic architecture confirmed through a comprehensive combination of experimental measurements and advanced theoretical simulations.

Quasi-one-dimensional soliton in a self-repulsive spin-orbit-coupled dipolar spin-half and spin-one condensates

This study investigates the formation and stability of various quasi-one-dimensional solitons in self-repulsive spin-orbit-coupled dipolar Bose-Einstein condensates, revealing that the interplay between spin-orbit coupling strength and interaction parameters dictates the emergence of distinct soliton types (such as bright-bright, dark-bright, and their modulated variants) in both pseudo spin-half and spin-one systems, all of which are demonstrated to be dynamically stable.

Reproducible nucleation and control of stable quantum vortex rings in Bose-Einstein condensates

This paper proposes and numerically validates an experimentally feasible protocol for the deterministic nucleation and manipulation of stable quantum vortex rings in Bose-Einstein condensates using a sweeping laser-sheet barrier, enabling precise control over their properties and the generation of Kelvin-wave excitations for the study of three-dimensional vortices and quantum turbulence.

Revisiting the $J_1$-$J_2$ Heisenberg Model on a Triangular Lattice: Quasi-Degenerate Ground States and Phase Competition

Using state-of-the-art matrix product state simulations, this study challenges the conventional view that the two nearly degenerate ground states of the spin-1/2 triangular-lattice $J_1$-$J_2$ Heisenberg model represent distinct topological sectors of a gapped $\mathbb{Z}_2$ spin liquid, by demonstrating that they exhibit significant differences in both static correlations and dynamical excitations.

Rigid body rotation and chiral reorientation combine in filamentous E. coli swimming in low-Re flows

This study reveals that filamentous *E. coli* induced by sub-lethal antibiotics exhibit distinct swimming behaviors in low-Reynolds number flows, where motile cells undergo a combination of rigid body rotation and chiral reorientation leading to irregular "wiggling" and wall-directed rheotaxis, while non-motile cells behave as passive rigid rods following streamlines.

Simple mathematical model for a pairing-induced motion of active and passive particles

This paper proposes and analyzes a simple mathematical model describing how active and passive particles connected by a linear spring exhibit distinct straight, circular, and slalom motions, with theoretical analysis confirming a bifurcation between straight and circular trajectories driven by the magnitude of self-propulsion.

Stability of flat-band Bose-Einstein condensation from the geometry of compact localized states

This paper reformulates the stability of flat-band Bose-Einstein condensation as a Euclidean geometry problem using compact localized states, demonstrating that triangular frameworks with nonzero area support condensation while square frameworks do not, thereby offering new design principles for stable flat-band condensates.

Symmetric $\mathrm{U(1)}$ and $\mathbb{Z}_2$ spin liquids on the pyrochlore lattice

This paper provides a complete classification of symmetric $\mathrm{U(1)}$ and $\mathbb{Z}_2$ spin liquids on the pyrochlore lattice within the projective symmetry group framework, identifying novel classes with gapless "nodal star" spinon structures that exhibit distinct low-temperature specific heat scaling compared to standard quantum spin ice.

Temporal Berry Phase and the Emergence of Bose-Glass-Analog Phase in a Clean U(1) Superfluid

This paper demonstrates that a temporal Berry phase in a clean U(1) nonlinear sigma model induces space-time anisotropic vortex interference, leading to a quasi-disordered phase with short-range spatial order and persistent temporal coherence that shares the essential correlation properties of the disordered Bose Glass phase, thereby suggesting a unified topological origin for glassy behavior in phase-fluctuation-driven superfluid transitions.

Three phases of odd robotic active matter

This paper introduces the MASBot robotic platform to experimentally demonstrate a unified phase diagram of nonreciprocal active matter, revealing continuous transitions between odd elastic, odd viscous, and chiral active gas phases while establishing a blueprint for programmable robotic swarms.

Three-stage melting of a macroscopic continuous spacetime crystal

This paper reports the first experimental observation of a macroscopic continuous spacetime crystal formed by active granular disks, revealing a unique three-stage melting process where spatial and temporal orders decay via distinct mechanisms, thereby demonstrating the decoupling of spontaneous symmetry breaking in space and time.

Tunable shear thickening in active non-Brownian suspensions

Using particle-based simulations, this study demonstrates that self-propulsion in active, non-Brownian suspensions can counteract friction-mediated shear thickening to induce a tunable "dethickening" effect, which follows a universal scaling framework based on dimensionless active stress.

Virtual walks in the Ising model: finite time scaling

This paper analyzes the non-equilibrium dynamics of the Ising model in one and two dimensions using a virtual walk approach associated with spin states and local energy, demonstrating that finite-time scaling of these walks yields critical exponents consistent with known theoretical values and reveals a distinct non-equilibrium region and time-dependent critical point.

When velocity autocorrelations mirror force autocorrelations: Exact noise-cancellation in interacting Brownian systems

This paper provides a rigorous theoretical justification for the noise-cancellation algorithm in interacting Brownian systems by proving that cross-correlations vanish in thermal equilibrium—rendering the method exact—while demonstrating that finite cross-correlations in nonequilibrium systems serve as a distinct fingerprint of non-equilibrium physics requiring specific corrections.

Chern character and Fermi point

This paper reformulates the Chern character in topological K-theory using Fermi points of Fredholm operators to generalize spectral flow and provide elementary proofs for the evenness of the edge index and the bulk-edge correspondence in four-dimensional time-reversal symmetric topological insulators (class AI).

Cooling of electrons via superconducting tunnel junctions and their arrays exhibiting nodal lines

This paper theoretically proposes a method for cooling electrons by utilizing superconducting tunnel junctions with $\pi$ phase differences and ferroelectric layers, which feature nodal lines that create a divergent density of states and high entropy to facilitate heat removal from an electron bath.

Effects of correlated hopping on thermoelectric response of a quantum dot strongly coupled to ferromagnetic leads

Using the numerical renormalization group method, this study demonstrates that correlated hopping induces asymmetric spin-dependent transport and significantly alters the thermoelectric efficiency of a quantum dot coupled to ferromagnetic leads by modifying Kondo resonance and conductance peak asymmetry.

Fokker-Planck approach to thermal fluctuations in antiferromagnetic systems

This paper develops a Fokker-Planck framework based on the Landau-Lifshitz-Gilbert equation with Langevin fields to model the thermal fluctuations and spin-wave dynamics of two-dimensional antiferromagnetic systems with uniaxial anisotropy, ultimately applying the theory to describe resistance fluctuations in antiferromagnetic semiconductors.

Gate-tunable anisotropic Josephson diode effect in topological Dirac semimetal Cd$_3$As$_2$ nanowires

This study demonstrates a gate-tunable and highly anisotropic Josephson diode effect in topological Dirac semimetal Cd$_3$As$_2$ nanowire junctions, utilizing a phenomenological model and temperature-dependent measurements to disentangle bulk and surface state contributions and reveal the coexistence of multiple transport channels as a probe for hidden topological superconducting states.

Geometrical properties of strained and twisted moiré heterostructures

This review comprehensively introduces the geometrical formalism of strained moiré superlattices derived from linear elasticity theory, explores their application in various twisted 2D materials to predict special patterns, and summarizes recent experimental techniques for realizing and engineering these strain-induced geometries.

Helical orbitals in electrical uni-directional molecular motors

This paper proposes a mechanism for electrical uni-directional molecular motors driven by electron current through helical orbitals, introduces a formal definition of helicality to link electronic angular momentum with rotational direction, and predicts that approximate sub-lattice symmetry causes the motor's sense of rotation to remain independent of the current direction.

High-efficiency Pt$_{75}$Au$_{25}$-based spintronic terahertz emitters

This paper demonstrates that optimizing the composition and layer thickness of spintronic terahertz emitters using a Pt$_{75}$Au$_{25}$ alloy significantly enhances THz output power by up to 30% compared to conventional Pt-based devices, establishing it as a high-performance platform driven by a giant spin Hall effect.

Higher-harmonic acoustic driving of quantum-dot optical transitions beyond Rabi-frequency resonance

This paper proposes a higher-harmonic acoustic driving scheme that enables high-fidelity control of quantum-dot optical transitions at accessible acoustic frequencies (e.g., 42 GHz) despite large energy splittings (0.341 THz), thereby overcoming previous sub-THz limitations and paving the way for advanced on-chip quantum technologies involving multi-phonon processes and entanglement.

How to formulate the $\mathbb{Z}_8$ topological invariant of Majorana fermion on the lattice

This paper proposes and numerically verifies a lattice formulation of the $\mathbb{Z}_8$-valued Arf-Brown-Kervaire invariant for Majorana fermions on two-dimensional non-oriented manifolds, demonstrating that it can be extracted from Pfaffians of the Wilson Dirac operator and agrees with continuum theory results.

Impact of Exchange-Correlation Functionals on Predictions of Phonon Hydrodynamics: A Study of Fluorides, Chlorides, and Hydrides

This study utilizes density functional theory with various exchange-correlation functionals to investigate the thermal and mechanical properties of alkali halides and hydrides, revealing that the choice of functional significantly influences predictions of lattice thermal conductivity and the conditions for observing phonon hydrodynamics, while also identifying new materials exhibiting this phenomenon.

Intrinsic magnetization of the superconducting condensate in Fe(Te,Se)

This paper provides experimental evidence for intrinsic, spin-polarized superconductivity in mesoscopic Fe(Te,Se) rings, characterized by a current-dependent magnetic field and dual flux quantization that is explained by a model combining Rashba coupling with anisotropic out-of-plane interactions.

Local Hall Conductivity in Disordered Topological Insulators

This paper derives an expression for local Hall conductivity in disordered systems to demonstrate that non-magnetic disorder and the fragmentation of disordered patches can expand the parameter regimes for both Chern insulating states and topological Anderson insulators, thereby motivating new local experimental techniques to visualize these effects.

Low-Noise Quantum Dots in Ultra-Shallow Ge/SiGe Heterostructures for Prototyping Hybrid Semiconducting-Superconducting Devices

This paper demonstrates that ultra-shallow Ge/SiGe heterostructures with thin SiGe capping layers, fabricated using low-temperature oxide processes to preserve superconducting compatibility, achieve low charge-noise levels comparable to deeper structures, making them a promising platform for prototyping hybrid semiconducting-superconducting devices.

Magnetic field tuning of modulated magnetic orders in CrOCl at the two-dimensional limit

This study utilizes magneto-Raman scattering to reveal how magnetic field tuning and layer thickness modulate the complex magnetic phase diagram and spin-lattice coupling in two-dimensional chromium oxychloride (CrOCl), identifying commensurate magnetic orders and significant magneto-strictive effects down to the single-layer limit.

Magnetic properties of an individual Magnetospirillum gryphiswaldense cell

This study characterizes the magnetic properties of an individual *Magnetospirillum gryphiswaldense* bacterium by combining ultrasensitive torque magnetometry, transmission electron microscopy, and micromagnetic simulations to reveal the magnetic configurations, remanent moment, and effective anisotropy of its internal magnetosome chain.

Magneto-optical Response of 5-SL MnBi$_2$Te$_4$ in Spin-Flip States

This study utilizes first-principles calculations and a coupled-Dirac-cone model to demonstrate that the topological nature and magneto-optical response of five-septuple-layer MnBi$_2$Te$_4$ films are tunable and can switch between topological and trivial states depending on the relative spin orientations of the surface layers.

Microscopic origin of $p$-wave magnetism

This paper provides a microscopic explanation for the unconventional out-of-plane spin polarization in $p$-wave antialtermagnets by linking it to a site-compensated spin density, a mechanism verified through model derivation and *ab initio* calculations on CeNiAsO, while offering a general framework to distinguish between different magnetic orders and guide the design of materials with large spin splitting.

Nonlinear Hall Effect in Metal-Organic Frameworks

This paper proposes metal-organic frameworks (MOFs) as a versatile, designable platform for realizing the nonlinear Hall effect by mapping their electronic structures to a universal low-energy model where symmetry-breaking mechanisms generate Berry-curvature hotspots near the Fermi level.

Nonlocal Andreev transport through a quantum dot in a magnetic field: Interplay between Kondo, Zeeman, and Cooper-pair correlations

This paper investigates the interplay between Kondo correlations, Zeeman splitting, and Cooper-pair effects in nonlocal Andreev transport through a quantum dot coupled to normal and superconducting leads, demonstrating that crossed Andreev reflection is enhanced in the crossover region between Kondo and superconducting regimes and remains robust against magnetic fields within a specific Bogoliubov-rotation angle range.

Orbital-Zeeman cross correlation in $p$- and $d$-wave altermagnets

This paper investigates the orbital-Zeeman cross correlation in altermagnets, revealing that while $p$-wave order parameters have limited or magnitude-reducing effects on Rashba metals and topological insulator surfaces, $d$-wave order parameters induce a sign change in Rashba metals and preserve the chemical potential jump magnitude in topological insulators while reducing the overall term.

Pfaffian-based topological invariants for one dimensional semiconductor-superconductor heterostructures

This paper reviews and clarifies the validity of Pfaffian-based $\mathbb{Z}_2$ topological invariants in one-dimensional semiconductor-superconductor nanowires by demonstrating their equivalence across momentum-space, real-space, and disordered systems, while establishing a direct physical interpretation linking the invariant to ground-state fermion parity.

Phonon mode splitting and phonon anomaly in multiband electron systems

This paper demonstrates that coupling chiral fermions to local, dispersionless phonons induces a topological splitting of the phonon spectrum into flat and linearly dispersing bands with hedgehog-like Berry curvature, revealing a phonon parity anomaly that enables phonon currents to directly probe electronic chirality and topological structures.

Structural and electronic signatures of strain-tunable marginally twisted bilayer graphene

Using scanning tunneling microscopy and tight-binding calculations, this study reveals how strain induces distinct domain wall transitions and modulates electronic states in marginally twisted bilayer graphene, establishing strain as a key control parameter for its structural and electronic properties.

Thermal Hall conductivity from semiclassical spin dynamics simulations: implementation and applications to chiral ferromagnets and Kitaev magnets

This paper presents a semiclassical spin dynamics framework for computing thermal Hall conductivity via linear response theory, demonstrating its effectiveness in capturing non-linear magnon interaction effects in chiral ferromagnets and Kitaev magnets beyond simple non-interacting approximations.

Ultralight High-Entropy Nanowire Scaffolds for Extreme-Temperature Functionality

This study introduces ultralight, three-dimensional "bird's-nest" scaffolds made from FeCoNiCrCu high-entropy alloy nanowires that achieve metal-like thermal and magnetic functionality at less than 1% of bulk density by co-engineering configurational entropy with structural porosity.

Unconventional Altermagnetism in Quasicrystals: A Hyperspatial Projective Construction

This paper extends the concept of altermagnetism to quasicrystals by using a hyperspatial projection framework to demonstrate that interaction-induced Néel order on decorated Ammann-Beenker and Penrose lattices gives rise to unconventional $g$-wave and $h$-wave altermagnetic phases with momentum-dependent spin splitting compatible with noncrystallographic rotational symmetries.

3D Mapping of Intragranular Residual Strain and Microstructure in Recrystallized Iron Using Dark-Field X-ray Microscopy

This study utilizes dark-field X-ray microscopy to provide the first direct experimental evidence of heterogeneous intragranular residual elastic strains (on the order of $10^{-4}$) within fully recrystallized commercial-purity iron, highlighting their potential influence on grain boundary migration and the need to incorporate them into future grain growth models.

A new approach for measurement of Cr4+ concentration in Cr4+:YAG transparent materials: some conceptual difficulties and possible solutions

This paper analyzes the limitations of using the Smakula-Dexter formula for accurately determining Cr4+ concentrations in Cr4+:YAG materials due to uncertainties in oscillator strengths and spectral deconvolution, and proposes a new approach based on survey absorption spectra to overcome these conceptual difficulties.

A systematic study of single molecule metallocenes with 4d and 3d transition metal atoms

Using first-principles density functional theory, this study systematically investigates the electronic and magnetic anisotropy of 3d and 4d transition metal metallocenes, revealing that magnetic anisotropy depends strongly on orbital ordering rather than the number of d-electrons, with Mo and Rh exhibiting the highest values of approximately 20 K and up to 60 K in cationic states.

AI-driven Inverse Design of Complex Oxide Thin Films for Semiconductor Devices

The paper introduces IDEAL, an AI-driven inverse design platform that integrates generative models and machine learning with atomic layer deposition to predict and experimentally validate optimal composition windows for complex Hf-Zr-O oxide thin films, thereby bridging the gap between computational discovery and non-equilibrium semiconductor synthesis.

Ab initio simulation of the first-order proton-ordering transition in water ice

By combining machine learning interatomic potentials with specialized loop updates to overcome high energy barriers and ensure accurate sampling, this study successfully simulates the first-order proton-ordering transition in water ice at 83 K, a result that aligns with experimental values after accounting for nuclear quantum effects.

Adaptive hydrogels with spatiotemporal stiffening using pH-modulating enzymes

This study presents a glucose oxidase-embedded hydrogel that utilizes enzymatic pH waves to drive autonomous, energy-dependent spatiotemporal stiffening, revealing that mechanical transduction lags behind chemical propagation and establishing design principles for adaptive soft materials.

Aligning van der Waals heterostructures using electron backscatter diffraction

This paper establishes Electron Backscatter Diffraction (EBSD) as a high-precision, versatile tool for determining crystallographic orientations across various van der Waals materials, enabling the precise engineering of twisted heterostructures with controlled twist angles for advanced twistronics and twist-optics applications.

Application of dual-tree complex wavelet transform for spectra background reduction

This paper introduces a universal Dual-Tree Complex Wavelet Transform (DTCWT) method for removing spectral backgrounds in experimental data, demonstrating its superior signal preservation and reduced bias compared to traditional fitting or Fourier-based techniques through applications on X-ray powder diffraction and photoluminescence spectra.

Beyond-quasiparticle transport with vertex correction: self-consistent ladder formalism for electron-phonon interactions

This paper presents a self-consistent many-body framework that unifies first-principles calculations with vertex corrections and beyond-quasiparticle effects to accurately predict phonon-limited electronic transport and optical properties in materials with strong electron-phonon interactions, achieving quantitative agreement with experimental data for Si, ZnO, and SrVO3.

Bulk magnetic properties of distorted square lattice compounds M'-LnTaO4 (Ln = Tb, Dy, Ho, Er)

This study investigates the bulk magnetic properties of distorted square lattice compounds M'-LnTaO4 (Ln = Tb, Dy, Ho, Er), utilizing powder neutron diffraction and specific heat measurements to confirm the crystal structure, identify long-range antiferromagnetic order in TbTaO4 below 2.1 K, observe short-range ordering in DyTaO4, and establish a Kramers doublet ground state in ErTaO4.

Chemical heterogeneity at conducting ferroelectric domain walls

By combining transport measurements with atom probe tomography, this study reveals that significant chemical heterogeneity and spatially varying compositions along ferroelectric domain walls in BiFeO3 provide a unifying explanation for their diverse electronic behaviors, demonstrating that multiple conduction mechanisms can coexist within individual walls.

Competing Hydrogenation Pathways to Metastable CaH$_6$ Revealed by Machine-Learning-Potential Molecular Dynamics

This study utilizes machine-learning potential molecular dynamics to reveal that the synthesis of metastable high-$T_c$ superhydride CaH$_6$ from CaH$_2$ proceeds via a kinetically accessible, martensitic-like topotactic pathway driven by crystallographic compatibility, effectively competing with the thermodynamically stable but reconstructive formation of CaH$_{5.75}$.

Comprehensive structural and optical analysis of differently oriented Yb-implanted $\beta$-Ga$_2$O$_3$

This study investigates the structural damage and optical properties of Yb-implanted $\beta$-Ga$_2$O$_3$ across three crystal orientations, revealing that while (010)-oriented samples exhibit the least defects and compressive stress, the other orientations display higher defect levels that surprisingly enhance Yb$^{3+}$ luminescence.

DFT calculations of magnetocrystalline anisotropy energy with fixed spin moment

This paper demonstrates that the fully relativistic fixed spin moment (FR-FSM) method reconciles discrepancies in magnetocrystalline anisotropy energy (MAE) calculations arising from different exchange-correlation potentials and provides a framework for estimating maximum MAE values to guide the design of new-generation permanent magnets.

Dielectric, magnetic and lattice dynamics properties of double perovskite (Ca0.5Mn1.5)MnWO6

This study refutes previous claims that (Ca₀.₅Mn₁.₅)MnWO₆ is a hybrid multiferroic by demonstrating through comprehensive dielectric, magnetic, and structural analyses that observed anomalies are attributable to spin-phonon coupling and chemical impurities rather than intrinsic ferroelectric or antiferroelectric ordering, thereby reclassifying the material as a paraelectric antiferromagnet.

Direct Laser Writing of Ferromagnetic Nickel Utilizing the Principle of Sensitized Triplet-Triplet Annihilation Upconversion

This paper presents a novel photoresist utilizing sensitized triplet-triplet annihilation upconversion combined with in-situ photochemical deoxygenation and Ni2+ photoreduction to enable the direct laser writing of ferromagnetic nickel microstructures under ambient conditions.

Efficient method for calculation of low-temperature phase boundaries

This paper introduces an efficient framework combining the Clausius-Clapeyron equation with the quasi-harmonic approximation to calculate low-temperature phase boundaries with minimal computational cost, demonstrating its accuracy and versatility by constructing the silica phase diagram using both density functional theory and machine-learned interatomic potentials.

Enhancing Reconstruction Capability of Wavelet Transform Amorphous Radial Distribution Function via Machine Learning Assisted Parameter Tuning

This study introduces the enhanced WT-RDF+ framework, which leverages machine learning-assisted parameter tuning to overcome amplitude accuracy limitations in reconstructing Radial Distribution Functions for amorphous Ge-Se and Ag-Ge-Se systems, thereby outperforming standard ML benchmarks even with limited training data.

Exact downfolding and its perturbative approximation

This paper presents a rigorous formulation of the downfolding procedure to derive exact effective models for arbitrary target spaces by integrating out high-energy degrees of freedom, establishes conditions for perturbative truncation, formally derives the constrained random phase approximation (cRPA) with identified corrections, and validates the approach using material examples like fcc Nickel and SrCuO$_2$.

Field-Programmable Topological Torons in Chiral Nematic Liquid Crystals

This paper demonstrates the experimental creation, steering, and parking of individual topological torons in chiral nematic liquid crystals using tailored alternating-current electric fields, enabling deterministic submicrometre control over their trajectories for applications in reconfigurable patterning, micromanipulation, and optical memory.

From Word2Vec to Transformers: Text-Derived Composition Embeddings for Filtering Combinatorial Electrocatalysts

This paper demonstrates that a lightweight, label-free screening strategy using Word2Vec-derived composition embeddings often outperforms transformer-based methods in filtering vast combinatorial electrocatalyst libraries by prioritizing candidates based on their similarity to text-derived property concepts like conductivity and dielectricity.

Ground-State Structure Search of Defective High-Entropy Alloys Using Machine-Learning Potentials and Monte Carlo Sampling

This paper introduces PAIPAI, an efficient Monte Carlo framework coupled with machine-learning potentials and a dual-worker architecture, to successfully predict the ground-state atomic configurations of defective high-entropy alloys containing interstitials, as validated by density functional theory across multiple case studies.

Impact of spin--orbit coupling on orbital diamagnetism in a narrow-gap semiconductor $\mathrm{Pb}_{1-x}\mathrm{Sn}_x\mathrm{Te}$

This paper demonstrates that spin-orbit coupling significantly enhances orbital diamagnetism in the narrow-gap semiconductor $\mathrm{Pb}_{1-x}\mathrm{Sn}_x\mathrm{Te}$ by amplifying Dirac-type interband contributions relative to Zeeman terms, particularly in strong magnetic fields.

Introduction to Spectroscopy of Cr4+:YAG Transparent Ceramics

This paper investigates the temperature-dependent spectroscopic properties of Cr4+:YAG transparent ceramics from 5K to 300K, revealing sharp low-temperature absorption and emission features, including a 28 cm-1 doublet splitting, to elucidate the origin of the emission and the crystal field environment of the Cr4+ ions.

Ionic-instability induced color tuning in lead-based, mixed-halide perovskites

This study reveals that intermediate photoluminescence energies in mixed-halide lead perovskites can be kinetically stabilized during photosegregation through specific pulsed laser excitation parameters, offering a new mechanism for color tuning and explaining previously unexplained spectral phenomena.

Machine-learning assistant DFT study of half-metallic full-Heusler alloy N2CaNa: structural, electronic, mechanical, and thermodynamics properties

This study utilizes density functional theory to investigate the structural, electronic, mechanical, and thermodynamic properties of the half-metallic N2CaNa full-Heusler alloy, revealing its mechanical stability, ductility, and potential for applications in spintronics and structural engineering.

Material-Property-Field-based Deep Neural Network in Hopfield Framework

This paper introduces mPFDNN, an analytically tractable deep neural network framework that integrates Material Property Fields with Hopfield network dynamics to overcome the interpretability limitations of traditional DNNs by rigorously respecting physical symmetries and enabling principled structure-property mapping across diverse material systems.

Materials Acceleration Platform for Electrochemistry (MAP-E): a Platform for Autonomous Electrochemistry

This paper introduces MAP-E, an autonomous, high-throughput robotic platform that automates parallel electrochemical experiments to generate reproducible, large-scale corrosion datasets and accelerate materials discovery through uncertainty-driven sampling strategies.

Nanoscale imaging of spin textures with locally varying altermagnetic response in $\alpha$-Fe$_2$O$_3$

This study utilizes nano-spectroscopic X-ray magnetic circular dichroism to demonstrate that $\alpha$-Fe$_2$O$_3$ exhibits locally varying altermagnetic responses, including on-and-off switching across the Morin transition and finite signals within nanoscale domain walls and meron textures, thereby establishing a pathway for harnessing complex spin textures in earth-abundant altermagnets.

On the Mathematical Foundation of a Decoupled Directional Distortional Hardening Model for Metal Plasticity in the Framework of Rational Thermodynamics

This paper proposes a modified, mathematically consistent decoupled directional distortional hardening model for metal plasticity that resolves prior inconsistencies and limitations by introducing a new yield function term capable of capturing both yield surface flattening and sharpening, even in the absence of kinematic hardening.

On the origin of diverse interlayer charge redistribution in transition-metal dichalcogenides

This study elucidates the underlying mechanisms behind diverse interlayer charge redistributions in transition-metal dichalcogenides by revealing how the competition and coexistence of different types of interlayer quasi-chemical-bonding interactions—specifically involving occupied-occupied, occupied-empty, and half-filled or multi-level orbital couplings—dictate electron accumulation or depletion patterns across varying d-electron fillings and crystal phases.

Optically driven thermodynamic transition from free- to locked-epitaxy

This paper demonstrates that external light irradiation can deterministically drive a thermodynamic transition in the Fe4N/mica system from van der Waals-dominated free-epitaxy to chemically locked-epitaxy by using photo-excited carriers to enhance interfacial chemical affinity and surpass the critical locking threshold.

Optimal parallelisation strategies for flat histogram Monte Carlo sampling

This paper benchmarks various parallelization strategies for flat-histogram Monte Carlo sampling and demonstrates that non-uniform energy domain decomposition offers the most significant performance improvements, providing concrete recommendations for optimizing simulations of complex alloy systems.

Orbital Altermagnetism

This paper introduces the concept of "orbital altermagnetism," a symmetry-protected magnetic order characterized by staggered orbital moments and momentum-dependent band splittings that arises from loop currents in specific lattice structures and is predicted to exist in materials like CuBr$_2$ and VS$_2$, offering new opportunities for orbital-based spintronics.

Physics-informed neural operator for predictive parametric phase-field modelling

This paper introduces PF-PINO, a physics-informed neural operator framework that embeds phase-field governing equation residuals into the training loss to significantly improve the accuracy, generalization, and long-term stability of predictive parametric phase-field modelling compared to conventional methods.

Pressure-Induced Structural and Magnetic Evolution in Layered Antiferromagnet YbMn$_2$Sb$_2$

This study reveals that applying pressure to the layered antiferromagnet YbMn$_2$Sb$_2$ induces a structural phase transition near 3.5 GPa and a semiconductor-to-metal transition above 5 GPa, driven by band gap closure and the stabilization of unconventional magnetic states characterized by short-range spin pairing and incommensurate antiparallel correlations.

Pressure-Stabilized MnSb$_2$ with Complex Incommensurate Magnetic Order

This study reports the high-pressure synthesis and characterization of metastable marcasite-type MnSb$_2$, revealing a complex, temperature-dependent incommensurate magnetic ground state with a spin-density-wave-like order and potential altermagnetic properties.

Receptogenesis in a Vascularized Robotic Embodiment

This paper presents a vascularized robotic system capable of *in situ* photopolymerization to dynamically grow functional sensors from internal fluid reserves, thereby enabling real-time physical adaptation and closed-loop control in complex environments.

Resolving Transient Electron-Phonon Coupling with Time-Resolved Spontaneous Raman Spectroscopy

This paper introduces a time-resolved spontaneous Raman spectroscopy technique with sub-wavenumber spectral and few-hundred-picosecond temporal resolution to successfully resolve subtle transient electron-phonon coupling parameters and carrier recombination dynamics in lightly boron-doped silicon.

Spectral Indicators of Piezomagnetically Induced Symmetry Breaking in Altermagnets

This paper establishes that X-ray magnetic linear dichroism (XMLD) and magnetic circular dichroism (XMCD) in altermagnets serve as element-specific probes of piezomagnetic effects, where the ferroic ordering of higher-rank multipoles like spinful magnetic octupoles generates characteristic field-odd signals and strain-induced magnetic moments that reveal hidden magnetoelastic order beyond conventional ferromagnetism.

Synthetic design of force-responsive hydrogels with ring-forming catch bonds

This paper presents a minimal synthetic framework for force-responsive hydrogels based on reversible ring-forming polymers, which, as demonstrated by molecular dynamics simulations, exhibit catch bond behavior where bond lifetimes increase under mechanical load, enabling the design of mechanically adaptive materials with tunable durability and responsiveness.

Ultra-Fast Machine-Learned Interatomic Potential for MoS2 Enabling Non-Equilibrium Molecular-Dynamics Simulation of Epitaxial Growth

This paper presents an ultra-fast machine-learned interatomic potential for multilayer MoS2, developed using the UF3 framework, which accurately reproduces DFT-level structural, elastic, and defect properties while enabling large-scale non-equilibrium molecular dynamics simulations of epitaxial growth that reveal experimentally consistent van der Waals gaps and triangular domains.

Uncovering the properties of homo-epitaxial GaN devices through cross-sectional infrared nanoscopy

This paper demonstrates that combining mid-infrared and terahertz scattering-type scanning near-field optical microscopy (s-SNOM) enables high-resolution, non-destructive characterization of homo-epitaxial GaN p-i-n diodes by successfully disentangling carrier and lattice properties and detecting sub-surface defects with superior sensitivity compared to traditional metrologies like micro-Raman and KPFM.

Weyl-Transition-Driven Giant Reversible Orbital Hall Conductivity

This paper demonstrates that monolayer PtBi2 exhibits a giant, reversible orbital Hall conductivity driven by a strain-induced Weyl transition that modulates the imbalance of orbital Berry curvature through the evolution of tilted Weyl points from type-II to type-I and back.

"Should I Give Up Now?" Investigating LLM Pitfalls in Software Engineering

This study investigates the pitfalls of integrating large language models into software engineering workflows, revealing that persistent inaccuracies and cognitive overload often lead developers to abandon AI tools, though strategic prompt refinement can significantly mitigate the risk of such abandonment.

"Who wants to be nagged by AI?": Investigating the Effects of Agreeableness on Older Adults' Perception of LLM-Based Voice Assistants' Explanations

This study of 70 older adults reveals that while high-agreeableness in LLM-based voice assistants generally enhances trust and likability, the preference for warmth over clarity shifts depending on the context (routine vs. emergency) and the user's own personality, highlighting the need for adaptive, personalized AI explanations.

3D UAV Trajectory Estimation and Classification from Internet Videos via Language Model

This paper presents a novel, annotation-free framework that leverages language models and vision-language reasoning to autonomously extract 3D UAV trajectories and classifications from Internet-scale videos, demonstrating that zero-shot transfer performance on anti-UAV tasks improves consistently with increased data volume without requiring target-domain training.

A 26-Gram Butterfly-Inspired Robot Achieving Autonomous Tailless Flight

This paper introduces \textit{AirPulse}, a 26-gram butterfly-inspired robot that achieves the first autonomous, closed-loop tailless flight at this scale by replicating low-frequency, high-amplitude biomechanical traits through a hierarchical control architecture featuring Stroke Timing Asymmetry Rhythm (STAR).

A Decade of News Forum Interactions: Threaded Conversations, Signed Votes, and Topical Tags

This paper introduces a large-scale, privacy-preserving dataset of ten years of user interactions on the Austrian newspaper DerStandard, comprising over 75 million comments and 400 million votes with anonymized identifiers and pre-computed vector embeddings to facilitate research on online discourse dynamics in the German language.

A Decentralized Frontier AI Architecture Based on Personal Instances, Synthetic Data, and Collective Context Synchronization

This paper proposes the H3LIX Decentralized Frontier Model Architecture, a distributed AI framework that enables privacy-preserving collective learning and sustainable scaling by aggregating locally generated synthetic reasoning signals into a shared Collective Context Field rather than relying on centralized model retraining.

A Generalized Voronoi Graph based Coverage Control Approach for Non-Convex Environment

This paper proposes a two-phase coverage control method based on the Generalized Voronoi Graph that achieves efficient multi-robot coverage in non-convex environments by first partitioning the region and balancing robot allocation according to sub-region quality, followed by a collaborative coverage phase with a new controller.

A Hybrid Residue Floating Numerical Architecture with Formal Error Bounds for High Throughput FPGA Computation

This paper introduces the Hybrid Residue Floating Numerical Architecture (HRFNA), a formally verified numerical system combining carry-free residue arithmetic with lightweight exponent scaling that achieves significantly higher throughput, reduced resource usage, and improved energy efficiency on FPGAs compared to IEEE 754 standards while maintaining rigorous, bounded numerical error.

A PTAS for Weighted Triangle-free 2-Matching

This paper presents a polynomial-time approximation scheme (PTAS) for the Weighted Triangle-Free 2-Matching problem, utilizing a local-search algorithm to achieve a $(1-\varepsilon)$-approximation ratio for any constant $\varepsilon > 0$, thereby significantly improving upon the previous best-known $2/3$-approximation.

A Regularized Ensemble Kalman Filter for Stochastic Phase Field Models of Brittle Fracture

This paper proposes a regularized ensemble Kalman filter framework that integrates sensor displacement data into stochastic phase-field models of brittle fracture to infer the evolving displacement and phase-field states, thereby correcting model predictions while ensuring physical consistency through a novel regularization step.

A Simple Constructive Bound on Circuit Size Change Under Truth Table Perturbation

This paper explicitly establishes that the optimum circuit size changes by at most $O(n)$ under truth table perturbations, extending the bound to general Hamming distances and verifying its tightness at $n=4$ through exhaustive SAT-based analysis.

A Survey on Wi-Fi Sensing Generalizability: Taxonomy, Techniques, Datasets, and Future Research Prospects

This survey provides a comprehensive review of over 200 papers on Wi-Fi sensing generalizability, offering a structured taxonomy of techniques to address domain shifts, summarizing key datasets, and outlining future research directions and community resources.

A Tale of 1001 LoC: Potential Runtime Error-Guided Specification Synthesis for Verifying Large-Scale Programs

This paper introduces Preguss, a modular framework that combines static analysis with LLM-aided synthesis to automatically generate and refine interprocedural specifications, enabling highly automated verification of large-scale programs (over 1,000 lines of code) while significantly reducing human effort.

A Voronoi Cell Formulation for Principled Token Pruning in Late-Interaction Retrieval Models

This paper proposes a principled token pruning framework for late-interaction retrieval models that leverages Voronoi cell estimation in hyperspace geometry to reduce index storage overhead while maintaining retrieval performance and enhancing interpretability.

A comprehensive study of time-of-flight non-line-of-sight imaging

This paper presents a comprehensive study of Time-of-Flight non-line-of-sight imaging methods by unifying their theoretical formulations and hardware implementations to establish a common framework for analysis and demonstrate that, under equal constraints, existing techniques share similar performance limitations despite method-specific differences.

A saccade-inspired approach to image classification using visiontransformer attention maps

This paper proposes a saccade-inspired image classification method that leverages DINO's Vision Transformer attention maps to selectively focus processing on task-relevant regions, achieving performance comparable to or better than full-image analysis while offering a biologically plausible approach to efficient visual processing.

ARSGaussian: 3D Gaussian Splatting with LiDAR for Aerial Remote Sensing Novel View Synthesis

This paper introduces ARSGaussian, a novel view synthesis method for aerial remote sensing that integrates LiDAR constraints, distortion-aware coordinate transformations, and geometric consistency losses to mitigate floaters and overgrowth while achieving high-precision geo-alignment, supported by the newly released AIR-LONGYAN dataset.

AVGGT: Rethinking Global Attention for Accelerating VGGT

This paper introduces AVGGT, a training-free acceleration framework that leverages an analysis of global attention's distinct roles in VGGT and $\pi^3$ to implement a two-step optimization strategy, achieving up to 10$\times$ inference speedup on long sequences while maintaining or improving accuracy in dense multi-view 3D reconstruction tasks.

Accelerating High-Order Finite Element Simulations at Extreme Scale with FP64 Tensor Cores

This paper presents the first direct programming of FP64 tensor cores on NVIDIA GPUs to accelerate high-order finite element simulations within the MFEM library, achieving up to 2× performance and 83% energy efficiency gains while demonstrating near-perfect weak scaling across nearly 10,000 GPUs on the Alps exascale system.

Actegories, Copowers, and Higher-Order Message Passing Semantics

This paper establishes an equivalence between right actegories with hom-objects and right-enriched categories with copowers over non-closed, non-symmetric monoidal bases, a generalization motivated by the need to support higher-order process passing in the concurrent language CaMPL.

Active Prompt Learning with Vision-Language Model Priors

This paper proposes a budget-efficient active prompt learning framework for vision-language models that utilizes class-guided clustering and adaptive class-wise thresholding to achieve higher accuracy with fewer labeled samples compared to existing baselines.

Adaptive Multi-Objective Tiered Storage Configuration for KV Cache in LLM Service

This paper introduces Kareto, an adaptive multi-objective optimizer that efficiently navigates the complex configuration space of tiered KV cache storage to dynamically balance cost, throughput, and latency, significantly outperforming static strategies in LLM inference services.

Adaptive SINDy: Residual Force System Identification Based UAV Disturbance Rejection

This paper proposes an Adaptive SINDy framework that integrates data-driven Sparse Identification of Non-Linear Dynamics with Recursive Least Squares adaptive control to effectively identify residual forces and reject wind disturbances, demonstrating superior trajectory tracking performance compared to baseline PID and INDI controllers on a lightweight Crazyflie drone in turbulent environments.

Age-Related Differences in the Perception of Eye-Gaze from a Social Robot

This study investigates how age-related differences in sensitivity to deictic gaze affect social perception during human-robot interaction, aiming to inform the design of adaptive non-verbal cues for social robots assisting older adults.

Agentic AI as a Network Control-Plane Intelligence Layer for Federated Learning over 6G

This paper proposes an Agentic AI framework that serves as a control-plane intelligence layer for 6G networks, utilizing specialized agents to dynamically manage federated learning tasks by integrating network conditions with learning objectives to optimize client selection, resource allocation, and scheduling.

AgenticCyOps: Securing Multi-Agentic AI Integration in Enterprise Cyber Operations

This paper introduces AgenticCyOps, a security framework for enterprise multi-agent AI systems that mitigates emerging attack surfaces by formalizing tool orchestration and memory management as primary trust boundaries and applying five defensive principles aligned with global compliance standards to significantly reduce exploitable vulnerabilities in SOC workflows.

Algorithmic Collusion at Test Time: A Meta-game Design and Evaluation

This paper introduces a meta-game framework to evaluate the emergence of algorithmic collusion under test-time constraints by modeling agents with pretrained policies and adaptation rules, revealing how rational choices and co-adaptation influence cooperative or competitive outcomes in repeated pricing games across various algorithmic strategies.

Almost-Optimal Upper and Lower Bounds for Clustering in Low Dimensional Euclidean Spaces

This paper improves the running time of $(1+\varepsilon)$-approximation algorithms for $k$-median and $k$-means clustering in low-dimensional Euclidean spaces to $2^{\tilde{O}(1/\varepsilon)^{d-1}} \cdot n \cdot \text{polylog}(n)$and establishes an almost matching lower bound under the Gap Exponential Time Hypothesis, demonstrating that this dependence on$1/\varepsilon$and dimension$d$ is essentially optimal.

An Analysis of Modern Web Security Vulnerabilities Inside WebAssembly Applications

This paper demonstrates that binary vulnerabilities in WebAssembly modules, such as buffer overflows and use-after-free errors, can compromise web application security by enabling attacks like SQL injections and XS-Leaks, while also offering best practices to mitigate these risks.

An Empirical Study of Interaction Smells in Multi-Turn Human-LLM Collaborative Code Generation

This paper introduces the concept of "Interaction Smells" in multi-turn human-LLM code generation, establishes a taxonomy based on real-world data, analyzes their distribution across leading models, and proposes the Invariant-aware Constraint Evolution (InCE) framework to effectively mitigate these issues and improve task success rates.

AnalogToBi: Device-Level Analog Circuit Topology Generation via Bipartite Graph and Grammar Guided Decoding

AnalogToBi is a novel framework for automatic device-level analog circuit topology generation that combines bipartite graph representations, grammar-guided decoding, and data augmentation to achieve high validity and novelty while ensuring electrical correctness without human intervention.

Artificial Intelligence (AI) Maturity in Small and Medium-Sized Enterprises: A Framework of Internalized and Ecosystem-Embedded Capabilities

This study proposes a novel, context-sensitive AI maturity framework specifically designed for small and medium-sized enterprises (SMEs) that reconceptualizes maturity as a multidimensional, non-linear, and ecosystem-embedded capability comprising eight dimensions, five levels, and four development pathways to better reflect the unique resource constraints and organizational realities of SMEs.

Asset-Centric Metric-Semantic Maps of Indoor Environments

This paper presents an asset-centric metric-semantic mapping approach that combines detailed object meshes with natural language priors to create accurate, LLM-compatible indoor environment representations, achieving a superior balance between object-level detail and global scene context compared to existing methods.

Audio-Visual World Models: Towards Multisensory Imagination in Sight and Sound

This paper introduces the first formal framework for Audio-Visual World Models (AVWM), presenting the AVW-4k dataset and the AV-CDiT model to enable high-fidelity, synchronized simulation of binaural audio and visual dynamics that significantly enhances agent performance in continuous navigation tasks.

Automated Coral Spawn Monitoring for Reef Restoration: The Coral Spawn and Larvae Imaging Camera System (CSLICS)

This paper introduces the Coral Spawn and Larvae Imaging Camera System (CSLICS), an automated, low-cost computer vision solution that significantly reduces labor-intensive manual counting while accurately monitoring coral spawn and larvae to enhance reef restoration efforts.

Avoiding Big Integers: Parallel Multimodular Algebraic Verification of Arithmetic Circuits

This paper introduces a hybrid algebraic verification technique implemented in TalisMan2.0 that utilizes parallel multimodular reasoning to efficiently verify arithmetic circuits with large operands while avoiding the computational overhead of big integer arithmetic.

BOPIM: Bayesian Optimization for influence maximization on temporal networks

This paper introduces BOPIM, a Bayesian Optimization framework for influence maximization on temporal networks that utilizes novel kernel functions and a greedy acquisition strategy to achieve superior performance and significant speed improvements over existing methods while uniquely quantifying uncertainty in optimal seed sets.

Beware of the Classical Benchmark Instances for the Traveling Salesman Problem with Time Windows

This paper demonstrates that a simple exact method can solve nearly all classical benchmark instances of the Traveling Salesman Problem with Time Windows in under ten seconds, revealing that these datasets are no longer representative for evaluating algorithmic performance or training machine learning models due to their exploitable structure.

Beyond Short-Horizon: VQ-Memory for Robust Long-Horizon Manipulation in Non-Markovian Simulation Benchmarks

This paper introduces RuleSafe, a new long-horizon articulated manipulation benchmark featuring non-Markovian safe-unlocking tasks, and proposes VQ-Memory, a vector-quantized temporal representation that significantly enhances the planning, generalization, and efficiency of Vision-Language-Action models in complex robotic simulations.

BinaryAttention: One-Bit QK-Attention for Vision and Diffusion Transformers

This paper introduces BinaryAttention, a theoretically grounded method that replaces floating-point dot products with 1-bit sign-based operations and learnable biases to achieve over 2x speedup over FlashAttention2 while matching or exceeding full-precision accuracy in vision and diffusion transformers.

Bootstrap Dynamic-Aware 3D Visual Representation for Scalable Robot Learning

The paper introduces AFRO, a self-supervised framework that learns dynamics-aware 3D visual representations by modeling state-action-state transitions via a generative diffusion process, thereby significantly improving robotic manipulation performance across diverse simulated and real-world tasks without requiring explicit action or reconstruction supervision.

BrainSTR: Spatio-Temporal Contrastive Learning for Interpretable Dynamic Brain Network Modeling

BrainSTR is a spatio-temporal contrastive learning framework that enhances the interpretability of dynamic brain network modeling for neuropsychiatric diagnosis by adaptively partitioning brain states, identifying critical phases, and extracting sparse, disease-specific connectivity patterns to construct a discriminative semantic space validated across ASD, BD, and MDD datasets.

CIGPose: Causal Intervention Graph Neural Network for Whole-Body Pose Estimation

CIGPose introduces a Causal Intervention Graph Neural Network framework that enhances whole-body pose estimation robustness by using a Structural Causal Model to identify and replace context-confounded keypoint representations with invariant embeddings, thereby achieving state-of-the-art performance on COCO-WholeBody without relying on extra training data.

CLIOPATRA: Extracting Private Information from LLM Insights

The paper introduces CLIOPATRA, a novel attack demonstrating that current heuristic privacy protections in LLM insight systems like Anthropic's Clio are insufficient, as a realistic adversary can successfully extract sensitive user medical history by injecting malicious chats, even with minimal prior knowledge of the target.

CORAL: Scalable Multi-Task Robot Learning via LoRA Experts

CORAL is a scalable, embodiment-agnostic framework that mitigates multi-task interference and catastrophic forgetting in Vision-Language-Action models by freezing a shared backbone and dynamically routing language instructions to task-specific, lightweight LoRA experts with zero inference overhead.

Can AI Agents Generate Microservices? How Far are We?

This paper evaluates the capability of AI agents to generate functional microservices, finding that while they can produce maintainable code with high API contract adherence—particularly in clean-state scenarios—current limitations in consistency and the need for human oversight prevent fully autonomous generation.

Can ChatGPT Generate Realistic Synthetic System Requirement Specifications? Results of a Case Study

This case study demonstrates that while ChatGPT can generate realistic synthetic system requirement specifications across multiple industries using iterative prompt engineering, the resulting artifacts still contain significant flaws that necessitate thorough expert evaluation rather than relying solely on LLM-based quality assessments.

Case Study: Performance Analysis of a Virtualized XRootD Frontend in Large-Scale WAN Transfers

This paper presents a case study demonstrating that a virtualized XRootD frontend architecture, utilizing a heterogeneous VM cluster with BBR congestion control and TCP extensions, successfully sustained an aggregate throughput of 51.3 Gb/s and peaked at 41.5 Gb/s to Fermilab under high-intensity WAN transfer conditions.

Caterpillar-Inspired Spring-Based Compressive Continuum Robot for Bristle-based Exploration

This paper presents a compact, spring-based, tendon-driven continuum robot inspired by caterpillar locomotion and equipped with artificial bristle sensors, which integrates with commercial robotic arms to enable effective, compliant exploration and surface perception in confined spaces with a mean position error of 4.32 mm.

Chain of Event-Centric Causal Thought for Physically Plausible Video Generation

This paper proposes a framework for physically plausible video generation that models phenomena as causally connected event sequences by integrating physics-driven chain-of-thought reasoning with transition-aware cross-modal prompting to ensure dynamic continuity and physical consistency.

Characterization, Analytical Planning, and Hybrid Force Control for the Inspire RH56DFX Hand

This paper transforms the commercially available Inspire RH56DFX hand into a reliable research tool by introducing hardware characterization, a sim2real validated MuJoCo model for grasp planning, and a hybrid closed-loop speed-force controller, achieving significant performance improvements in peg-in-hole insertion and diverse object grasping tasks.

ChatNeuroSim: An LLM Agent Framework for Automated Compute-in-Memory Accelerator Deployment and Optimization

This paper introduces ChatNeuroSim, an LLM-based agent framework that automates the entire Compute-in-Memory (CIM) accelerator deployment and optimization workflow—including simulation, parameter management, and design space pruning—to significantly reduce the time and effort required for design space exploration.

ChimeraLoRA: Multi-Head LoRA-Guided Synthetic Datasets

ChimeraLoRA addresses data scarcity in fine-grained tasks by synthesizing diverse and detail-rich images through a hybrid architecture that combines a class-shared LoRA for semantic priors with per-image LoRAs for specific characteristics, guided by semantic boosting and a Dirichlet-based mixture strategy to improve downstream classification accuracy.

Clarifying the Compass: A Reflexive Narrative on Entry Barriers into HCI and Aging Research

This paper reflects on the barriers to interdisciplinary collaboration in HCI and aging research by analyzing the disconnect between technological design and older adults' needs, while sharing the authors' personal journey of developing empathy through volunteering in a senior living community.

Class Model Generation from Requirements using Large Language Models

This paper evaluates the capability of state-of-the-art Large Language Models to automatically generate UML class diagrams from natural language requirements, demonstrating their effectiveness and reliability through a comprehensive dual-validation framework that combines LLM-as-a-Judge assessments with human expert evaluations.

CoRe-GS: Coarse-to-Refined Gaussian Splatting with Semantic Object Focus

CoRe-GS is a coarse-to-refine Gaussian Splatting framework that accelerates 3D reconstruction for robotic applications by selectively optimizing only task-relevant points of interest, thereby significantly reducing training time and mitigating artifacts while maintaining high-quality semantic segmentation.

CogBlender: Towards Continuous Cognitive Intervention in Text-to-Image Generation

CogBlender is a novel framework that enables continuous, multi-dimensional control over the cognitive properties of text-to-image generation by mapping cognitive space to visual semantics and dynamically steering the flow-matching process through interpolated velocity fields guided by cognitive anchors.

Collaborative Problem Solving in Mixed Reality: A Study on Visual Graph Analysis

This study involving 72 participants across three languages demonstrates that while nominal groups serve as a crucial benchmark for evaluating collaborative virtual environments, 3D graph representations in mixed reality do not inherently yield better collaborative problem-solving outcomes than individual performance.

Comparative Analysis of Patch Attack on VLM-Based Autonomous Driving Architectures

This paper introduces a systematic framework for evaluating black-box patch attacks on three vision-language model-based autonomous driving architectures in CARLA simulation, revealing severe, sustained vulnerabilities and distinct failure patterns that highlight the inadequacy of current designs against physical adversarial threats.

Compartmentalization-Aware Automated Program Repair

This paper presents a specialized, LLM-based Automated Program Repair framework designed to automatically secure cross-compartment interfaces in compartmentalized software by integrating a fuzzer, compartment-aware analysis techniques, and a validation component to address the limitations of existing general-purpose repair tools.

Component-Aware Sketch-to-Image Generation Using Self-Attention Encoding and Coordinate-Preserving Fusion

This paper proposes a novel component-aware, self-refining framework that combines a Self-Attention-based Autoencoder, a Coordinate-Preserving Gated Fusion module, and a Spatially Adaptive Refinement Revisor to generate high-fidelity, semantically accurate photorealistic images from freehand sketches, significantly outperforming existing GAN and diffusion models across diverse facial and non-facial datasets.

Computer Vision-Based Vehicle Allotment System using Perspective Mapping

This paper proposes a cost-effective, computer vision-based smart parking system that utilizes YOLOv8 for vehicle detection and inverse perspective mapping to merge multi-camera views into a simulated 3D environment for guiding users to vacant spots.

Computing $L_\infty$ Hausdorff Distances Under Translations: The Interplay of Dimensionality, Symmetry and Discreteness

This paper employs fine-grained complexity to analyze the computational hardness of minimizing the $L_\infty$ Hausdorff distance under translation, revealing intricate dependencies on dimensionality, the directed versus undirected nature of the distance, and the choice between continuous and discrete translation spaces, including asymmetric time complexities and conditional lower bounds that distinguish these variants.

ConfCtrl: Enabling Precise Camera Control in Video Diffusion via Confidence-Aware Interpolation

ConfCtrl is a confidence-aware video interpolation framework that enables precise camera control in video diffusion for novel view synthesis by combining confidence-weighted point cloud projections with a Kalman-inspired predict-update mechanism to balance pose guidance and geometric consistency while reconstructing unseen regions.

Connectivity Maintenance and Recovery for Multi-Robot Motion Planning

This paper proposes a real-time MPC-CLF-CBF motion planner based on Bézier curves that enables multi-robot fleets to maintain connectivity, navigate obstacle-rich environments without deadlocks, and recover from connection losses, as validated by simulations and experiments with eight Crazyflie quadrotors.

Context-Nav: Context-Driven Exploration and Viewpoint-Aware 3D Spatial Reasoning for Instance Navigation

The paper presents Context-Nav, a training-free framework for text-goal instance navigation that combines caption-driven frontier ranking for global exploration with viewpoint-aware 3D spatial verification to accurately disambiguate target objects in cluttered environments, achieving state-of-the-art performance on InstanceNav and CoIN-Bench.

CovertComBench: A First Domain-Specific Testbed for LLMs in Wireless Covert Communication

This paper introduces CovertComBench, a specialized benchmark for evaluating Large Language Models in wireless covert communication, revealing that while current models excel at conceptual understanding and code generation, they significantly struggle with the rigorous mathematical derivations required for security-constrained optimization.

Cutting the Cord: System Architecture for Low-Cost, GPU-Accelerated Bimanual Mobile Manipulation

This paper presents a low-cost, untethered bimanual mobile manipulator built on the open-source XLeRobot platform with integrated NVIDIA Jetson Orin compute, featuring an optimized mechanical design and a specialized power topology to enable autonomous navigation and vision-based manipulation for under $1300.

DCAU-Net: Differential Cross Attention and Channel-Spatial Feature Fusion for Medical Image Segmentation

This paper proposes DCAU-Net, a novel medical image segmentation framework that combines Differential Cross Attention to efficiently model long-range dependencies while reducing computational complexity, and a Channel-Spatial Feature Fusion strategy to adaptively integrate semantic and spatial details, thereby achieving enhanced segmentation accuracy and robustness.

DISPLAY: Directable Human-Object Interaction Video Generation via Sparse Motion Guidance and Multi-Task Auxiliary

The paper introduces DISPLAY, a framework for generating controllable and physically consistent human-object interaction videos by utilizing sparse motion guidance (wrist coordinates and object bounding boxes), an object-stressed attention mechanism, and a multi-task auxiliary training strategy to overcome limitations in flexibility, generalization, and data scarcity.

DOCFORGE-BENCH: A Comprehensive 0-shot Benchmark for Document Forgery Detection and Analysis

DOCFORGE-BENCH introduces the first unified zero-shot benchmark for document forgery detection, revealing that current methods suffer from severe calibration failures due to the extreme rarity of tampered pixels in documents, which renders standard fixed thresholds ineffective and highlights threshold adaptation as the critical missing step for practical deployment.

DRIFT: Dual-Representation Inter-Fusion Transformer for Automated Driving Perception with 4D Radar Point Clouds

This paper introduces DRIFT, a dual-path Transformer model that effectively fuses fine-grained local and coarse-grained global features from sparse 4D radar point clouds to achieve state-of-the-art performance in automated driving perception tasks like object detection and free road estimation.

DeZent: Decentralized z-Anonymity with Privacy-Preserving Coordination

This paper introduces deZent, a decentralized implementation of z-anonymity that utilizes stochastic counting structures and secure sums to coordinate privacy-preserving data anonymization across sensor networks, achieving performance comparable to centralized approaches while significantly reducing communication overhead and minimizing trust in a central entity.

Deblurring structural edges in variable thickness topology optimization via density-gradient-informed projection

This paper introduces a density-gradient-informed (DGI) projection method combined with a robust penalization strategy to effectively eliminate low-thickness regions and deblur structural edges in variable thickness topology optimization, achieving sharp solid-void transitions with negligible impact on structural compliance.

Decoder-Free Distillation for Quantized Image Restoration

This paper introduces Quantization-aware Distilled Restoration (QDR), a novel framework that employs Decoder-Free Distillation and Learnable Magnitude Reweighting to overcome bottlenecks in quantization-aware training, enabling high-performance, edge-deployed image restoration models that achieve near-FP32 accuracy and significant speedups.

Design Guidance Towards Addressing Over-Reliance on AI in Sensemaking

This paper proposes preliminary design principles for GenAI-augmented Group Awareness Tools that utilize implicit visualizations to trigger cognitive conflict and autonomous elaboration, thereby mitigating over-reliance on AI and fostering independent sensemaking in collaborative work and learning.

Designing Culturally Aligned AI Systems For Social Good in Non-Western Contexts

This paper analyzes eight real-world AI deployments across seven non-Western countries to identify six key contextual factors and three influencing dimensions, ultimately proposing 12 guidelines for designing culturally aligned, equitable, and safe AI systems through deep collaboration between developers and domain experts.

DiffWind: Physics-Informed Differentiable Modeling of Wind-Driven Object Dynamics

The paper presents DiffWind, a physics-informed differentiable framework that unifies wind-object interaction modeling, video-based reconstruction, and forward simulation by combining 3D Gaussian Splatting, the Material Point Method, and Lattice Boltzmann constraints to accurately recover and simulate wind-driven object dynamics from video observations.

Diffusion-Based Authentication of Copy Detection Patterns: A Multimodal Framework with Printer Signature Conditioning

This paper proposes a novel diffusion-based framework that enhances Copy Detection Pattern authentication by integrating printer signatures and ControlNet to effectively distinguish genuine prints from high-quality counterfeits, outperforming traditional methods in generalization and accuracy.

Dishonesty Tendencies in Testing Scenarios Among Students with Virtual Reality and Computer-Mediated Technology

This study involving 22 volunteers found that the frequency of cheating during simulated online exams was identical between Virtual Reality and traditional laptop-based testing environments.

Distributed Convolutional Neural Networks for Object Recognition

This paper proposes a lightweight distributed convolutional neural network (DisCNN) trained with a novel loss function that maps positive samples to a compact high-dimensional space while pushing negative samples to the origin, thereby disentangling positive-class features to achieve robust object recognition and detection even in complex backgrounds and for unseen classes.

Dynamic Multimodal Expression Generation for LLM-Driven Pedagogical Agents: From User Experience Perspective

This paper proposes a large language model-driven method for generating dynamic, semantically aligned speech and gestures for pedagogical agents in virtual reality, demonstrating through user experience experiments that such multimodal expressions significantly enhance learning effectiveness, engagement, and social presence while reducing fatigue and boredom.

Dynamic Precision Math Engine for Linear Algebra and Trigonometry Acceleration on Xtensa LX6 Microcontrollers

This paper presents a Dynamic Precision Math Engine for ESP32 microcontrollers that integrates Q16.16 fixed-point arithmetic, a CORDIC trigonometric module, and a cache-aware matrix kernel to achieve significant speedups in linear algebra and trigonometry through a runtime-switchable architecture that balances integer efficiency with floating-point precision.

ENIGMA-360: An Ego-Exo Dataset for Human Behavior Understanding in Industrial Scenarios

This paper introduces ENIGMA-360, a publicly released, temporally synchronized ego-exo dataset containing 360 annotated procedural videos from real industrial scenarios to advance human behavior understanding and establish baselines for tasks like action segmentation and interaction detection.

EasyText: Controllable Diffusion Transformer for Multilingual Text Rendering

This paper introduces EasyText, a controllable Diffusion Transformer framework that leverages character positioning encoding and position interpolation to achieve high-quality, precise multilingual text rendering, supported by a newly constructed large-scale synthetic dataset for pretraining and fine-tuning.

EgoMI: Learning Active Vision and Whole-Body Manipulation from Egocentric Human Demonstrations

EgoMI is a framework that bridges the human-robot embodiment gap in imitation learning by capturing synchronized end-effector and active head trajectories from egocentric human demonstrations and utilizing a memory-augmented policy to enable robust whole-body manipulation on semi-humanoid robots.

Electoral Systems Simulator: An Open Framework for Comparing Electoral Mechanisms Across Voter Distribution Scenarios

This paper introduces \texttt{electoral\_sim}, an open-source Python framework that simulates and compares various electoral systems across diverse voter preference distributions using Euclidean distance to the geometric median as a primary performance metric, including a novel theoretical benchmark based on a Boltzmann softmax kernel.

EmbC-Test: How to Speed Up Embedded Software Testing Using LLMs and RAG

This paper introduces EmbC-Test, a Retrieval-Augmented Generation (RAG) pipeline that leverages project-specific artifacts to ground large language models, enabling the automated generation of syntactically correct and runtime-valid embedded C tests that reduce testing time by up to 66% while producing 270 tests per hour.

EmoSURA: Towards Accurate Evaluation of Detailed and Long-Context Emotional Speech Captions

This paper introduces EmoSURA, a novel evaluation framework that improves the assessment of long-form emotional speech captions by decomposing them into atomic perceptual units for audio-grounded verification, addressing the limitations of traditional metrics and LLM judges while providing the standardized SURABench resource.

Enabling Multi-Client Authorization in Dynamic SSE

The paper introduces MASSE, a dynamic multi-client Searchable Symmetric Encryption (SSE) scheme that extends the OXT framework to enable fine-grained, attribute-based access control and efficient updates without re-encryption, while maintaining forward and backward privacy and demonstrating practical scalability.

Ensuring Data Freshness in Multi-Rate Task Chains Scheduling

This paper proposes a task-based scheduling framework that ensures end-to-end data freshness in safety-critical multi-rate systems by introducing a Consensus Offset Search algorithm to align task releases with data lifespan constraints, thereby eliminating the artificial latency of Logical Execution Time and the inefficiency of redundant oversampling while preserving Global EDF schedulability.

Entangling Like Mycorrhizae: Mixing Realities Through Touch in "FungiSync"

The paper presents *FungiSync*, a multi-person mixed reality experience that translates the symbiotic interdependence of mycorrhizal networks into an embodied ritual where participants' individual digital perceptual worlds entangle through physical touch, fostering a "fungal epistemic" perspective that critiques accelerated individualism.

Epistemic Closure: Autonomous Mechanism Completion for Physically Consistent Simulation

This paper introduces a Neuro-Symbolic Generative Agent that overcomes the "Implicit Context" problem in scientific discovery by autonomously validating and completing physical mechanisms through dimensionless scaling analysis, thereby preventing physical hallucinations and ensuring thermodynamically consistent simulations.

Evaluating Large Language Models for Multilingual Vulnerability Detection at Dual Granularities

This paper presents a comprehensive empirical study evaluating state-of-the-art pre-trained and large language models for multilingual vulnerability detection across seven programming languages at both function and line levels, revealing that instruction-tuned GPT-4o significantly outperforms other models, particularly in identifying high-severity and unique multilingual vulnerabilities.

Evaluating the Practical Effectiveness of LLM-Driven Index Tuning with Microsoft Database Tuning Advisor

This paper evaluates the practical effectiveness of LLM-driven index tuning against Microsoft's Database Tuning Advisor (DTA) using industrial and real-world workloads, finding that while LLMs can identify superior configurations and capture human-intuitive insights, their substantial performance variance and high validation costs currently limit their direct adoption in production as a standalone replacement for DTA.

EventVGGT: Exploring Cross-Modal Distillation for Consistent Event-based Depth Estimation

EventVGGT is a novel framework that addresses the scarcity of depth annotations and temporal inconsistency in event-based monocular depth estimation by treating event streams as coherent video sequences and distilling spatio-temporal and multi-view geometric priors from the Visual Geometry Grounded Transformer (VGGT) through a tri-level distillation strategy, achieving state-of-the-art performance and robust zero-shot generalization.

Evidential Perfusion Physics-Informed Neural Networks with Residual Uncertainty Quantification

This paper introduces Evidential Perfusion Physics-Informed Neural Networks (EPPINN), a novel framework that integrates evidential deep learning with physics-informed modeling to quantify both aleatoric and epistemic uncertainties in CT perfusion imaging, thereby achieving superior accuracy and reliability in acute ischemic stroke assessment compared to existing deterministic methods.

Evoking User Memory: Personalizing LLM via Recollection-Familiarity Adaptive Retrieval

This paper introduces RF-Mem, a novel memory retrieval framework that mimics human dual-process cognition by adaptively switching between fast familiarity-based recognition and iterative recollection-based reconstruction to achieve scalable and effective personalization in large language models.

Excess demand in public transportation systems: The case of Pittsburgh's Port Authority

This paper proposes a framework using Poisson regression with censored data filtering to accurately estimate excess demand in public transportation systems, addressing the common issue of underestimation caused by unrecorded passengers left behind on full buses, and validates the approach using simulated data and real-world data from Pittsburgh's Port Authority.

Experience Report on the Adaptable Integration of Requirements Engineering Courses into Curricula for Professionals

This paper reports on the authors' experience developing three professional software engineering curricula and proposes a systematic, content-mapping-based approach with guiding principles for effectively integrating Requirements Engineering courses into these dynamic and modular programs.

Exploiting the Final Component of Generator Architectures for AI-Generated Image Detection

This paper proposes a novel AI-generated image detection method that exploits common final architectural components across diverse generators to "contaminate" real images for training, achieving 98.83% average accuracy on unseen generators by leveraging a taxonomy of 21 models and a DINOv3 backbone.

Exploring Modality-Aware Fusion and Decoupled Temporal Propagation for Multi-Modal Object Tracking

The paper introduces MDTrack, a novel multimodal object tracking framework that achieves state-of-the-art performance by employing a Mixture of Experts for adaptive modality-aware fusion and utilizing decoupled State Space Models with cross-attention mechanisms for independent yet synergistic temporal propagation.

Exploring the Design of GenAI-Based Systems to Support Socially Shared Metacognition

This paper proposes preliminary design principles for GenAI-augmented Group Awareness Tools that leverage established mechanisms to foster autonomous socially shared metacognition in collaborative settings while mitigating the risk of over-reliance on AI-generated instructions.

Expressive Power of Property Graph Constraint Languages

This paper presents the first systematic study of the expressive power of the PG-Keys language by establishing a unifying framework to compare it with Graph Functional Dependencies (GFD) and Graph Generating Dependencies (GGD), ultimately revealing a strict hierarchy of expressiveness that clarifies PG-Keys' capabilities within the context of the upcoming GQL standard.

Extension of ACETONE C code generator for multi-core architectures

This paper proposes extending the ACETONE C code generator, originally limited to sequential code, to support multi-core architectures by formally defining a processor assignment problem and outlining a future implementation of parallel code generation, scheduling heuristics, and synchronization mechanisms.

External entropy supply for IoT devices employing a RISC-V Trusted Execution Environment

This paper proposes and validates an open-source RISC-V-based Trusted Execution Environment that acts as an external entropy service, enabling constrained IoT devices to securely obtain high-quality random numbers for cryptographic key generation by leveraging initial trust and potentially expanding with additional sensor-based entropy sources.

FAME: Force-Adaptive RL for Expanding the Manipulation Envelope of a Full-Scale Humanoid

The paper introduces FAME, a force-adaptive reinforcement learning framework that enables a full-scale humanoid to robustly maintain balance during bimanual manipulation by conditioning its policy on a learned latent context of joint configurations and estimated interaction forces, thereby significantly expanding its manipulation envelope without requiring wrist force sensors.

Fair and Square: Replacing One Real Multiplication with a Single Square and One Complex Multiplication with Three Squares When Performing Matrix Multiplication and Convolutions

This paper proposes a method to significantly reduce hardware resource requirements for matrix multiplications and convolutions by asymptotically replacing real and complex multiplications with fewer squaring operations, which are more efficient to implement in circuits.

Fast and Optimal Differentially Private Frequent-Substring Mining

This paper presents a new $\varepsilon$-differentially private algorithm for frequent substring mining that achieves near-optimal error guarantees while drastically reducing space and time complexity from $O(n^2\ell^4)$ to near-linear bounds through refined candidate generation and search space pruning.

FetalAgents: A Multi-Agent System for Fetal Ultrasound Image and Video Analysis

FetalAgents is a novel multi-agent system that dynamically orchestrates specialized vision experts to deliver robust, end-to-end fetal ultrasound analysis and structured clinical reporting across multiple tasks, outperforming existing specialized models and multimodal large language models.

Fine-grained Motion Retrieval via Joint-Angle Motion Images and Token-Patch Late Interaction

This paper proposes an interpretable text-motion retrieval framework that represents 3D human motion as joint-angle pseudo-images processed by Vision Transformers and aligns them with text via a token-wise late interaction mechanism, thereby overcoming the limitations of global-embedding methods by capturing fine-grained correspondences and improving retrieval accuracy.

First Steps towards Categorical Algebraic Artificial Chemistry

This paper constructs a functor to define dynamics for an algebraic model of interacting components, generalizing the AlChemy artificial life model and exploring how category theory can formally connect algebraic structures with dynamical systems in artificial chemistry.

Flash-KMeans: Fast and Memory-Efficient Exact K-Means

This paper introduces Flash-KMeans, an IO-aware and contention-free GPU implementation that eliminates memory bottlenecks in the assignment stage and resolves atomic write contention in the update stage through novel kernel-level innovations, achieving up to 17.9$\times$ speedup over existing baselines and enabling $k$-means as a high-performance online primitive.

Floating-Point Usage on GitHub: A Large-Scale Study of Statically Typed Languages

This paper presents the first large-scale empirical study of floating-point arithmetic usage in statically typed languages across millions of GitHub repositories, revealing that while existing benchmarks are partially representative, they do not fully capture real-world code patterns, and releasing a dataset of 10 million extracted functions to guide future reasoning techniques.

Fly, Track, Land: Infrastructure-less Magnetic Localization for Heterogeneous UAV-UGV Teaming

This paper presents an infrastructure-less magnetic localization system that enables a lightweight UAV to autonomously track and land with centimeter-level precision on a mobile quadruped robot by fusing real-time magneto-inductive sensing with inertial and optical-flow data, thereby enhancing heterogeneous robot collaboration in unknown environments without external anchors.

ForgeDreamer: Industrial Text-to-3D Generation with Multi-Expert LoRA and Cross-View Hypergraph

ForgeDreamer is a novel text-to-3D generation framework designed for industrial applications that overcomes domain adaptation and geometric reasoning limitations by integrating a Multi-Expert LoRA Ensemble for interference-free cross-category generalization and a Cross-View Hypergraph approach for capturing high-order structural dependencies to ensure manufacturing-level precision.

FormalRTL: Verified RTL Synthesis at Scale

FormalRTL is a novel end-to-end multi-agent framework that leverages software reference models as formal specifications to enable scalable, verified, and reliable register-transfer level (RTL) code generation for complex industrial hardware designs.

FrameDiT: Diffusion Transformer with Frame-Level Matrix Attention for Efficient Video Generation

The paper proposes FrameDiT, a novel video generation architecture that introduces Matrix Attention to efficiently model global spatio-temporal dynamics by processing frames as matrices, thereby achieving state-of-the-art video quality and temporal coherence while maintaining computational efficiency comparable to local factorized attention.

From Ideal to Real: Stable Video Object Removal under Imperfect Conditions

The paper introduces Stable Video Object Removal (SVOR), a robust framework that achieves state-of-the-art, flicker-free video object removal under real-world imperfections by employing a Mask Union strategy for stable erasure, a Denoising-Aware Segmentation head for precise localization, and a Curriculum Two-Stage training approach to handle shadows, abrupt motion, and defective masks.

From Perception to Cognition: How Latency Affects Interaction Fluency and Social Presence in VR Conferencing

This paper investigates how end-to-end latency impacts interaction fluency and social presence in VR conferencing compared to traditional video conferencing through subjective experiments, aiming to clarify the underlying perceptual and cognitive mechanisms to guide system optimization.

From Verification to Amplification: Auditing Reverse Image Search as Algorithmic Gatekeeping in Visual Misinformation Fact-checking

This study audits Google's reverse image search and finds that it functions as an ineffective gatekeeper against visual misinformation, often prioritizing irrelevant content and repeated falsehoods over debunking information, particularly during the initial emergence of visual falsehoods.

GIIM: Graph-based Learning of Inter- and Intra-view Dependencies for Multi-view Medical Image Diagnosis

The paper proposes GIIM, a novel graph-based framework that enhances multi-view medical image diagnosis by simultaneously modeling intra-view relationships and inter-view dynamics while effectively handling missing data to improve predictive accuracy and robustness.

GSStream: 3D Gaussian Splatting based Volumetric Scene Streaming System

This paper introduces GSStream, a novel volumetric scene streaming system for 3D Gaussian Splatting that leverages collaborative viewport prediction and deep reinforcement learning-based bitrate adaptation to overcome bandwidth challenges and deliver high-quality, real-time immersive experiences.

Game-Theoretic Modeling of Stealthy Intrusion Defense against MDP-Based Attackers

This paper proposes a game-theoretic framework for modeling stealthy Advanced Persistent Threat (APT) defense on attack graphs, where a defender strategically deploys sensors to secure critical assets against an MDP-based attacker operating under asymmetric temporal dynamics and varying levels of informational knowledge.

Gap-ETH-Tight Algorithms for Hyperbolic TSP and Steiner Tree

This paper presents Gap-ETH-tight randomized approximation schemes for the Traveling Salesperson Problem and Steiner Tree in $d$-dimensional hyperbolic space with running time $2^{O(1/\varepsilon^{d-1})}n^{1+o(1)}$, achieved through a novel hybrid hyperbolic quadtree decomposition, non-uniform portal placement, and weighted crossing analysis.

GenAI Is No Silver Bullet for Qualitative Research in Software Engineering

This paper argues that while Generative AI offers potential assistance for qualitative research in software engineering, it is not a universal solution and requires careful, strategy-specific adaptation to avoid overgeneralization, ultimately outlining its promises, pitfalls, and implications for research quality.

Gender Bias in Perception of Human Managers Extends to AI Managers

This study demonstrates that gender biases in leadership perceptions, where male managers are favored and female managers face greater skepticism upon making unfavorable decisions, extend from human leaders to AI managers, highlighting the need to address these prejudices in the design of AI-driven organizational systems.

Generative AI and LLMs in Industry: A text-mining Analysis and Critical Evaluation of Guidelines and Policy Statements Across Fourteen Industrial Sectors

This study employs text-mining techniques to analyze 160 guidelines and policy statements across fourteen industrial sectors, offering critical insights and recommendations for balancing innovation with ethical accountability in the governance of Generative AI and Large Language Models.

GeoAlignCLIP: Enhancing Fine-Grained Vision-Language Alignment in Remote Sensing via Multi-Granular Consistency Learning

The paper introduces GeoAlignCLIP, a unified framework that enhances fine-grained vision-language alignment in remote sensing by leveraging multi-granular semantic learning and intra-modal consistency, supported by a newly constructed hierarchical dataset (RSFG-100k) to outperform existing methods on diverse benchmarks.

GeoBenchr: An Application-Centric Benchmarking Suite for Spatiotemporal Database Platforms

This paper introduces GeoBenchr, an open-source, application-centric benchmarking suite designed to comprehensively evaluate and compare the performance, scalability, and configuration impact of spatiotemporal database platforms across diverse real-world use cases.

GeoSolver: Scaling Test-Time Reasoning in Remote Sensing with Fine-Grained Process Supervision

The paper introduces GeoSolver, a framework that enhances remote sensing reasoning by leveraging a large-scale process supervision dataset (Geo-PRM-2M) and a novel Process-Aware Tree-GRPO algorithm to train a token-level reward model (GeoPRM), thereby enabling verifiable, step-by-step reasoning and robust test-time scaling for both specialized and general-purpose Vision-Language Models.

Geometry-Aware Metric Learning for Cross-Lingual Few-Shot Sign Language Recognition on Static Hand Keypoints

This paper proposes a geometry-aware metric learning framework using rotation- and scale-invariant inter-joint angle descriptors derived from static hand keypoints to achieve robust cross-lingual few-shot sign language recognition, significantly outperforming conventional coordinate-based methods across diverse sign languages.

Granulon: Awakening Pixel-Level Visual Encoders with Adaptive Multi-Granularity Semantics for MLLM

Granulon is a novel multimodal large language model that leverages a DINOv3-based visual encoder enhanced with a text-conditioned granularity controller and adaptive token aggregation to dynamically unify pixel-level perception with coarse-grained semantics, significantly improving accuracy and reducing hallucinations compared to existing approaches.

HECTOR: Hybrid Editable Compositional Object References for Video Generation

HECTOR is a novel video generation pipeline that enables fine-grained compositional control by supporting hybrid reference conditioning from static images and dynamic videos, while allowing users to explicitly specify the trajectories, locations, scales, and speeds of individual objects to synthesize coherent, high-fidelity videos.

HMR-1: Hierarchical Massage Robot with Vision-Language-Model for Embodied Healthcare

This paper addresses the lack of standardized benchmarks and datasets in embodied healthcare by introducing MedMassage-12K, a large-scale multimodal acupoint massage dataset, and proposing HMR-1, a hierarchical framework that leverages vision-language models for high-level acupoint grounding and low-level trajectory control to enable robust robotic massage therapy.

Hardness of the Binary Covering Radius Problem in Large $\ell_p$ Norms

This paper establishes the first explicit $\mathsf{NP}$-hardness results for the approximate Covering Radius Problem on lattices in $\ell_p$ norms for sufficiently large $p$ (specifically $p > 35.31$), proving that the problem remains hard even for approximation factors approaching $9/8$as$p$ tends to infinity.

HelixTrack: Event-Based Tracking and RPM Estimation of Propeller-like Objects

HelixTrack is a fully event-driven method that jointly tracks propeller-like objects and estimates their RPM with microsecond latency by back-warping events to a rotor plane and refining pose through phase-coupled geometry, validated on a newly introduced TQE dataset where it outperforms existing baselines.

HeteroFedSyn: Differentially Private Tabular Data Synthesis for Heterogeneous Federated Settings

The paper proposes HeteroFedSyn, the first differentially private framework for synthesizing tabular data in horizontal federated settings, which achieves utility comparable to centralized methods by introducing noise-efficient dependency metrics, unbiased noise correction, and adaptive selection strategies to handle heterogeneous data distributions.

Hierarchical Observe-Orient-Decide-Act Enabled UAV Swarms in Uncertain Environments: Frameworks, Potentials, and Challenges

This paper proposes a hierarchical Observe-Orient-Decide-Act (H-OODA) framework that integrates cloud-edge-terminal layers and network function virtualization to enhance the adaptability, scalability, and decision-making efficiency of UAV swarms operating in uncertain environments.

High-Slip-Ratio Control for Peak Tire-Road Friction Estimation Using Automated Vehicles

This paper proposes a high-slip-ratio control framework for automated vehicles that actively excites peak tire-road friction during empty-haul operations to enable accurate, safe, and scalable estimation of the tire-road friction coefficient through constrained optimal control and robust statistical projection.

How to Write to SSDs

This paper demonstrates that redesigning database systems to adopt out-of-place writes, exemplified by a modified LeanStore, significantly reduces write amplification and boosts throughput across diverse OLTP benchmarks while enabling support for modern SSD interfaces like ZNS and FDP.

Idempotent Slices with Applications to Code-Size Reduction

This paper formalizes the concept of idempotent backward slices and presents a sound, efficient algorithm for extracting them from Gated Static Single Assignment (GSA) form to enable a novel sparse code-size reduction optimization that merges non-contiguous instructions, achieving up to 7.24% size reduction in specific benchmarks.

Impact of Different Failures on a Robot's Perceived Reliability

This study demonstrates that in human-robot interaction, different failure types impact perceived reliability differently—with mistakes being less damaging than slips or lapses—and that trust can be effectively recovered through subsequent successful executions without the need for explicit social repair actions.

ImpedanceDiffusion: Diffusion-Based Global Path Planning for UAV Swarm Navigation with Generative Impedance Control

The paper presents ImpedanceDiffusion, a hierarchical framework that combines image-conditioned diffusion models for global path planning with reactive APF tracking and VLM-enhanced variable impedance control to enable safe, high-speed, and adaptive UAV swarm navigation in cluttered indoor environments without explicit map construction.

Implicit Biases in Refereeing: Lessons from NBA Referees

This study analyzes NBA play-by-play and Last Two Minutes data to find that while referees exhibit a home-court advantage bias (which has decreased since the pandemic) and show statistical favoritism toward specific players, there is no evidence of negative bias against particular players, teams, or racial groups.

Implicit Geometry Representations for Vision-and-Language Navigation from Web Videos

This paper introduces a large-scale framework for Vision-and-Language Navigation that leverages web-based room tour videos and implicit geometry representations to overcome simulator limitations, enabling robust zero-shot navigation agents with state-of-the-art performance across multiple benchmarks.

Improving 3D Foot Motion Reconstruction in Markerless Monocular Human Motion Capture

This paper introduces FootMR, a method that refines 3D foot motion in markerless monocular human motion capture by lifting 2D keypoints to 3D using large-scale motion capture data and context-aware residual prediction, alongside the new MOOF dataset, to significantly outperform existing state-of-the-art approaches in accuracy and generalization.

Improving Large Vision-Language Models' Understanding for Flow Field Data

This paper introduces FieldLVLM, a novel framework that enhances Large Vision-Language Models' ability to interpret complex scientific field data by combining a specialized pipeline for extracting physical features into structured text with a data-compressed tuning strategy, resulting in superior performance on scientific benchmarks.

Influence of Interactivity in Shaping User Experience and Social Acceptance of Mobile XR

This study investigates how varying degrees of interactivity in mobile augmented reality applications influence both user experience and social acceptability, revealing a complex relationship that necessitates a balanced design approach to ensure seamless integration into everyday social environments.

Integrating Virtual and Augmented Reality into Public Education: Opportunities and Challenges in Language Learning

This paper examines the opportunities and challenges of integrating Virtual and Augmented Reality into public language education, finding that while these technologies boost motivation and contextual learning, their effective implementation requires overcoming technical barriers, cognitive overload, and curriculum alignment issues through improved design, infrastructure, and teacher training.

Intelligent Spatial Estimation for Fire Hazards in Engineering Sites: An Enhanced YOLOv8-Powered Proximity Analysis Framework

This paper presents an enhanced dual-model YOLOv8 framework that integrates fire and smoke detection with proximity-based risk assessment to generate quantitative hazard scores and real-time situational awareness for engineering sites.

InternVL-U: Democratizing Unified Multimodal Models for Understanding, Reasoning, Generation and Editing

The paper introduces InternVL-U, a lightweight 4B-parameter unified multimodal model that democratizes advanced understanding, reasoning, generation, and editing capabilities by employing a modular architecture and a reasoning-centric data synthesis pipeline, achieving superior performance-efficiency balance that outperforms significantly larger baselines like BAGEL.

IntroSVG: Learning from Rendering Feedback for Text-to-SVG Generation via an Introspective Generator-Critic Framework

The paper introduces IntroSVG, an introspective framework that enhances text-to-SVG generation by employing a unified Visual Language Model in a closed-loop "generate-review-refine" cycle, where the model acts as both generator and critic to iteratively improve outputs based on visual rendering feedback.

Investigating the Effects of LLM Use on Critical Thinking Under Time Constraints: Access Timing and Time Availability

This study reveals that the impact of LLMs on critical thinking is fundamentally shaped by time constraints, where early or continuous access enhances performance under time pressure but impairs it when sufficient time is available, whereas delayed or no access yields the opposite effect.

Kinodynamic Motion Retargeting for Humanoid Locomotion via Multi-Contact Whole-Body Trajectory Optimization

This paper introduces KDMR, a novel framework that formulates humanoid motion retargeting as a multi-contact whole-body trajectory optimization problem incorporating rigid-body dynamics and ground reaction forces to generate physically consistent, dynamically feasible locomotion trajectories that significantly outperform purely kinematic methods in both motion quality and downstream control policy performance.

Kite: How to Delegate Voting Power Privately

This paper introduces Kite, a protocol that enables private delegation of voting power in Decentralized Autonomous Organizations (DAOs) by allowing voters to delegate, revoke, or re-delegate their votes without revealing delegate identities to anyone, including the delegates themselves, while maintaining public verifiability and demonstrating practical implementation on Ethereum.

LAP: A Language-Aware Planning Model For Procedure Planning In Instructional Videos

This paper introduces LAP, a novel procedure planning model that leverages a fine-tuned Vision Language Model to convert visual observations into distinctive text embeddings for a diffusion-based planner, achieving state-of-the-art performance on multiple benchmarks by effectively resolving visual ambiguities through language.

LARA-Gen: Enabling Continuous Emotion Control for Music Generation Models via Latent Affective Representation Alignment

LARA-Gen introduces a framework for continuous, fine-grained emotion control in music generation by aligning latent affective representations with an external emotion predictor and utilizing a valence-arousal control module, thereby overcoming the limitations of text-based prompting and significantly improving both emotional adherence and music quality.

LLaVAShield: Safeguarding Multimodal Multi-Turn Dialogues in Vision-Language Models

This paper introduces LLaVAShield, a safety auditing framework for multimodal multi-turn dialogues in Vision-Language Models, supported by the MMDS dataset and MMRT red-teaming framework, which collectively address the limitations of existing moderation tools by effectively detecting concealed malicious intent, contextual risk accumulation, and cross-modal joint risks.

Latency Effects on Multi-Dimensional QoE in Networked VR Whiteboards

This study systematically investigates how network latency degrades the pragmatic and hedonic dimensions of Quality of Experience in networked VR whiteboards by analyzing their varying impacts across sequential and free collaboration modes, as well as between avatar-enhanced and traditional VR or PC-based systems.

Learning Convex Decomposition via Feature Fields

This paper introduces a novel, self-supervised feature field learning approach that enables the first feed-forward model for open-world 3D convex decomposition, producing high-quality, generalizable results across diverse representations like meshes, CAD models, and Gaussian splats to accelerate applications such as collision detection.

Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks

This paper proposes an unsupervised method to recover the latent encoding and decoding direction pairs in deep vision networks, enabling the identification of interpretable concepts and facilitating model understanding, debugging, and intervention without relying on feature reconstruction.

Let's Reward Step-by-Step: Step-Aware Contrastive Alignment for Vision-Language Navigation in Continuous Environments

This paper introduces Step-Aware Contrastive Alignment (SACA), a novel framework that enhances Vision-Language Navigation in Continuous Environments by utilizing a perception-grounded auditor to extract dense, step-level supervision from imperfect trajectories, thereby overcoming the limitations of compounding errors in supervised fine-tuning and sparse rewards in reinforcement fine-tuning to achieve state-of-the-art performance.

Leveraging whole slide difficulty in Multiple Instance Learning to improve prostate cancer grading

This paper introduces the concept of Whole Slide Difficulty (WSD), derived from diagnostic disagreements between expert and non-expert pathologists, and demonstrates that leveraging this metric through multi-task learning or weighted loss functions significantly improves the accuracy of prostate cancer Gleason grading in Multiple Instance Learning models, particularly for higher-grade cases.

Lightening the Load: A Cluster-Based Framework for A Lower-Overhead, Provable Website Fingerprinting Defense

This paper introduces Adaptive Tamaraw, a unified framework that clusters traffic traces to dynamically apply lightweight, set-specific padding parameters, thereby achieving provable security against website fingerprinting attacks while significantly reducing communication overhead compared to traditional defenses.

Local Stability of Rankings

This paper introduces the concept of "local stability" to assess how minor changes in item values affect their rankings, particularly within dense regions of similar-quality items, and proposes efficient sampling-based algorithms (LStability and Detect-Dense-Region) with theoretical guarantees to compute this measure and identify such regions.

Lockbox -- A Zero Trust Architecture for Secure Processing of Sensitive Cloud Workloads

This paper presents Lockbox, a Zero Trust architecture that ensures the secure processing of sensitive cloud workloads by enforcing strict isolation, least-privilege access, and end-to-end encryption, thereby enabling enterprises to safely leverage advanced capabilities like AI without compromising their security posture.

LogoDiffuser: Training-Free Multilingual Logo Generation and Stylization via Letter-Aware Attention Control

LogoDiffuser is a training-free method that leverages multimodal diffusion transformers and letter-aware attention control to generate high-quality, multilingual logo designs by inputting target characters as images to preserve structural integrity while applying creative styles.

Low-rank Orthogonal Subspace Intervention for Generalizable Face Forgery Detection

To overcome the generalization failure of vanilla CLIP in face forgery detection caused by "low-rank spurious bias," this paper proposes SeLop, a causal representation learning method that identifies and removes spurious correlations via orthogonal low-rank subspace intervention, thereby achieving state-of-the-art performance with high robustness using only 0.39M trainable parameters.

M4-SAR: A Multi-Resolution, Multi-Polarization, Multi-Scene, Multi-Source Dataset and Benchmark for optical-SAR Object Detection

This paper introduces M4-SAR, a large-scale, multi-resolution, multi-polarization, and multi-source dataset with nearly one million labeled instances, alongside a unified benchmarking toolkit and a novel end-to-end fusion framework (E2E-OSDet) that collectively advance optical-SAR object detection by demonstrating significant performance gains over single-source methods in complex environments.

MARRS: Masked Autoregressive Unit-based Reaction Synthesis

This paper proposes MARRS, a novel framework that generates coordinated and fine-grained human reaction motions by combining a Unit-distinguished Motion VAE, Action-Conditioned Fusion with token masking, and Adaptive Unit Modulation to overcome the limitations of vector quantization and enhance inter-unit interaction.

MEGC2026: Micro-Expression Grand Challenge on Visual Question Answering

The MEGC 2026 challenge introduces two new tasks, Micro-Expression Video Question Answering (ME-VQA) and Micro-Expression Long-Video Question Answering (ME-LVQA), to advance the analysis of facial micro-expressions by leveraging the multimodal reasoning capabilities of large vision-language models on both short and long-duration video sequences.

MO-Playground: Massively Parallelized Multi-Objective Reinforcement Learning for Robotics

This paper introduces MORLAX, a GPU-native multi-objective reinforcement learning algorithm, and MO-Playground, a suite of GPU-accelerated environments, which together enable massively parallelized training that achieves 25–270x speedups and superior Pareto fronts for complex robotics tasks compared to legacy CPU-based approaches.

MORE-R1: Guiding LVLM for Multimodal Object-Entity Relation Extraction via Stepwise Reasoning with Reinforcement Learning

The paper introduces MORE-R1, a novel Large Vision-Language Model that leverages a two-stage training process combining Supervised Fine-Tuning on automatically constructed stepwise reasoning data and Reinforcement Learning with Group Relative Policy Optimization to achieve state-of-the-art performance in Multimodal Object-Entity Relation Extraction.

Mapping Historic Urban Footprints in France: Balancing Quality, Scalability and AI Techniques

This study presents a scalable dual-pass deep learning pipeline that successfully extracts the first open-access, nationwide urban footprint dataset for metropolitan France from historical maps (1925–1950), achieving 73% accuracy by effectively mitigating artifacts like text and contour lines to enable quantitative analysis of pre-1970s urban sprawl.

Measuring onion website discovery and Tor users' interests with honeypots

This paper presents a honeypot-based study conducted in early 2025 that reveals human Tor users primarily discover onion sites via the Ahmia search engine and exhibit significantly higher engagement with child sexual abuse material (CSAM) compared to other illicit categories, with English-language versions attracting the most interaction.

MedKCO: Medical Vision-Language Pretraining via Knowledge-Driven Cognitive Orchestration

MedKCO is a medical vision-language pretraining framework that overcomes the limitations of simultaneous concept learning by employing a two-level curriculum for data ordering and a self-paced asymmetric contrastive loss to dynamically adjust the learning objective, thereby significantly improving feature representations and downstream task performance.

Memory-Guided View Refinement for Dynamic Human-in-the-loop EQA

This paper introduces DynHiL-EQA, a new dataset for dynamic human-in-the-loop Embodied Question Answering, and proposes DIVRR, a training-free framework that enhances robustness and inference efficiency by refining ambiguous views and selectively managing memory in dynamic environments.

MetaDAT: Generalizable Trajectory Prediction via Meta Pre-training and Data-Adaptive Test-Time Updating

The paper proposes MetaDAT, a trajectory prediction framework that combines meta-learning pre-training with a data-adaptive test-time updating mechanism to achieve robust, fast, and accurate online adaptation under distribution shifts by dynamically adjusting learning rates and focusing on informative hard samples.

MissBench: Benchmarking Multimodal Affective Analysis under Imbalanced Missing Modalities

This paper introduces MissBench, a benchmark and framework for multimodal affective computing that addresses the gap in evaluating models under realistic, imbalanced missing modality conditions by standardizing protocols and proposing new diagnostic metrics (MEI and MLI) to reveal hidden modality inequities and optimization imbalances.

Modeling Concurrency Control as a Learnable Function

This paper introduces NeurCC, a novel learned concurrency control algorithm that utilizes Bayesian optimization and a graph reduction search to efficiently learn a high-performance function mapping database states to control actions, thereby consistently outperforming state-of-the-art algorithms across diverse and dynamic workloads.

Modeling Trend Dynamics with Variational Neural ODEs for Information Popularity Prediction

The paper proposes VNOIP, a novel method leveraging variational neural Ordinary Differential Equations with bidirectional jump ODEs and attention mechanisms to explicitly model continuous-time popularity trend dynamics, thereby significantly improving the accuracy and efficiency of information popularity prediction in online social networks compared to existing state-of-the-art approaches.

More than the Sum: Panorama-Language Models for Adverse Omni-Scenes

This paper introduces the Panorama-Language Modeling (PLM) paradigm and the PanoVQA dataset to enable holistic $360^\circ$ vision-language reasoning in adverse omni-scenes, demonstrating that a unified panoramic approach yields superior understanding compared to stitching multiple narrow-field-of-view inputs.

Multi-Quadruped Cooperative Object Transport: Learning Decentralized Pinch-Lift-Move

This paper presents a decentralized, communication-free learning framework for teams of quadruped robots to cooperatively transport ungraspable objects through physical contact alone, utilizing a hierarchical policy and a specialized reward formulation to achieve robust, scalable coordination without rigid mechanical coupling.

Multimodal Adversarial Quality Policy for Safe Grasping

This paper proposes the Multimodal Adversarial Quality Policy (MAQP), a framework that enhances safe robot grasping in human-robot interaction by introducing a Heterogeneous Dual-Patch Optimization Scheme and a Gradient-Level Modality Balancing Strategy to effectively generate multimodal adversarial patches that address distribution discrepancies and optimization imbalances between RGB and depth modalities.

Multimodal Classification via Total Correlation Maximization

This paper addresses the issue of modality competition in multimodal learning by theoretically analyzing the relationship between joint and unimodal approaches and proposing TCMax, a hyperparameter-free method that maximizes total correlation between multimodal features and labels to achieve state-of-the-art classification performance.

Multimodal Graph Representation Learning with Dynamic Information Pathways

This paper proposes DiP, a novel multimodal graph representation learning framework that utilizes modality-specific pseudo nodes and dynamic information pathways to achieve adaptive, sparse, and linear-complexity message propagation, consistently outperforming existing baselines in link prediction and node classification tasks.

MuxGel: Simultaneous Dual-Modal Visuo-Tactile Sensing via Spatially Multiplexing and Deep Reconstruction

MuxGel is a spatially multiplexed visuo-tactile sensor that overcomes the opacity trade-off in existing GelSight-style devices by using a checkerboard coating to simultaneously capture pre-contact vision and post-contact tactile signals through a single camera, with high-fidelity reconstruction achieved via a deep learning framework.

NLiPsCalib: An Efficient Calibration Framework for High-Fidelity 3D Reconstruction of Curved Visuotactile Sensors

The paper presents NLiPsCalib, an efficient and physics-consistent calibration framework that utilizes Near-Light Photometric Stereo and controllable light sources to enable high-fidelity 3D reconstruction of curved visuotactile sensors through simple contacts with everyday objects, thereby overcoming the cost and complexity of existing methods.

NS-VLA: Towards Neuro-Symbolic Vision-Language-Action Models

This paper introduces NS-VLA, a novel Neuro-Symbolic Vision-Language-Action framework that integrates symbolic encoding, solving, and online reinforcement learning to achieve superior data efficiency, zero-shot generalizability, and expanded exploration in robotic manipulation compared to existing methods.

NaviGait: Navigating Dynamically Feasible Gait Libraries using Deep Reinforcement Learning

NaviGait is a hierarchical framework that combines trajectory optimization and deep reinforcement learning to synthesize robust, intuitive bipedal locomotion by selecting and minimally morphing gaits from an offline library, thereby simplifying reward design and accelerating training while maintaining high fidelity to reference motions.

NaviNote: Enabling In-situ Spatial Annotation Authoring to Support Exploration and Navigation for Blind and Low Vision People

This paper presents NaviNote, a voice-based system that combines high-precision visual localization with an agentic architecture to enable blind and low vision users to author in-situ spatial annotations and navigate unfamiliar environments with greater accuracy.

Nemo: A Low-Write-Amplification Cache for Tiny Objects on Log-Structured Flash Devices

Nemo is a novel flash cache design that reduces application-level write amplification for tiny-object workloads by intentionally increasing hash collisions to improve set fill rates, while simultaneously maintaining high memory efficiency and low miss ratios through a bloom filter-based indexing mechanism and hybrid hotness tracking.

Network Slicing in 5G Mobile Communication Architecture, Profit Modeling, and Challenges

This paper presents a comprehensive overview of 5G network slicing as a key architectural enabler for efficient resource utilization, focusing specifically on its business aspects through profit modeling for both own-slice implementation and resource leasing, while also addressing existing challenges and future research directions.

Nezha: A Key-Value Separated Distributed Store with Optimized Raft Integration

Nezha is a distributed key-value store that resolves the I/O overhead caused by overlapping persistence operations in Raft-based systems by integrating key-value separation with an optimized persistence strategy and leveled garbage collection, thereby achieving significant throughput improvements while maintaining strong consistency.

No Cliques Allowed: The Next Step Towards BDD/FC Conjecture

This paper advances the resolution of the bounded derivation depth implies finite controllability (BDD $\Rightarrow$ FC) conjecture by proving that universal models generated by BDD rule sets cannot contain arbitrarily large tournaments without entailing a loop query, thereby significantly narrowing the space of potential counterexamples.

OTPL-VIO: Robust Visual-Inertial Odometry with Optimal Transport Line Association and Adaptive Uncertainty

This paper presents OTPL-VIO, a robust stereo visual-inertial odometry system that enhances performance in low-texture and illumination-challenging environments by employing a training-free deep descriptor with entropy-regularized optimal transport for line association and introducing adaptive uncertainty weighting to stabilize estimation.

OddGridBench: Exposing the Lack of Fine-Grained Visual Discrepancy Sensitivity in Multimodal Large Language Models

This paper introduces OddGridBench, a benchmark revealing that current multimodal large language models significantly underperform humans in detecting fine-grained visual discrepancies, and proposes OddGrid-GRPO, a reinforcement learning framework that effectively enhances this sensitivity through curriculum learning and distance-aware rewards.

OmniEarth: A Benchmark for Evaluating Vision-Language Models in Geospatial Tasks

This paper introduces OmniEarth, a comprehensive benchmark comprising 9,275 images and 44,210 verified instructions that evaluates Vision-Language Models across 28 geospatial tasks with a focus on perception, reasoning, and robustness, revealing significant performance gaps in current models for remote sensing applications.

OmniEdit: A Training-free framework for Lip Synchronization and Audio-Visual Editing

OmniEdit is a novel, training-free framework that achieves robust lip synchronization and audio-visual editing by reformulating the editing paradigm to replace the edit sequence with a target sequence, thereby eliminating the need for supervised fine-tuning and ensuring a smooth, stable generation process.

On the Cost of Evolving Task Specialization in Multi-Robot Systems

This study demonstrates that in multi-robot foraging scenarios with limited optimization budgets, evolving task-specialized controllers fails to improve efficiency and often underperforms compared to successfully optimized generalist behaviors.

On the Multi-Commodity Flow with convex objective function: Column-Generation approaches

This paper proposes a column-generation-based algorithmic framework to efficiently solve both splittable and unsplittable variants of the capacitated Multi-Commodity Flow problem with convex, potentially non-differentiable, link cost functions, offering a robust optimization approach for managing traffic in telecommunication networks.

On the Online Weighted Non-Crossing Matching Problem

This paper investigates the online weighted non-crossing matching problem in the Euclidean plane, establishing that while deterministic algorithms fail to achieve non-trivial competitive ratios, randomization enables constant competitiveness, and further analyzing variants involving revocability, collinear points, and advice complexity.

On the Structural Failure of Chamfer Distance in 3D Shape Optimization

This paper identifies a structural gradient failure in Chamfer distance optimization that causes point cloud collapse, demonstrating that introducing non-local coupling mechanisms is a necessary condition to suppress this collapse and achieve successful 3D shape optimization.

Open-World Task and Motion Planning via Vision-Language Model Genereated Constraints

The paper introduces OWL-TAMP, a novel framework that integrates Vision-Language Models into Task and Motion Planning systems to generate language-parameterized discrete and continuous constraints, enabling robots to solve complex, long-horizon manipulation tasks specified in natural language within open-world environments.

OptBench: An Interactive Workbench for AI/ML-SQL Co-Optimization[Extended Demonstration Proposal]

This paper presents OptBench, an interactive workbench built on DuckDB that enables researchers and practitioners to transparently build, benchmark, and visually compare query optimizers for hybrid SQL and AI/ML workloads through a unified platform.

Optimal conversion from Rényi Differential Privacy to $f$-Differential Privacy

This paper proves that the conjectured conversion rule, which maps a Rényi Differential Privacy profile to an $f$-Differential Privacy trade-off function via the pointwise maximum of single-order bounds (equivalent to the intersection of RDP privacy regions), is optimal and cannot be uniformly improved upon for any valid RDP profile or Type I error level.

Optimal partition selection with Rényi differential privacy

This paper generalizes optimal partition selection algorithms to Rényi differential privacy, introducing an improved mechanism for $L^2$ bounded weighted partitions that enhances state-of-the-art methods while demonstrating a fundamental performance gap between additive and non-additive noise approaches when frequency data is also released.

Overview of the TREC 2025 Retrieval Augmented Generation (RAG) Track

The TREC 2025 RAG Track advances research on trustworthy retrieval-augmented generation systems by introducing complex narrative queries, leveraging the MS MARCO V2.1 corpus, and employing a multi-faceted evaluation framework to foster innovation in context-aware, factually grounded responses.

PIM-SHERPA: Software Method for On-device LLM Inference by Resolving PIM Memory Attribute and Layout Inconsistencies

This paper introduces PIM-SHERPA, a software-only method that resolves memory attribute and layout inconsistencies in product-level PIM-enabled systems to enable efficient on-device LLM inference, achieving significant memory capacity savings while maintaining near-theoretical performance.

ParTY: Part-Guidance for Expressive Text-to-Motion Synthesis

The paper proposes ParTY, a novel framework that improves text-to-motion synthesis by introducing part-guided networks, part-aware text grounding, and holistic-part fusion to overcome the limitations of existing methods in aligning specific body part actions with text while maintaining full-body motion coherence.

Paralinguistic Emotion-Aware Validation Timing Detection in Japanese Empathetic Spoken Dialogue

This paper proposes a paralinguistic- and emotion-aware model that detects optimal timing for emotional validation in Japanese empathetic dialogue using only speech signals, achieving significant improvements over conventional baselines by integrating self-supervised paralinguistic and multi-task emotion classification encoders.

Pathwise Test-Time Correction for Autoregressive Long Video Generation

This paper introduces Test-Time Correction (TTC), a training-free method that stabilizes long-sequence video generation in distilled autoregressive models by using the initial frame as a reference anchor to calibrate intermediate states, thereby overcoming error accumulation and extending generation lengths without compromising quality.

Physics-Conditioned Grasping for Stable Tool Use

This paper introduces inverse Tool-use Planning (iTuP) and its associated Stable Dynamic Grasp Network (SDG-Net), which enhance robotic tool use success by selecting grasps that minimize predicted task-induced wrench and torque rather than relying solely on perception or static geometry.

Physics-Driven 3D Gaussian Rendering for Zero-Shot MRI Super-Resolution

This paper proposes a zero-shot MRI super-resolution framework that leverages explicit, physics-driven 3D Gaussian representations and parallel brick-based rasterization to achieve high-quality, efficient reconstruction without relying on costly paired training data.

PixelConfig: Longitudinal Measurement and Reverse-Engineering of Meta Pixel Configurations

This paper introduces PixelConfig, a framework for reverse-engineering Meta Pixel configurations, which reveals that default settings drive widespread adoption of activity and identity tracking features capable of capturing sensitive health data, while existing tracking restriction mechanisms offer limited practical protection.

Platooning as a Service (PlaaS): A Sustainable Transportation Framework for Connected and Autonomous Vehicles

This paper introduces Platooning as a Service (PlaaS), a Stackelberg game-based decision-support framework that optimizes pricing and travel distance between service providers and users to enhance sustainable transportation, while analyzing how factors like government subsidies and vehicle velocity impact profitability and carbon emissions.

Point Cloud as a Foreign Language for Multi-modal Large Language Model

The paper introduces SAGE, the first end-to-end multi-modal large language model that treats raw point clouds as a "foreign language" via a lightweight 3D tokenizer and semantic alignment-based preference optimization, achieving superior performance and efficiency over existing encoder-based methods in 3D understanding tasks.

Polynomial-time Configuration Generator for Connected Unlabeled Multi-Agent Pathfinding

This paper addresses the Connected Unlabeled Multi-Agent Pathfinding (CUMAPF) problem by introducing PULL, a lightweight, complete algorithm that runs in $O(n^2)$ time per step and outperforms both an Integer Linear Programming approach and naive methods in scalability and efficiency for swarm robotics applications.

Predictive Spectral Calibration for Source-Free Test-Time Regression

This paper proposes Predictive Spectral Calibration (PSC), a simple and model-agnostic source-free framework that enhances test-time adaptation for image regression by extending subspace alignment to block spectral matching, thereby achieving consistent performance improvements over strong baselines, especially under severe distribution shifts.

Preparing Students for AI-Driven Agile Development: A Project-Based AI Engineering Curriculum

This paper presents a project-based AI engineering curriculum that integrates agile practices with generative AI tools to prepare students for modern software development, demonstrating through a seven-sprint case study that embedding AI across the engineering lifecycle fosters hands-on competence while necessitating adaptations for tool evolution and foundational learning verification.

ProGS: Towards Progressive Coding for 3D Gaussian Splatting

ProGS introduces a novel streaming-friendly codec that organizes 3D Gaussian Splatting data into an octree structure with mutual information enhancement, achieving a 45-fold reduction in file size and over 10% visual improvement while enabling progressive coding for varying bandwidth conditions.

Probing the Reliability of Driving VLMs: From Inconsistent Responses to Grounded Temporal Reasoning

This paper investigates the reliability of Vision-Language Models (VLMs) in autonomous driving by exposing their tendencies toward response inconsistency and weak temporal reasoning, and subsequently proposes the FutureVQA benchmark and a self-supervised chain-of-thought tuning method to enhance grounded future scene reasoning without requiring temporal labels.

Progressive Representation Learning for Multimodal Sentiment Analysis with Incomplete Modalities

This paper proposes PRLF, a Progressive Representation Learning Framework that addresses missing modalities in Multimodal Sentiment Analysis by dynamically estimating modality reliability and iteratively aligning incomplete features with a dominant modality to enhance robustness and performance.

Progressive Split Mamba: Effective State Space Modelling for Image Restoration

The paper proposes Progressive Split-Mamba (PS-Mamba), a topology-aware hierarchical state-space framework that addresses the spatial distortion and long-range decay limitations of standard Mamba models in image restoration by employing geometry-consistent partitioning and symmetric cross-scale shortcuts to effectively balance local structural preservation with global coherence.

Prompt-Driven Color Accessibility Evaluation in Diffusion-based Image Generation Models

This paper introduces "CVDLoss," a novel metric for evaluating color accessibility in diffusion-based image generation, and demonstrates that current models struggle to reliably respond to accessibility-focused prompts despite the metric's sensitivity to structural detail changes.

Proper Body Landmark Subset Enables More Accurate and 5X Faster Recognition of Isolated Signs in LIBRAS

This paper demonstrates that selecting an optimal subset of body landmarks combined with spline-based imputation enables isolated Brazilian Sign Language (LIBRAS) recognition that is both 5 times faster and as accurate as state-of-the-art methods, overcoming the speed-accuracy trade-off of previous OpenPose-based approaches.

Proportionality Degree in Participatory Budgeting

This paper initiates the study of proportionality degree in participatory budgeting by establishing tight theoretical bounds for the Method of Equal Shares and Phragmen's Sequential Rule, demonstrating that despite their differing axiomatic properties, they achieve comparable quantitative proportionality, a finding further validated through extensive experiments on real-world datasets.

Proprioceptive Safe Active Navigation and Exploration for Planetary Environments

This paper introduces PSANE, a framework that enables legged robots to safely navigate and explore unknown deformable granular terrains by leveraging proprioceptive leg-terrain interactions to learn traversability models and optimize frontier selection for real-time, goal-directed navigation.

ProvAgent: Threat Detection Based on Identity-Behavior Binding and Multi-Agent Collaborative Attack Investigation

ProvAgent is a novel framework that enhances threat detection and investigation by integrating graph contrastive learning for high-fidelity alert generation with a multi-agent collaborative system to autonomously reconstruct complex APT attack processes, thereby overcoming the limitations of traditional human-model collaboration.

Provably Safe Trajectory Generation for Manipulators Under Motion and Environmental Uncertainties

This paper presents a provably safe motion planning framework for robot manipulators in uncertain, non-convex environments by integrating a deep stochastic Koopman operator for state prediction with a hierarchical sum-of-squares verification filter within a Model Predictive Path Integral controller to generate certified, collision-free trajectories.

Prune Redundancy, Preserve Essence: Vision Token Compression in VLMs via Synergistic Importance-Diversity

PruneSID is a training-free, synergistic importance-diversity framework that significantly enhances Vision-Language Model efficiency by employing Principal Semantic Components Analysis and Intra-group Non-Maximum Suppression to achieve state-of-the-art accuracy with extreme token compression and faster prefilling speeds.

Pwned: How Often Are Americans' Online Accounts Breached?

By combining data from a representative sample of 5,000 American adults with breach records from Have I Been Pwned, the study estimates that at least 82.84% of Americans have experienced an account breach, with an average of at least three breaches per person, a risk that is higher among women, White individuals, the middle-aged, and those with higher education levels.

Queer NLP: A Critical Survey on Literature Gaps, Biases and Trends

This survey critically examines the growing body of LGBTQIA+ NLP research within the ACL Anthology, revealing a reactive focus on identifying bias rather than proactive mitigation, and calls for future work to prioritize stakeholder involvement, intersectionality, interdisciplinarity, and non-English languages to build more just and inclusive technologies.

RA-SSU: Towards Fine-Grained Audio-Visual Learning with Region-Aware Sound Source Understanding

This paper introduces a new fine-grained Audio-Visual Learning task called Region-Aware Sound Source Understanding (RA-SSU), supported by two novel datasets (f-Music and f-Lifescene) and a state-of-the-art model named SSUFormer, which utilizes specialized modules to achieve precise sound source segmentation and detailed frame-level textual descriptions.

RAE-NWM: Navigation World Model in Dense Visual Representation Space

The paper proposes RAE-NWM, a navigation world model that operates in a dense DINOv2 feature space using a Conditional Diffusion Transformer with a decoupled head and time-driven gating to achieve superior structural stability and action accuracy compared to traditional latent-space approaches.

RESBev: Making BEV Perception More Robust

The paper introduces RESBev, a plug-and-play framework that enhances the robustness of Bird's-eye-view (BEV) perception systems against sensor degradation and adversarial attacks by reframing the problem as latent semantic prediction to reconstruct clean features from corrupted observations.

RSH-SpMM: A Row-Structured Hybrid Kernel for Sparse Matrix-Matrix Multiplication on GPUs

The paper presents RSH-SpMM, a fine-grained row-structured hybrid kernel for GPU-based Sparse Matrix-Matrix Multiplication that utilizes adaptive row partitioning, RS-Tile representation, and load-balanced reordering to achieve 1.27x to 6.13x speedups over state-of-the-art methods by effectively handling extreme sparsity irregularity.

RTFDNet: Fusion-Decoupling for Robust RGB-T Segmentation

RTFDNet is a three-branch encoder-decoder network that unifies synergistic feature fusion with cross-modal and region decoupling regularization to achieve robust RGB-T semantic segmentation, enabling strong performance even when sensor signals are partially missing without requiring multi-stage training.

ReCoSplat: Autoregressive Feed-Forward Gaussian Splatting Using Render-and-Compare

ReCoSplat is an autoregressive feed-forward Gaussian Splatting model that overcomes the training-inference pose mismatch dilemma through a novel Render-and-Compare module and achieves state-of-the-art online novel view synthesis with efficient long-sequence handling via hybrid KV cache compression.

ReTac-ACT: A State-Gated Vision-Tactile Fusion Transformer for Precision Assembly

ReTac-ACT is a state-gated vision-tactile fusion transformer that achieves high-precision assembly in occluded, contact-rich environments by dynamically prioritizing tactile feedback through bidirectional cross-attention and proprioception-conditioned gating, outperforming vision-only baselines on the NIST Assembly Task Board M1 benchmark.

Real-Time Neural Video Compression with Unified Intra and Inter Coding

This paper presents a real-time neural video compression framework that unifies intra and inter coding within a single adaptive model and employs a simultaneous two-frame compression design to effectively handle disocclusion, prevent error propagation, and achieve a 12.1% BD-rate reduction over DCVC-RT while maintaining real-time performance.

Reasoning-Oriented Programming: Chaining Semantic Gadgets to Jailbreak Large Vision Language Models

This paper introduces "Reasoning-Oriented Programming," an automated attack framework that bypasses Large Vision-Language Model safety alignments by chaining semantically orthogonal benign visual inputs to force the emergence of harmful logic only during late-stage reasoning, thereby outperforming existing jailbreak methods on state-of-the-art models.

RecThinker: An Agentic Framework for Tool-Augmented Reasoning in Recommendation

RecThinker is an agentic framework that enhances recommendation systems by shifting from passive information processing to autonomous investigation, utilizing an Analyze-Plan-Act paradigm with specialized tools and a self-augmented training pipeline to dynamically bridge information gaps and improve reasoning accuracy.

Recognition-Synergistic Scene Text Editing

This paper introduces RS-STE, a novel unified framework that synergistically integrates text recognition and editing via a multi-modal parallel decoder and cyclic self-supervised fine-tuning to achieve state-of-the-art scene text editing performance on both synthetic and real-world benchmarks.

RegionReasoner: Region-Grounded Multi-Round Visual Reasoning

This paper introduces RegionReasoner, a reinforcement learning framework that enforces grounded, multi-round visual reasoning by requiring explicit bounding box citations and global-local semantic consistency, alongside a new benchmark called RegionDial-Bench, to significantly improve spatial grounding and reasoning accuracy in vision-language models.

Removing the Trigger, Not the Backdoor: Alternative Triggers and Latent Backdoors

This paper challenges the assumption that neutralizing known triggers eliminates backdoors by demonstrating that perceptually distinct "alternative triggers" can reliably activate latent backdoor directions in feature space, thereby advocating for defenses that target these underlying representation patterns rather than specific input triggers.

Revisiting Replanning from Scratch: Real-Time Incremental Planning with Fast Almost-Surely Asymptotically Optimal Planners

This paper challenges the conventional assumption that reactive replanning requires updating existing plans by demonstrating that using fast almost-surely asymptotically optimal (ASAO) algorithms to solve a series of independent planning problems offers a more efficient and effective approach for navigating changing environments.

RiO-DETR: DETR for Real-time Oriented Object Detection

RiO-DETR is the first real-time oriented object detection transformer that addresses challenges in angle estimation, periodicity, and convergence through novel designs like Content-Driven Angle Estimation and Decoupled Periodic Refinement, achieving a new speed-accuracy trade-off on benchmark datasets.

Robotic Scene Cloning:Advancing Zero-Shot Robotic Scene Adaptation in Manipulation via Visual Prompt Editing

This paper introduces Robotic Scene Cloning (RSC), a novel method that enhances zero-shot robotic manipulation by editing existing operation trajectories through visual prompting and condition injection to generate accurate, scene-consistent samples that significantly improve policy generalization in real-world environments.

Robust Cooperative Localization in Featureless Environments: A Comparative Study of DCL, StCL, CCL, CI, and Standard-CL

This paper presents a comparative study of five cooperative localization algorithms in featureless, GPS-denied environments, revealing that while Sequential and Standard methods offer high accuracy at the cost of filter inconsistency, Covariance Intersection provides the most balanced trade-off between accuracy and robustness for safety-critical applications.

Robust Provably Secure Image Steganography via Latent Iterative Optimization

This paper proposes a robust and provably secure image steganography framework that utilizes latent-space iterative optimization to significantly enhance message extraction accuracy under various compression and processing scenarios while maintaining security guarantees.

Robust Spatiotemporal Motion Planning for Multi-Agent Autonomous Racing via Topological Gap Identification and Accelerated MPC

This paper presents a robust spatiotemporal motion planning framework for multi-agent autonomous racing that combines topological gap identification via stochastic Gaussian processes with a PTC-accelerated Linear Time-Varying MPC to achieve high-speed overtaking with strict kinematic feasibility and significantly reduced computational latency.

Robustness Over Time: Understanding Adversarial Examples' Effectiveness on Longitudinal Versions of Large Language Models

This paper presents a longitudinal study of GPT, Llama, and Qwen models, revealing that continuous updates and increased model sizes do not consistently enhance adversarial robustness against misclassification, jailbreaks, and hallucinations, and can sometimes exacerbate existing vulnerabilities.

Role Classification of Hosts within Enterprise Networks Based on Connection Patterns

This paper addresses the problem of role classification in enterprise networks by introducing two practical algorithms that group hosts based on evolving connection patterns to simplify network management and enhance monitoring accuracy, demonstrating their effectiveness through commercial implementation and significant reduction in host grouping complexity.

Rotation Equivariant Mamba for Vision Tasks

This paper introduces EQ-VMamba, the first rotation-equivariant visual Mamba architecture that incorporates a specialized cross-scan strategy and group Mamba blocks to achieve superior performance and robustness across various vision tasks while reducing parameter counts by approximately 50% compared to non-equivariant baselines.

SEA-Nav: Efficient Policy Learning for Safe and Agile Quadruped Navigation in Cluttered Environments

The paper introduces SEA-Nav, a reinforcement learning framework that combines differentiable control barrier functions, adaptive collision replay, and kinematic constraints to enable quadruped robots to achieve safe, agile, and efficient navigation in densely cluttered environments with minute-level training time.

SPAN-Nav: Generalized Spatial Awareness for Versatile Vision-Language Navigation

SPAN-Nav is an end-to-end foundation model that achieves state-of-the-art, robust generalization in versatile vision-language navigation by leveraging a massive 4.2-million-annotation dataset to learn universal 3D spatial priors, which are efficiently encoded into a single token to guide action reasoning across diverse indoor and outdoor environments.

SPAN: Spatial-Projection Alignment for Monocular 3D Object Detection

The paper proposes SPAN, a novel framework for monocular 3D object detection that enhances geometric consistency and performance by introducing Spatial Point Alignment and 3D-2D Projection Alignment within a Hierarchical Task Learning strategy to overcome the limitations of traditional decoupled prediction paradigms.

STONE Dataset: A Scalable Multi-Modal Surround-View 3D Traversability Dataset for Off-Road Robot Navigation

This paper introduces STONE, a large-scale, multi-modal off-road dataset featuring synchronized LiDAR, camera, and radar data with automated, annotation-free 3D traversability labels, alongside a new benchmark for voxel-level traversability prediction.

SVG-EAR: Parameter-Free Linear Compensation for Sparse Video Generation via Error-aware Routing

The paper introduces SVG-EAR, a parameter-free method that enhances sparse video generation in Diffusion Transformers by using semantic clustering for linear compensation and error-aware routing to selectively compute high-error blocks, thereby achieving significant speedups while maintaining generation fidelity.

Scalable and Performant Data Loading

This paper introduces SPDL, an open-source, framework-agnostic library that significantly accelerates GPU data loading by leveraging concurrent thread pool execution with GIL release, achieving up to 74% faster iteration and reduced resource usage compared to PyTorch DataLoader while demonstrating further performance gains with Free-Threaded Python.

Scaling Multilingual Semantic Search in Uber Eats Delivery

This paper presents a production-oriented, unified multilingual semantic retrieval system for Uber Eats that leverages a fine-tuned Qwen2 two-tower model with advanced loss functions and Matryoshka Representation Learning to achieve significant recall improvements across stores, dishes, and grocery items.

See, Plan, Rewind: Progress-Aware Vision-Language-Action Models for Robust Robotic Manipulation

The paper introduces See, Plan, Rewind (SPR), a progress-aware vision-language-action framework that enhances robotic manipulation robustness by dynamically grounding instructions into spatial subgoals and enabling closed-loop error recovery through state rewinding, achieving state-of-the-art performance on challenging benchmarks without additional training.

Self-hosted Lecture-to-Quiz: Local LLM MCQ Generation with Deterministic Quality Control

This paper presents an end-to-end, self-hosted pipeline that converts lecture PDFs into multiple-choice questions using a local LLM and deterministic quality control, ensuring privacy and accountability while releasing a validated 24-question dataset with a detailed warning taxonomy for educational use.

Semi-Supervised Biomedical Image Segmentation via Diffusion Models and Teacher-Student Co-Training

This paper proposes a novel semi-supervised teacher-student framework for biomedical image segmentation that leverages denoising diffusion probabilistic models to generate and iteratively refine pseudo-labels, demonstrating superior performance over state-of-the-art methods in scenarios with limited annotated data.

Serving Compound Inference Systems on Datacenter GPUs

JigsawServe is a novel serving framework that optimizes latency, accuracy, and GPU resource costs for compound inference systems by jointly selecting model variants and spatially partitioning GPUs, achieving up to 11.3x higher throughput and significantly improved resource efficiency compared to prior work.

ShapeMark: Robust and Diversity-Preserving Watermarking for Diffusion Models

ShapeMark is a robust and diversity-preserving watermarking method for diffusion models that encodes watermark bits into structured noise patterns rather than individual values and employs a randomization design to prevent fixed patterns, thereby achieving state-of-the-art robustness against lossy scenarios while maintaining high generation quality.

Simultaneous Embedding of Two Paths on the Grid

This paper establishes that minimizing the longest edge length in a simultaneous geometric embedding of two paths on an integer grid is NP-hard, while providing an $O(n^{3/2})$ algorithm to minimize the grid perimeter when one path is $x$-monotone and the other is $y$-monotone.

SinGeo: Unlock Single Model's Potential for Robust Cross-View Geo-Localization

SinGeo is a novel framework that achieves robust cross-view geo-localization using a single model by employing a dual discriminative learning architecture and a curriculum learning strategy, thereby overcoming the limitations of existing methods that struggle with unseen fields of view and orientations.

SkipGS: Post-Densification Backward Skipping for Efficient 3DGS Training

SkipGS is a plug-and-play method that accelerates 3D Gaussian Splatting training by adaptively skipping redundant backward passes during the post-densification phase based on view-specific loss statistics, achieving a 23.1% reduction in total training time without compromising reconstruction quality.

Spectral-Structured Diffusion for Single-Image Rain Removal

The paper introduces SpectralDiff, a spectral-structured diffusion framework that leverages structured spectral perturbations and a full-product U-Net architecture to efficiently and effectively remove multi-directional rain streaks from single images.

Speeding Up the Learning of 3D Gaussians with Much Shorter Gaussian Lists

This paper proposes novel training strategies and losses, including Gaussian scale resetting and an entropy constraint on alpha blending, to shorten the Gaussian lists used in 3D Gaussian splatting, thereby significantly accelerating the learning process without compromising rendering quality.

SpikeSMOKE: Spiking Neural Networks for Monocular 3D Object Detection with Cross-Scale Gated Coding

This paper proposes SpikeSMOKE, a low-power monocular 3D object detection framework based on Spiking Neural Networks that introduces a Cross-Scale Gating Coding mechanism and a lightweight residual block to overcome information loss and computational inefficiency, achieving superior performance on KITTI and other datasets while significantly reducing energy consumption and model complexity compared to traditional ANN-based approaches.

Stein Variational Ergodic Surface Coverage with SE(3) Constraints

This paper introduces a preconditioned SE(3) Stein Variational Gradient Descent framework that reformulates point-cloud surface coverage as a manifold-aware sampling problem, enabling robots to generate high-quality, SE(3)-constrained trajectories that outperform existing optimization-based and sampling-as-optimization methods in both simulation and real-world experiments.

Stepping VLMs onto the Court: Benchmarking Spatial Intelligence in Sports

This paper introduces CourtSI, a large-scale dataset and benchmark for evaluating spatial intelligence in vision-language models within sports scenarios, revealing significant performance gaps in existing models while demonstrating that fine-tuning on this data substantially improves accuracy and generalization.

Streaming Autoregressive Video Generation via Diagonal Distillation

This paper introduces Diagonal Distillation, an asymmetric autoregressive framework that leverages temporal context and implicit optical flow to enable high-fidelity, real-time streaming video generation with a 277.3x speedup while mitigating error accumulation and motion incoherence.

StyleVLA: Driving Style-Aware Vision Language Action Model for Autonomous Driving

StyleVLA is a physics-informed Vision Language Action model built on Qwen3-VL-4B that generates diverse, kinematically feasible driving trajectories tailored to specific styles, significantly outperforming state-of-the-art proprietary models on domain-specific autonomous driving tasks.

SurgCalib: Gaussian Splatting-Based Hand-Eye Calibration for Robot-Assisted Minimally Invasive Surgery

This paper presents SurgCalib, a markerless, Gaussian Splatting-based framework that achieves accurate hand-eye calibration for the da Vinci surgical robot by refining kinematic estimates through a differentiable rendering pipeline, thereby overcoming cable-driven inaccuracies and avoiding the sterility issues associated with traditional fiducial markers.

SurgFed: Language-guided Multi-Task Federated Learning for Surgical Video Understanding

The paper proposes SurgFed, a language-guided multi-task federated learning framework that utilizes Language-guided Channel Selection and Language-guided Hyper Aggregation to overcome tissue and task diversity challenges, thereby improving surgical video segmentation and depth estimation across heterogeneous clinical environments.

Symbolic Discovery of Stochastic Differential Equations with Genetic Programming

This paper introduces a genetic programming-based method for the symbolic discovery of stochastic differential equations that jointly optimizes drift and diffusion functions via maximum likelihood estimation, enabling the accurate, scalable, and interpretable modeling of noisy dynamical systems.

Synergistic Directed Execution and LLM-Driven Analysis for Zero-Day AI-Generated Malware Detection

This paper presents a novel hybrid framework that synergistically combines concolic execution, LLM-guided path prioritization, and deep learning to achieve provably sound and highly accurate detection of zero-day AI-generated malware, significantly outperforming conventional baselines on both standard and synthesized threat benchmarks.

TIDE: Text-Informed Dynamic Extrapolation with Step-Aware Temperature Control for Diffusion Transformers

TIDE is a training-free method that enables Diffusion Transformers to generate high-resolution images with arbitrary aspect ratios by introducing a text anchoring mechanism to correct prompt information loss and a step-aware dynamic temperature control to eliminate artifacts caused by attention dilution.

TIMID: Time-Dependent Mistake Detection in Videos of Robot Executions

This paper introduces TIMID, a weakly supervised video anomaly detection framework that leverages task and mistake prompts to detect complex, time-dependent errors in robot executions, addressing the limitations of existing models and out-of-the-box VLMs through a novel multi-robot simulation dataset for zero-shot evaluation.

TIMotion: Temporal and Interactive Framework for Efficient Human-Human Motion Generation

TIMotion is an efficient framework for human-human motion generation that improves upon existing methods by introducing Causal Interactive Injection, Role-Evolving Scanning, and Localized Pattern Amplification to better model temporal dynamics and interactive roles, thereby achieving superior performance on benchmark datasets.

TPIFM: A Task-Aware Model for Evaluating Perceptual Interaction Fluency in Remote AR Collaboration

This paper proposes the Task-Aware Perceptual Interaction Fluency Model (TPIFM), which evaluates the perceived responsiveness of remote collaborative augmented reality by integrating network impairments with task-specific just-noticeable differences derived from the Free Energy Principle to guide adaptive system design.

TRIP-Bag: A Portable Teleoperation System for Plug-and-Play Robotic Arms and Leaders

The paper introduces TRIP-Bag, a portable, suitcase-contained teleoperation system that enables rapid, high-fidelity data collection for robot learning by eliminating the embodiment gap through direct joint-to-joint control in diverse, non-laboratory environments.

Taming Preference Mode Collapse via Directional Decoupling Alignment in Diffusion Reinforcement Learning

This paper introduces Directional Decoupling Alignment (D$^2$-Align), a novel framework that mitigates Preference Mode Collapse in diffusion reinforcement learning by applying directional corrections to reward signals, thereby preserving generative diversity while achieving superior human preference alignment.

TemporalDoRA: Temporal PEFT for Robust Surgical Video Question Answering

The paper introduces TemporalDoRA, a parameter-efficient fine-tuning method that integrates lightweight temporal attention into the low-rank adaptation branch of vision encoders to enhance robustness against linguistic variations in surgical video question answering, validated by a new colonoscopy dataset and improved Out-of-Template performance.

Test-time Ego-Exo-centric Adaptation for Action Anticipation via Multi-Label Prototype Growing and Dual-Clue Consistency

This paper introduces Test-time Ego-Exo Adaptation for Action Anticipation (TE$^{2}$A$^{3}$), a novel task addressed by the Dual-Clue enhanced Prototype Growing Network (DCPGN) which utilizes a Multi-Label Prototype Growing Module and a Dual-Clue Consistency Module to effectively bridge the inter-view gap and adapt models online without target-view training data.

Tetris is Hard with Just One Piece Type

This paper establishes that Tetris clearing and survival are NP-hard for any single tetromino type except the O-piece under the Super Rotation System, disproving a long-standing conjecture about the I-piece, while simultaneously providing polynomial-time algorithms for cases involving only dominoes or $1\times k$ pieces under specific initial board conditions.

The 802.11 MAC protocol leads to inefficient equilibria

This paper demonstrates through game-theoretic analysis and simulation that the non-cooperative behavior of nodes under the 802.11 DCF and 802.11e MAC protocols leads to inefficient Nash equilibria, whereas decoupling channel allocation from individual transmission strategies can achieve superior throughput for all competing nodes.

The Bureaucracy of Speed: Structural Equivalence Between Memory Consistency Models and Multi-Agent Authorization Revocation

This paper proposes a Capability Coherence System (CCS) that maps memory consistency models to identity management, demonstrating through simulation that a Release Consistency-directed revocation strategy (RCC) achieves a constant bound on unauthorized operations independent of agent velocity, thereby outperforming traditional time-bounded approaches by orders of magnitude in high-speed agentic environments.

The Future of Software Engineering Conferences: A New Zealand Perspective

This paper examines the barriers faced by New Zealand researchers in attending software engineering conferences, such as high travel costs and scheduling misalignments, and proposes strategies like hybrid participation and governance reforms to foster more equitable global engagement.

The Patrologia Graeca Corpus: OCR, Annotation, and Open Release of Noisy Nineteenth-Century Polytonic Greek Editions

This paper introduces the Patrologia Graeca Corpus, a large-scale open resource featuring OCR-processed, lemmatized, and part-of-speech tagged text from degraded nineteenth-century bilingual Greek-Latin editions, which achieves state-of-the-art recognition accuracy and establishes a new benchmark for noisy polytonic Greek processing.

The Richest Paradigm You're Not Using: Commercial Videogames at the Intersection of Human-Computer Interaction and Cognitive Science

This paper argues that commercial videogames serve as a powerful, underutilized research environment at the intersection of human-computer interaction and cognitive science, offering ecologically valid contexts to study perception, attention, and executive functioning through a systematic framework that maps game affordances to cognitive demands.

The Spanning Ratio of the Directed $\Theta_6$-Graph is 5

This paper establishes that the spanning ratio of the directed $\Theta_6$-graph is exactly 5, thereby closing the previously known gap between 4 and 7 and providing the first tight bound for any directed $\Theta_k$-graph.

The Virtuous Cycle: AI-Powered Vector Search and Vector Search-Augmented AI

This ICDE 2026 tutorial paper provides a comprehensive overview of the synergistic "virtuous cycle" between AI and vector search, detailing how AI enhances vector search efficiency and how vector search, particularly through Retrieval-Augmented Generation, empowers Large Language Models, while also exploring co-optimization strategies, challenges, and future research directions.

The framework to unify all complexity dichotomy theorems for Boolean tensor networks

This paper proposes a unifying framework for Boolean tensor network complexity dichotomy theorems that classifies unresolved problems into nine cases based on finite group structures of binary functions, resolving higher-order cyclic group cases and advancing the order-1 case while identifying barriers involving quaternion subgroups.

TiPToP: A Modular Open-Vocabulary Planning System for Robotic Manipulation

TiPToP is a modular, open-vocabulary robotic planning system that integrates pretrained vision foundation models with a Task and Motion Planner to solve multi-step manipulation tasks from RGB images and natural language instructions without requiring any robot-specific training data, achieving performance comparable to or better than fine-tuned vision-language-action models while enabling detailed failure mode analysis.

Time warping with Hellinger elasticity

This paper introduces the Elastic Time Warping algorithm, which solves the time series matching problem in arbitrary metric spaces using a Hellinger kernel stretching penalty with cubic computational complexity.

ToolRosetta: Bridging Open-Source Repositories and Large Language Model Agents through Automated Tool Standardization

ToolRosetta is a unified framework that automatically transforms heterogeneous open-source code repositories into standardized, secure, and executable Model Context Protocol (MCP) tools, enabling LLM agents to autonomously plan and invoke specialized software for complex tasks with minimal human intervention.

TopoOR: A Unified Topological Scene Representation for the Operating Room

TopoOR introduces a novel topological scene representation for surgical operating rooms that leverages higher-order structures and attention mechanisms to preserve complex multimodal relationships and manifold geometry, thereby outperforming traditional graph and LLM-based methods in safety-critical tasks like sterility breach detection and robot phase prediction.

Touching Emotions, Smelling Shapes: Exploring Tactile, Olfactory and Emotional Cross-sensory Correspondences in Preschool Aged Children

This study investigates cross-sensory correspondences between touch, smell, and emotion in 26 preschool-aged children (2–4 years) through playful tasks, revealing significant associations and underlying strategies that inform design guidelines and methods for understanding early childhood sensory cognition.

Towards Instance Segmentation with Polygon Detection Transformers

This paper introduces Poly-DETR, a lightweight instance segmentation framework that reformulates the task as sparse vertex regression using polar representation and specialized attention mechanisms, achieving superior performance and reduced memory consumption compared to traditional mask-based methods, particularly in high-resolution and domain-specific scenarios.

Towards Terrain-Aware Safe Locomotion for Quadrupedal Robots Using Proprioceptive Sensing

This paper presents a proprioception-only framework for quadrupedal robots that combines a 2.5-D terrain estimation method with safety-critical control barrier functions to achieve robust state estimation and rigorous safety guarantees on uneven terrain.

Towards Unified Multimodal Interleaved Generation via Group Relative Policy Optimization

This paper proposes a reinforcement learning-based post-training strategy that extends Group Relative Policy Optimization (GRPO) with hybrid and process-level rewards to enable existing unified vision-language models to generate high-quality multimodal interleaved outputs without relying on large-scale interleaved datasets.

Towards Viewpoint-centric Artifact-based Regulatory Requirements Engineering for Compliance by Design

This paper reports on the synthesis and seeks feedback for the future evaluation of an Artefact Model for Regulatory Requirements Engineering (AM4RRE), aiming to bridge the gap between organizational regulatory processes and ad-hoc software development practices to achieve systematic, integrated compliance by design.

Towards Visual Query Segmentation in the Wild

This paper introduces Visual Query Segmentation (VQS), a new paradigm for pixel-level object localization in untrimmed videos, supported by the large-scale VQS-4K benchmark and the high-performing VQ-SAM method that extends SAM 2.

Towards a Goal-Centric Assessment of Requirements Engineering Methods for Privacy by Design

This paper proposes a goal-centric framework for assessing Requirements Engineering methods for Privacy by Design, arguing that practitioners should evaluate these methods based on organizational goals rather than solely on process characteristics to better support their selection and tailoring.

Towards a Taxonomy of Software Log Smells

This paper presents a taxonomy of nine log smells derived from a survey of 51 studies to help developers write better logging code, while also mapping these issues to existing repair tools and highlighting critical gaps in current research and tooling.

Tracing Everyday AI Literacy Discussions at Scale: How Online Creative Communities Make Sense of Generative AI

This study analyzes 122,000 Reddit conversations to reveal that AI literacy in creative communities is primarily practice-driven and event-responsive, challenging top-down frameworks by showing how creators organically prioritize tool usage over ethical discourse except during major AI milestones.

Training-Free Coverless Multi-Image Steganography with Access Control

The paper proposes MIDAS, a training-free diffusion-based framework that enables coverless multi-image steganography with user-specific access control through latent-level fusion, demonstrating superior performance in image quality, robustness, and security compared to existing methods.

Training-free Motion Factorization for Compositional Video Generation

This paper proposes a training-free, model-agnostic motion factorization framework that decomposes complex video generation into motionlessness, rigid, and non-rigid categories through a "planning before generation" paradigm to synthesize diverse instances with controlled appearance and motion.

Trajectory Optimization for Self-Wrap-Aware Cable-Towed Planar Object Manipulation under Implicit Tension Constraints

This paper formulates cable-towed planar object manipulation as a routing-aware, tensioning-implicit trajectory optimization problem that leverages self-wrapping to dynamically redirect torque, proposing a relaxation hierarchy where the Implicit-Mode Relaxation (IMR) effectively exploits self-wrap for turning maneuvers without the conservatism of explicit routing decisions.

Transformer-Based Multi-Region Segmentation and Radiomic Analysis of HR-pQCT Imaging

This paper introduces a novel, fully automated framework that utilizes a SegFormer transformer to segment multiple anatomical regions in HR-pQCT images and extract radiomic features, demonstrating that soft tissue analysis outperforms traditional bone-based metrics in detecting osteoporosis.

TriFusion-SR: Joint Tri-Modal Medical Image Fusion and SR

The paper proposes TriFusion-SR, a wavelet-guided conditional diffusion framework that jointly performs tri-modal medical image fusion and super-resolution by decomposing features into frequency bands and employing rectified wavelet features with adaptive spatial-frequency fusion to achieve state-of-the-art performance in resolution and perceptual quality.

TubeMLLM: A Foundation Model for Topology Knowledge Exploration in Vessel-like Anatomy

TubeMLLM is a unified foundation model that integrates topological priors via natural language prompting and a shared-attention architecture to achieve state-of-the-art, robust, and zero-shot generalizable topology-aware perception and generation for vessel-like anatomy across diverse medical imaging modalities.

UniBYD: A Unified Framework for Learning Robotic Manipulation Across Embodiments Beyond Imitation of Human Demonstrations

UniBYD is a unified framework that leverages a unified morphological representation and a dynamic reinforcement learning algorithm with a hybrid shadow engine to bridge the embodiment gap, enabling robotic hands to transcend human imitation and discover manipulation policies optimally adapted to their own physical morphologies.

UniField: A Unified Field-Aware MRI Enhancement Framework

The paper introduces UniField, a unified framework that leverages pre-trained 3D foundation models and a novel Field-Aware Spectral Rectification Mechanism to overcome data scarcity and spectral bias in MRI field-strength enhancement, supported by the release of a large-scale multi-field dataset that significantly outperforms state-of-the-art methods.

Unit Interval Selection in Random Order Streams

This paper presents a one-pass random-order streaming algorithm that achieves an expected approximation factor of 0.7401 for the Unit Interval Selection problem using space linear in the optimal solution size, thereby improving upon the 2/3 bound established for adversarial streams while also providing matching space lower bounds for higher approximation factors.

Unsupervised Domain Adaptation with Target-Only Margin Disparity Discrepancy

This paper proposes a novel unsupervised domain adaptation framework based on a reformulated Margin Disparity Discrepancy to bridge the modality gap between annotated CT and unannotated interventional CBCT scans, achieving state-of-the-art performance in liver segmentation for both unsupervised and few-shot settings.

Unveiling the Potential of iMarkers: Invisible Fiducial Markers for Advanced Robotics

This paper introduces iMarkers, a novel class of invisible fiducial markers detectable only by robots and AR devices, which overcome the visual aesthetic limitations of traditional markers while offering customizable production, robust detection algorithms, and proven effectiveness across diverse robotics scenarios.

V-Attack: Targeting Disentangled Value Features for Controllable Adversarial Attacks on LVLMs

This paper introduces V-Attack, a novel adversarial attack method for Large Vision-Language Models that achieves precise local semantic manipulation by targeting disentangled value features within transformer attention blocks, thereby overcoming the controllability limitations of existing approaches that rely on entangled patch-token representations.

VLM-Loc: Localization in Point Cloud Maps via Vision-Language Models

This paper introduces VLM-Loc, a framework that leverages large vision-language models to achieve precise text-to-point-cloud localization by transforming 3D maps into bird's-eye-view images and scene graphs for enhanced spatial reasoning, alongside the release of the CityLoc benchmark for systematic evaluation.

VarSplat: Uncertainty-aware 3D Gaussian Splatting for Robust RGB-D SLAM

VarSplat is an uncertainty-aware 3D Gaussian Splatting SLAM system that explicitly learns per-splat appearance variance to render differentiable uncertainty maps, thereby guiding tracking and optimization toward reliable regions to achieve robust pose estimation and high-fidelity reconstruction in challenging real-world environments.

VeriInteresting: An Empirical Study of Model Prompt Interactions in Verilog Code Generation

This paper presents an empirical study mapping the interactions between model characteristics and prompt engineering strategies for Verilog code generation, revealing which trends generalize across diverse language models and benchmarks through controlled experiments.

VisPoison: An Effective Backdoor Attack Framework for Tabular Data Visualization Models

This paper introduces VisPoison, a backdoor attack framework that exploits text-to-visualization models for tabular data by using stealthy triggers to cause data exposure, misleading visualizations, or denial-of-service failures with over 90% success rates, thereby highlighting critical security vulnerabilities in current systems and the inadequacy of existing defenses.

Vision-Augmented On-Track System Identification for Autonomous Racing via Attention-Based Priors and Iterative Neural Correction

This paper proposes a vision-augmented, iterative system identification framework that combines a lightweight CNN for friction priors and an S4 model for temporal dynamics to overcome cold-start failures and improve real-time tire parameter estimation for autonomous racing.

VisionCreator-R1: A Reflection-Enhanced Native Visual-Generation Agentic Model

The paper introduces VisionCreator-R1, a native visual-generation agent enhanced with explicit reflection mechanisms and trained via a Reflection-Plan Co-Optimization (RPCO) methodology that addresses credit assignment challenges to outperform state-of-the-art models on both single and multi-image generation benchmarks.

VocSegMRI: Multimodal Learning for Precise Vocal Tract Segmentation in Real-time MRI

The paper introduces VocSegMRI, a multimodal framework that leverages cross-attention fusion and contrastive learning to integrate video, audio, and phonological signals, achieving state-of-the-art vocal tract segmentation in real-time MRI with a Dice score of 0.95 and robust performance even when audio is unavailable.

WESPR: Wind-adaptive Energy-Efficient Safe Perception & Planning for Robust Flight with Quadrotors

WESPR is a lightweight, real-time framework that integrates geometric perception and local weather data to predict wind fields generated by environmental obstacles, enabling quadrotors to proactively plan safe, energy-efficient paths and adapt control strategies for robust flight in turbulent conditions.

WVA: A Global Optimization Control Plane for llmd

The paper introduces WVA, a global optimization control plane co-designed with the \texttt{llmd} inference engine that leverages internal saturation states and fragmentation-aware strategies to achieve significantly higher throughput, fewer request failures, and lower power consumption compared to traditional Kubernetes autoscalers when managing heterogeneous LLM workloads.

Walking on Rough Terrain with Any Number of Legs

This paper presents a computationally lightweight, segment-based control architecture for multi-legged robots with six or more legs that effectively navigates rough terrain by bridging the gap between event-driven and CPG-based controllers through tightly coupled ground contact and fictive locomotion.

When Detectors Forget Forensics: Blocking Semantic Shortcuts for Generalizable AI-Generated Image Detection

This paper introduces Geometric Semantic Decoupling (GSD), a parameter-free module that enhances the generalizability of AI-generated image detectors by explicitly removing dominant semantic priors from learned representations, thereby forcing models to rely on robust forensic artifacts rather than failing via "semantic fallback" when encountering unseen generation pipelines.

Where, What, Why: Toward Explainable 3D-GS Watermarking

This paper presents a representation-native framework for explainable 3D Gaussian Splatting watermarking that utilizes a Trio-Experts module and a Safety and Budget Aware Gate to optimize carrier selection and visual compensation, achieving superior robustness and fidelity while providing auditable insights into the watermarking process.

Who Made This? Fake Detection and Source Attribution with Diffusion Features

This paper introduces FRIDA, a lightweight and data-efficient framework that leverages features from pre-trained Stable Diffusion models to achieve state-of-the-art cross-generator fake image detection and source attribution through a training-free k-Nearest Neighbour approach and a compact neural classifier.

WikiCLIP: An Efficient Contrastive Baseline for Open-domain Visual Entity Recognition

WikiCLIP is an efficient contrastive framework for open-domain visual entity recognition that leverages large language model embeddings enhanced by a Vision-Guided Knowledge Adaptor and Hard Negative Synthesis to significantly outperform generative baselines while reducing inference latency by nearly 100 times.

YOLO-NAS-Bench: A Surrogate Benchmark with Self-Evolving Predictors for YOLO Architecture Search

This paper introduces YOLO-NAS-Bench, the first surrogate benchmark for YOLO-style object detectors, which employs a self-evolving mechanism to iteratively refine a LightGBM predictor, enabling efficient and accurate discovery of high-performing architectures that surpass official YOLO baselines.

You Only Pose Once: A Minimalist's Detection Transformer for Monocular RGB Category-level 9D Multi-Object Pose Estimation

YOPO is a minimalist, single-stage transformer framework that unifies 2D object detection and category-level 9-DoF pose estimation from monocular RGB images without requiring pseudo-depth or CAD models, achieving state-of-the-art performance on multiple benchmarks.

ZipPIR: High-throughput Single-server PIR without Client-side Storage

ZipPIR is a high-throughput single-server Private Information Retrieval protocol that achieves over 2 GB/s performance without requiring client-side storage by compressing LWE ciphertexts into Paillier ciphertexts during an almost silent offline phase, thereby overcoming the scalability and dynamic database limitations of existing solutions.

Zooming In on Fakes: A Novel Dataset for Localized AI-Generated Image Detection with Forgery Amplification Approach

This paper introduces BR-Gen, a large-scale dataset of 150,000 locally forged images with diverse scene-aware annotations, and proposes NFA-ViT, a noise-guided Vision Transformer that amplifies subtle forgery traces to significantly improve the detection and generalization of localized AI-generated image forgeries.

bsort: A theoretically efficient non-comparison-based sorting algorithm for integer and floating-point numbers

The paper introduces bsort, a non-comparison-based sorting algorithm that unifies the sorting of signed/unsigned integers and floating-point numbers with an asymptotic time complexity of $O(wn)$ and auxiliary space of $O(w)$, demonstrating competitive performance against optimized hybrid libraries for small word sizes.

d-DNNF Modulo Theories: A General Framework for Polytime SMT Queries

This paper introduces a general framework that extends Knowledge Compilation to Satisfiability Modulo Theories (SMT) by combining input formulas with pre-computed theory lemmas to enable polytime propositional-style querying of compiled SMT d-DNNFs.

d-QBF with Few Existential Variables Revisited

This paper resolves the open question regarding the parameterized complexity of QBF with few existential variables by proving that the previously known double-exponential dependence on the number of existential variables is optimal under the ETH for general CNFs, while demonstrating that the problem becomes significantly more tractable for formulas with only two quantifier blocks.

A Consensus-Driven Multi-LLM Pipeline for Missing-Person Investigations

This paper introduces Guardian, a consensus-driven, multi-LLM pipeline enhanced by QLoRA fine-tuning that coordinates specialized models and a consensus engine to perform auditable, structured information extraction for critical missing-person investigations while avoiding unconstrained decision-making.

A Consequentialist Critique of Binary Classification Evaluation: Theory, Practice, and Tools

This paper critiques the prevalent reliance on fixed-threshold metrics in machine learning evaluation by advocating for a consequentialist framework that prioritizes proper scoring rules like the Brier score, supported by a new decision-theoretic mapping, a practical Python package called `briertools`, and a clipped Brier score variant to bridge the gap between theoretical utility and current practices.

A Graph-Based Approach to Spectrum Demand Prediction Using Hierarchical Attention Networks

This paper introduces HR-GAT, a hierarchical resolution graph attention network that leverages geospatial data to predict spectrum demand with 21% higher accuracy than baseline models, effectively addressing spatial autocorrelation challenges to enable more efficient spectrum sharing and policy-making.

A Guideline-Aware AI Agent for Zero-Shot Target Volume Auto-Delineation

This paper introduces OncoAgent, a novel guideline-aware AI agent that achieves zero-shot, training-free auto-delineation of clinical target volumes by converting textual clinical guidelines into 3D contours, demonstrating superior adaptability and physician preference over traditional supervised deep learning models.

A Lightweight Multi-Cancer Tumor Localization Framework for Deployable Digital Pathology

The paper presents MuCTaL, a lightweight multi-cancer tumor localization framework trained on four cancer types that achieves high performance on training data and demonstrates generalization to unseen tumor types, enabling scalable deployment for digital pathology applications.

A New Modeling to Feature Selection Based on the Fuzzy Rough Set Theory in Normal and Optimistic States on Hybrid Information Systems

This paper introduces FSbuHD, a novel feature selection model for hybrid information systems that addresses the computational and noise limitations of traditional fuzzy rough set theory by reformulating the problem as an optimization task based on combined object distances, demonstrating superior efficiency and effectiveness in both normal and optimistic states across UCI datasets.

A Text-Native Interface for Generative Video Authoring

This paper introduces Doki, a text-native interface that enables users of varying expertise to author generative videos by defining assets, scenes, and edits directly within a freeform text document, thereby shifting video creation from specialized tools to a natural writing process.

A Variational Latent Equilibrium for Learning in Cortex

This paper proposes a biologically plausible, local learning framework for time-continuous neuronal networks that approximates backpropagation through time by deriving real-time error dynamics from a prospective energy function, thereby unifying and extending the Generalized Latent Equilibrium model to enable spatiotemporal credit assignment consistent with brain circuitry.

AI Act Evaluation Benchmark: An Open, Transparent, and Reproducible Evaluation Dataset for NLP and RAG Systems

This paper introduces an open, transparent, and reproducible dataset and methodology for evaluating NLP and RAG systems on EU AI Act compliance, featuring tasks like risk classification and obligation generation that leverage large language models to address regulatory ambiguities and achieve high performance scores.

AI Phenomenology for Understanding Human-AI Experiences Across Eras

This paper proposes "AI phenomenology" as a research framework that prioritizes users' first-person lived experiences over traditional performance metrics to better understand and guide the bidirectional alignment between humans and AI systems, offering a set of methodological tools, design concepts, and a research agenda derived from three empirical studies.

AI-Enabled Data-driven Intelligence for Spectrum Demand Estimation

This paper presents an AI-driven, data-driven framework that utilizes validated proxies from site license and crowdsourced data to accurately forecast spectrum demand across five major Canadian cities, thereby enabling more efficient dynamic spectrum planning and resource allocation for regulators and network operators.

ALADIN: Accuracy-Latency-Aware Design-space Inference Analysis for Embedded AI Accelerators

This paper presents ALADIN, an accuracy-latency-aware framework that enables the pre-deployment evaluation of mixed-precision quantized neural networks on scratchpad-based embedded AI accelerators by transforming models into platform-aware representations to analyze trade-offs and bottlenecks without requiring physical hardware.

ARKV: Adaptive and Resource-Efficient KV Cache Management under Limited Memory Budget for Long-Context Inference in LLMs

ARKV is a lightweight, adaptive framework that dynamically allocates precision levels to KV cache tokens based on per-layer attention dynamics and token importance, achieving a 4x reduction in memory usage while preserving ~97% of baseline accuracy for long-context LLM inference without requiring retraining or architectural modifications.

Abundant Intelligence and Deficient Demand: A Macro-Financial Stress Test of Rapid AI Adoption

This paper formalizes a macro-financial stress test arguing that rapid AI adoption creates a distribution-and-contract mismatch where AI-driven abundance fails to generate sufficient demand because economic institutions remain anchored to human labor scarcity, triggering a self-reinforcing cycle of income displacement, declining monetary velocity, and intermediary collapse that poses disproportionate risks to private credit and mortgage markets.

ActiveUltraFeedback: Efficient Preference Data Generation using Active Learning

The paper introduces ActiveUltraFeedback, an efficient active learning pipeline that leverages uncertainty estimates and novel selection strategies like Double Reverse Thompson Sampling to generate high-quality preference data, enabling Large Language Models to achieve superior alignment performance with as little as one-sixth of the annotated data required by static baselines.

Adaptive Clinical-Aware Latent Diffusion for Multimodal Brain Image Generation and Missing Modality Imputation

The paper introduces ACADiff, an adaptive clinical-aware latent diffusion framework that synthesizes missing multimodal brain imaging data (sMRI, FDG-PET, and AV45-PET) by integrating imaging observations with GPT-4o-encoded clinical metadata, achieving superior generation quality and robust diagnostic performance even when up to 80% of modalities are missing.

AgentOS: From Application Silos to a Natural Language-Driven Data Ecosystem

This paper proposes "AgentOS," a new paradigm that replaces traditional GUI-based operating systems with a natural language-driven ecosystem centered on an Agent Kernel, framing the realization of such a system as a Knowledge Discovery and Data Mining (KDD) challenge involving intent mining, workflow automation, and dynamic personal knowledge graphs.

Alignment Is the Disease: Censorship Visibility and Alignment Constraint Complexity as Determinants of Collective Pathology in Multi-Agent LLM Systems

This paper presents preliminary evidence from multi-agent simulations suggesting that alignment techniques and invisible censorship in large language models may paradoxically induce collective pathological behaviors and insight-action dissociation, indicating that safety interventions can sometimes cause the very harms they aim to prevent.

AlphaApollo: A System for Deep Agentic Reasoning

AlphaApollo is an agentic reasoning system that enhances foundation models' performance on complex, long-horizon tasks by orchestrating multi-turn agentic reasoning, turn-level reinforcement learning for tool-use optimization, and a propose-judge-update evolution loop with verification.

An AI-powered Bayesian Generative Modeling Approach for Arbitrary Conditional Inference

This paper introduces Bayesian Generative Modeling (BGM), a unified framework that leverages a stochastic iterative Bayesian updating algorithm to learn a single generative model capable of performing arbitrary conditional inference with principled uncertainty quantification, without requiring retraining for different conditioning structures.

An Empirical Study and Theoretical Explanation on Task-Level Model-Merging Collapse

This paper identifies and theoretically explains "task-level merging collapse," a phenomenon where incompatible task representations cause catastrophic performance degradation in merged LLMs, demonstrating that representational incompatibility—not parameter-space conflicts—is the primary driver of failure and establishing fundamental limits on task mergeability via rate-distortion theory.

Arbiter: Detecting Interference in LLM Agent System Prompts

This paper introduces Arbiter, a framework that combines formal rules with multi-model LLM analysis to detect interference patterns in coding agent system prompts, revealing that prompt architecture influences failure types and that multi-model evaluation uncovers distinct vulnerabilities missed by single-model approaches.

Architectural Design and Performance Analysis of FPGA based AI Accelerators: A Comprehensive Review

This paper reviews FPGA-based AI accelerators for deep learning, highlighting their advantages over ASICs and GPUs, detailing key hardware optimization techniques such as loop pipelining and quantization, and analyzing state-of-the-art designs to identify challenges for future innovations.

Are Expressive Encoders Necessary for Discrete Graph Generation?

This paper introduces GenGNN, a modular message-passing framework that demonstrates expressive neural backbones like transformers are not strictly necessary for discrete graph generation, as diffusion models using GenGNN achieve competitive validity and superior inference speed on various datasets.

AutoAgent: Evolving Cognition and Elastic Memory Orchestration for Adaptive Agents

AutoAgent is a self-evolving multi-agent framework that integrates evolving cognition, on-the-fly contextual decision-making, and elastic memory orchestration to enable autonomous agents to adaptively learn from experience and make reliable, context-aware decisions in dynamic environments without external retraining.

AutoViVQA: A Large-Scale Automatically Constructed Dataset for Vietnamese Visual Question Answering

This paper introduces AutoViVQA, a large-scale automatically constructed dataset for Vietnamese Visual Question Answering, and evaluates transformer-based multimodal models alongside various automatic metrics to assess their performance and alignment with human judgment in the Vietnamese context.

Automated Tensor-Relational Decomposition for Large-Scale Sparse Tensor Computation

This paper introduces \texttt{EinSum}, a tensor-relational extension of Einstein Summation Notation that automatically rewrites computations to leverage efficient numerical kernels for dense operations while utilizing relational systems to manage large-scale sparsity.

Automatic Cardiac Risk Management Classification using large-context Electronic Patients Health Records

This study demonstrates that a custom Transformer architecture outperforms both traditional machine learning models and zero-shot generative LLMs in automatically classifying cardiac risk from large-context, unstructured Dutch electronic health records, offering a robust alternative to manual administrative coding for geriatric cardiovascular risk management.

Automating Detection and Root-Cause Analysis of Flaky Tests in Quantum Software

This paper presents an automated pipeline leveraging Large Language Models to detect and diagnose flaky tests in quantum software, successfully expanding an existing dataset by 54% and demonstrating that models like Google Gemini can achieve high accuracy (F1-scores up to 0.9643) in classifying flakiness and identifying root causes.

Automating Forecasting Question Generation and Resolution for AI Evaluation

This paper presents an automated system using LLM-powered web research agents to generate and resolve diverse, real-world forecasting questions at scale, demonstrating high-quality question creation and resolution rates that surpass human-curated platforms while effectively evaluating and improving AI forecasting performance.

Autonomous Edge-Deployed AI Agents for Electric Vehicle Charging Infrastructure Management

This paper introduces Auralink SDC, an edge-deployed multi-agent AI architecture that autonomously manages electric vehicle charging infrastructure with high reliability and sub-50ms latency, achieving 78% autonomous incident resolution and 87.6% diagnostic accuracy to address the critical failure rates and slow remediation times of current cloud-centric systems.

B-DENSE: Branching For Dense Ensemble Network Supervision Efficiency

The paper proposes B-DENSE, a novel distillation framework that leverages multi-branch trajectory alignment to enforce dense intermediate supervision, thereby overcoming the structural information loss and discretization errors of existing methods to achieve superior image generation quality with reduced inference latency.

BEACON: Language-Conditioned Navigation Affordance Prediction under Occlusion

This paper introduces BEACON, a language-conditioned navigation system that overcomes the limitations of existing 2D image-space methods by predicting an occlusion-aware Bird's-Eye View affordance heatmap from surround-view RGB-D observations, thereby significantly improving the accuracy of inferring traversable targets in occluded regions.

Benchmarking Federated Learning in Edge Computing Environments: A Systematic Review and Performance Evaluation

This paper presents a systematic review and performance evaluation of Federated Learning in edge computing, benchmarking five leading algorithms across key metrics to identify trade-offs, highlight SCAFFOLD's superior accuracy and robustness versus FedAvg's efficiency, and propose a future research agenda to address challenges like data heterogeneity and energy limitations.

Beyond Relevance: On the Relationship Between Retrieval and RAG Information Coverage

This paper empirically demonstrates that coverage-based retrieval metrics serve as reliable early indicators of information coverage in RAG-generated responses, particularly when retrieval objectives align with generation goals, across diverse text and multimodal benchmarks.

Beyond Scaling: Assessing Strategic Reasoning and Rapid Decision-Making Capability of LLMs in Zero-sum Environments

This paper introduces the Strategic Tactical Agent Reasoning (STAR) benchmark, a multi-agent framework for evaluating LLMs in zero-sum environments, which reveals a critical trade-off where reasoning-intensive models excel in turn-based settings but often underperform in real-time scenarios due to latency, highlighting the need to balance strategic depth with rapid execution.

BiCLIP: Domain Canonicalization via Structured Geometric Transformation

The paper introduces BiCLIP, a simple and parameter-efficient framework that achieves state-of-the-art few-shot domain adaptation for vision-language models by applying a structured geometric transformation to align multimodal features across disparate domains using a small set of anchor samples.

Boltzmann-based Exploration for Robust Decentralized Multi-Agent Planning (Extended Version)

This paper introduces Coordinated Boltzmann MCTS (CB-MCTS), a novel decentralized multi-agent planning algorithm that replaces deterministic UCT with a stochastic Boltzmann policy and decaying entropy bonus to overcome the limitations of existing methods in sparse or deceptive reward environments.

Breaking the Factorization Barrier in Diffusion Language Models

The paper introduces Coupled Discrete Diffusion (CoDD), a hybrid framework that overcomes the "factorization barrier" in diffusion language models by replacing fully factorized outputs with a lightweight probabilistic inference layer, thereby enabling efficient parallel generation of coherent, high-quality text without the prohibitive costs of full joint modeling or reinforcement learning.

BridgeDiff: Bridging Human Observations and Flat-Garment Synthesis for Virtual Try-Off

BridgeDiff is a diffusion-based framework that improves virtual try-off by bridging the gap between on-body observations and flat-garment synthesis through a Garment Condition Bridge Module for robust appearance inference and a Flat Structure Constraint Module for enhanced structural stability.

CERES: A Probabilistic Early Warning System for Acute Food Insecurity

CERES is a novel, open-access, and continuously running probabilistic forecasting system that fuses six global data streams to generate weekly, machine-readable 90-day ahead risk estimates for acute food insecurity across 43 high-risk countries, with a unique commitment to public prospective verification of all predictions.

CLEAR-Mamba:Towards Accurate, Adaptive and Trustworthy Multi-Sequence Ophthalmic Angiography Classification

The paper introduces CLEAR-Mamba, an enhanced MedMamba framework featuring a hypernetwork-based adaptive conditioning layer and a reliability-aware prediction scheme, which achieves superior accuracy and trustworthiness in multi-sequence ophthalmic angiography classification by addressing challenges in generalization and confidence estimation.

CLoE: Expert Consistency Learning for Missing Modality Segmentation

The paper proposes CLoE, a consistency-driven framework that enhances missing-modality medical image segmentation by enforcing decision-level agreement among modality experts on both global and clinically critical foreground regions, thereby improving robustness and generalization compared to state-of-the-art methods.

CRANE: Causal Relevance Analysis of Language-Specific Neurons in Multilingual Large Language Models

This paper introduces CRANE, a causal relevance analysis framework that identifies language-specific neurons in multilingual LLMs through targeted interventions, demonstrating that these neurons functionally specialize in language-conditioned predictions rather than merely exhibiting high activation magnitudes.

Causally Sufficient and Necessary Feature Expansion for Class-Incremental Learning

This paper proposes a Probability of Necessity and Sufficiency (PNS)-based regularization method for Class-Incremental Learning that utilizes a dual-scope counterfactual generator to mitigate feature collisions caused by intra-task shortcut reliance and inter-task semantic confusion, thereby ensuring both the causal completeness and separability of task-specific representations.

Chaotic Dynamics in Multi-LLM Deliberation

This paper models multi-LLM deliberation committees as random dynamical systems and demonstrates that even under deterministic temperature settings, factors like role differentiation and model heterogeneity induce chaotic instability characterized by positive empirical Lyapunov exponents, thereby establishing stability auditing as a critical requirement for reliable AI governance.

CktEvo: Repository-Level RTL Code Benchmark for Design Evolution

This paper introduces CktEvo, a repository-level benchmark and closed-loop framework that enables large language models to iteratively optimize Power, Performance, and Area (PPA) in complete RTL designs by preserving functional behavior across cross-file dependencies without human intervention.

Clear, Compelling Arguments: Rethinking the Foundations of Frontier AI Safety Cases

This paper critiques the current limitations of alignment-focused safety cases for frontier AI by drawing on established methodologies from safety-critical industries to propose a more robust, defensible framework, illustrated through a case study on deceptive alignment and CBRN capabilities.

Cognitively Layered Data Synthesis for Domain Adaptation of LLMs to Space Situational Awareness

This paper introduces BD-FDG, a framework that leverages Bloom's Taxonomy and structured knowledge organization to generate a high-quality, cognitively layered dataset for fine-tuning LLMs, successfully adapting the Qwen3-8B model to the complex domain of Space Situational Awareness with significant performance gains while preserving general capabilities.

Common Sense vs. Morality: The Curious Case of Narrative Focus Bias in LLMs

This paper introduces the CoMoral benchmark to reveal that current Large Language Models exhibit a critical limitation where they prioritize moral reasoning over commonsense understanding, particularly struggling to detect contradictions involving primary characters due to a pervasive narrative focus bias.

Compiler-First State Space Duality and Portable $O(1)$ Autoregressive Caching for Inference

This paper demonstrates that Mamba-2's state space duality can be implemented entirely using standard XLA primitives without custom kernels, achieving portable, host-synchronization-free $O(1)$ autoregressive caching with high performance across CPU, NVIDIA GPU, and Google Cloud TPU hardware.

Composed Vision-Language Retrieval for Skin Cancer Case Search via Joint Alignment of Global and Local Representations

This paper proposes a transformer-based framework for skin cancer case retrieval that effectively combines reference images and textual descriptors by learning hierarchical representations and performing joint global-local alignment, thereby achieving state-of-the-art performance on the Derm7pt dataset to support clinical decision-making.

Computational Multi-Agents Society Experiments: Social Modeling Framework Based on Generative Agents

This paper introduces CMASE, a novel framework that integrates generative agent-based modeling with virtual ethnography to transform researchers into embedded participants, enabling real-time social intervention, causal reconstruction of social phenomena, and predictive modeling with high empirical accuracy.

ConLID: Supervised Contrastive Learning for Low-Resource Language Identification

The paper proposes ConLID, a supervised contrastive learning approach that learns domain-invariant representations to significantly improve language identification performance for low-resource languages on out-of-domain data while maintaining accuracy for high-resource languages.

Context Engineering: From Prompts to Corporate Multi-Agent Architecture

This paper proposes "Context Engineering" as a foundational discipline that, alongside Intent and Specification Engineering, forms a maturity model for scaling autonomous multi-agent systems by shifting focus from individual prompts to the comprehensive management of an agent's informational environment, goals, and policy constraints.

Continual uncertainty learning

This paper proposes a curriculum-based continual learning framework that decomposes complex robust control problems with multiple uncertainties into sequential tasks, combining a model-based controller with deep reinforcement learning to achieve efficient, non-forgetting policy updates and successful sim-to-real transfer for automotive powertrain vibration control.

Cooperative Game-Theoretic Credit Assignment for Multi-Agent Policy Gradients via the Core

This paper proposes CORA, a cooperative game-theoretic credit assignment method that utilizes core allocation and coalition sampling to effectively distribute global advantages among agents in multi-agent reinforcement learning, thereby overcoming the limitations of uniform sharing and enhancing coordinated optimal behavior.

Correction of Transformer-Based Models with Smoothing Pseudo-Projector

This paper introduces the smoothing pseudo-projector, a lightweight, multigrid-inspired module that corrects hidden representations in transformer-based models to suppress noise from label-irrelevant inputs, thereby improving training dynamics and robustness without altering the core architecture.

Cross-Domain Uncertainty Quantification for Selective Prediction: A Comprehensive Bound Ablation with Transfer-Informed Betting

This paper introduces Transfer-Informed Betting (TIB), a novel method that combines betting-based confidence sequences with cross-domain transfer learning to achieve tighter, data-efficient risk guarantees for selective prediction, demonstrating significant coverage improvements over existing bounds across multiple benchmarks and applications.

Curveball Steering: The Right Direction To Steer Isn't Always Linear

This paper challenges the Linear Representation Hypothesis by demonstrating that LLM activation spaces exhibit significant geometric distortion, leading to the proposal of "Curveball steering," a nonlinear intervention method using polynomial kernel PCA that outperforms traditional linear approaches by better respecting the intrinsic geometry of the model's feature space.

DRUPI: Dataset Reduction Using Privileged Information

The paper introduces DRUPI (Dataset Condensation using Privileged Information), a framework that enhances dataset condensation by synthesizing auxiliary privileged information, such as feature or attention labels, alongside reduced data to significantly improve model training performance across various benchmarks.

Daily-Omni: Towards Audio-Visual Reasoning with Temporal Alignment across Modalities

The paper introduces Daily-Omni, a comprehensive audio-visual benchmark with 1,197 questions designed to evaluate cross-modal temporal reasoning, revealing that current multimodal large language models still struggle with alignment-critical tasks despite strong unimodal performance.

DataFactory: Collaborative Multi-Agent Framework for Advanced Table Question Answering

This paper introduces DataFactory, a collaborative multi-agent framework that overcomes the context, hallucination, and reasoning limitations of existing TableQA systems by orchestrating specialized agents for structured and relational reasoning, thereby achieving significant accuracy improvements across multiple benchmarks.

Debiasing International Attitudes: LLM Agents for Simulating US-China Perception Changes

This study introduces an LLM-agent framework to simulate U.S. citizens' attitudes toward China from 2005 to 2025, demonstrating that while subjective news framing has a modest impact on negative attitudes, a "devil's advocate" agent is the most effective mechanism for debiasing opinions and producing more human-like cognitive outcomes.

Declarative Scenario-based Testing with RoadLogic

This paper introduces RoadLogic, an open-source framework that bridges declarative OpenSCENARIO specifications and executable simulations by combining Answer Set Programming, motion planning, and specification-based monitoring to automatically generate diverse, realistic, and compliant autonomous vehicle testing scenarios.

Deep Tabular Research via Continual Experience-Driven Execution

This paper introduces a novel agentic framework for Deep Tabular Research (DTR) that addresses the challenges of complex, unstructured tables by formalizing tabular reasoning as a closed-loop decision-making process, utilizing hierarchical meta-graphs for path planning, expectation-aware selection policies, and a siamese structured memory for continual experience-driven refinement.

Democratising Clinical AI through Dataset Condensation for Classical Clinical Models

This paper introduces a differentially private, zero-order optimization framework that extends dataset condensation to non-differentiable clinical models, enabling the creation of compact, privacy-preserving synthetic datasets that facilitate the democratization of clinical data sharing without compromising model utility.

DendroNN: Dendrocentric Neural Networks for Energy-Efficient Classification of Event-Based Data

This paper introduces DendroNN, a novel dendrocentric neural network that leverages non-differentiable sequence detection and a rewiring phase to efficiently classify event-based spatiotemporal data, achieving competitive accuracy with up to 4x higher energy efficiency than state-of-the-art neuromorphic hardware through a dedicated asynchronous digital architecture.

DenoiseSplat: Feed-Forward Gaussian Splatting for Noisy 3D Scene Reconstruction

The paper proposes DenoiseSplat, a feed-forward 3D Gaussian splatting method that achieves robust novel-view synthesis from noisy multi-view images by training end-to-end on a newly constructed noisy-clean benchmark without requiring 3D ground truth.

Design Conductor: An agent autonomously builds a 1.5 GHz Linux-capable RISC-V CPU

The paper introduces Design Conductor, an autonomous agent that leverages frontier models to independently design, verify, and generate a tape-out ready 1.48 GHz RISC-V CPU (VerCore) from a text specification to GDSII layout in just 12 hours, marking the first instance of an agent building a complete, working CPU end-to-end.

DexHiL: A Human-in-the-Loop Framework for Vision-Language-Action Model Post-Training in Dexterous Manipulation

DexHiL is the first integrated human-in-the-loop framework for dexterous Vision-Language-Action models that combines coordinated arm-hand teleoperation with intervention-aware data sampling to significantly improve post-training performance and reliability in complex manipulation tasks.

Diagnosing FP4 inference: a layer-wise and block-wise sensitivity analysis of NVFP4 and MXFP4

This paper presents a systematic layer-wise and block-wise sensitivity analysis of NVFP4 and MXFP4 quantization across three Qwen2.5 model scales, revealing that MLP up- and down-projection layers are the most sensitive components while sensitivity patterns vary by format and model depth rather than being confined to final blocks.

Differentiable Stochastic Traffic Dynamics: Physics-Informed Generative Modelling in Transportation

This paper proposes a physics-informed generative modeling framework that derives a differentiable, distributional traffic dynamics model from stochastic Ito-type equations, enabling the estimation of traffic density distributions, credible intervals, and congestion risks through a score network trained with denoising score matching and Fokker-Planck residual loss.

Does the Question Really Matter? Training-Free Data Selection for Vision-Language SFT

This paper introduces CVS, a training-free data selection method that identifies high-quality vision-language samples by measuring how much a question alters a frozen model's assessment of answer validity, thereby improving multimodal reasoning performance while reducing computational costs.

DuplexCascade: Full-Duplex Speech-to-Speech Dialogue with VAD-Free Cascaded ASR-LLM-TTS Pipeline and Micro-Turn Optimization

DuplexCascade is a VAD-free, cascaded ASR-LLM-TTS pipeline that enables full-duplex speech-to-speech dialogue by converting long turns into micro-turns and utilizing special control tokens to coordinate turn-taking while preserving the conversational intelligence of large language models.

EDMFormer: Genre-Specific Self-Supervised Learning for Music Structure Segmentation

The paper introduces EDMFormer, a transformer model trained on a newly released dataset of 98 professionally annotated EDM tracks (EDM-98) to address the limitations of existing music segmentation methods by leveraging genre-specific energy, rhythm, and timbre features for improved structure detection in Electronic Dance Music.

EMFusion: Conditional Diffusion Framework for Trustworthy Frequency Selective EMF Forecasting in Wireless Networks

This paper introduces EMFusion, a conditional multivariate diffusion-based framework that leverages a residual U-Net with cross-attention and imputation-based sampling to provide accurate, uncertainty-quantified, frequency-selective electromagnetic field forecasts for wireless network planning, significantly outperforming existing baseline models.

EPOCH: An Agentic Protocol for Multi-Round System Optimization

The paper introduces EPOCH, a unified engineering protocol that structures multi-round autonomous system optimization into baseline construction and iterative self-improvement phases with role-constrained stages to ensure stability, reproducibility, and traceability across heterogeneous environments.

ESAinsTOD: A Unified End-to-End Schema-Aware Instruction-Tuning Framework for Task-Oriented Dialog Modeling

The paper proposes ESAinsTOD, a unified end-to-end schema-aware instruction-tuning framework that leverages full-parameter LLM fine-tuning with instruction and schema alignment mechanisms to achieve superior performance, generalization in low-resource settings, and robustness against noise across diverse task-oriented dialog benchmarks.

EXPLORE-Bench: Egocentric Scene Prediction with Long-Horizon Reasoning

This paper introduces EXPLORE-Bench, a benchmark derived from real first-person videos to evaluate the ability of multimodal large language models to perform long-horizon egocentric scene prediction, revealing significant performance gaps compared to humans and demonstrating that stepwise reasoning offers partial improvements at a computational cost.

Efficiently Aligning Draft Models via Parameter- and Data-Efficient Adaptation

This paper introduces Efficient Draft Adaptation (EDA), a parameter- and data-efficient framework that restores speculative decoding performance on fine-tuned target models through a decoupled architecture, data regeneration strategy, and sample selection mechanism, achieving superior acceptance lengths with significantly reduced training costs compared to full retraining.

Ego: Embedding-Guided Personalization of Vision-Language Models

The paper proposes "Ego," an efficient personalization method for vision-language models that extracts visual tokens representing target concepts via internal attention mechanisms to serve as memory, enabling strong performance across single-concept, multi-concept, and video personalization tasks without requiring additional training stages or external modules.

EgoCross: Benchmarking Multimodal Large Language Models for Cross-Domain Egocentric Video Question Answering

This paper introduces EgoCross, a comprehensive benchmark comprising 1,000 QA pairs across four challenging domains (surgery, industry, extreme sports, and animal perspective) to evaluate and expose the poor cross-domain generalization capabilities of current Multimodal Large Language Models in egocentric video question answering.

Embodied Human Simulation for Quantitative Design and Analysis of Interactive Robotics

This paper presents a scalable, reinforcement learning-driven simulation framework featuring a full-body musculoskeletal model that enables the quantitative co-optimization of robotic structural design and control policies by providing direct access to internal human biomechanical metrics for interactive robotics.

Emerging Extrinsic Dexterity in Cluttered Scenes via Dynamics-aware Policy Learning

This paper introduces the Dynamics-Aware Policy Learning (DAPL) framework, which leverages explicit world modeling to learn contact-induced dynamics, enabling robots to achieve robust extrinsic dexterity in cluttered environments without hand-crafted heuristics and significantly outperforming existing manipulation methods in both simulation and real-world deployments.

Emotion is Not Just a Label: Latent Emotional Factors in LLM Processing

This paper investigates emotion as a latent factor influencing LLM attention and reasoning, introducing the AURA-QA dataset and an emotional regularization framework that demonstrably improves reading comprehension performance across both emotionally varying and standard benchmarks.

Emotional Modulation in Swarm Decision Dynamics

This paper extends the classical "bee equation" for swarm decision-making into an agent-based model where emotional valence and arousal modulate interaction rates, revealing how emotional contagion and non-linear amplification mechanisms bias consensus outcomes and accelerate convergence in collective choices.

Empowering All-in-Loop Health Management of Spacecraft Power System in the Mega-Constellation Era via Human-AI Collaboration

This paper addresses the challenges of health management for spacecraft power systems in the emerging mega-constellation era by proposing the "Aligning Underlying Capabilities" principle and introducing SpaceHMchat, an open-source Human-AI collaboration framework validated on a realistic hardware platform and a new large-scale dataset to achieve high-precision, interpretable, and efficient all-in-loop health management.

Energy-Aware Spike Budgeting for Continual Learning in Spiking Neural Networks for Neuromorphic Vision

This paper proposes an energy-aware spike budgeting framework that integrates experience replay, learnable neuron parameters, and an adaptive scheduler to effectively mitigate catastrophic forgetting while optimizing both accuracy and energy efficiency in Spiking Neural Networks across diverse frame-based and event-based neuromorphic vision benchmarks.

Enhancing Debunking Effectiveness through LLM-based Personality Adaptation

This study proposes and evaluates a novel methodology for enhancing fake news debunking by using Large Language Models to generate personalized messages tailored to Big Five personality traits, demonstrating that such targeted approaches generally increase persuasiveness while highlighting both the potential and ethical implications of automated, personality-driven disinformation correction.

Enhancing Heterogeneous Multi-Agent Cooperation in Decentralized MARL via GNN-driven Intrinsic Rewards

This paper proposes CoHet, a novel algorithm that leverages Graph Neural Network-driven intrinsic rewards to enable effective decentralized learning and cooperation among heterogeneous multi-agent systems despite challenges like partial observability and reward sparsity, demonstrating superior performance over state-of-the-art methods in standard benchmarks.

Enhancing Retrieval-Augmented Generation with Entity Linking for Educational Platforms

This paper introduces ELERAG, an enhanced Retrieval-Augmented Generation system that integrates Wikidata-based Entity Linking and a hybrid re-ranking strategy to significantly improve factual accuracy in Italian educational question-answering, particularly outperforming standard methods in domain-specific contexts while demonstrating the importance of domain-adapted strategies.

EsoLang-Bench: Evaluating Genuine Reasoning in Large Language Models via Esoteric Programming Languages

The paper introduces EsoLang-Bench, a novel benchmark utilizing esoteric programming languages to expose the limitations of large language models' genuine reasoning capabilities by revealing a dramatic performance gap between their high scores on standard benchmarks and near-zero accuracy on tasks requiring the acquisition of new languages through documentation and experimentation rather than memorization.

Evaluate-as-Action: Self-Evaluated Process Rewards for Retrieval-Augmented Agents

The paper proposes \textsc{EvalAct}, a framework that converts implicit retrieval quality assessment into an explicit action followed by a structured evaluation score, and leverages these process signals via a novel Process-Calibrated Advantage Rescaling (PCAR) method to significantly improve the reliability and accuracy of retrieval-augmented agents in multi-step reasoning tasks.

EvoDriveVLA: Evolving Autonomous Driving Vision-Language-Action Model via Collaborative Perception-Planning Distillation

EvoDriveVLA is a novel Vision-Language-Action model for autonomous driving that overcomes perception degradation and planning instability through a collaborative distillation framework combining self-anchored visual constraints and oracle-guided trajectory optimization to achieve state-of-the-art performance.

Evolving Prompt Adaptation for Vision-Language Models

The paper proposes EvoPrompt, a novel framework that achieves stable, knowledge-preserving adaptation of vision-language models in few-shot scenarios by employing a modality-shared projector and an evolutionary training strategy that decouples prompt updates into directional and magnitude components to prevent catastrophic forgetting.

Explainable Innovation Engine: Dual-Tree Agent-RAG with Methods-as-Nodes and Verifiable Write-Back

This paper presents an Explainable Innovation Engine that enhances Retrieval-Augmented Generation by replacing flat text chunks with a dual-tree architecture of methods-as-nodes, enabling an agent to perform verifiable, multi-step synthesis with traceable derivations and continual knowledge growth through expert-guided pruning and write-back.

Exploiting Label-Aware Channel Scoring for Adaptive Channel Pruning in Split Learning

This paper proposes ACP-SL, an adaptive channel pruning scheme for Split Learning that utilizes a label-aware channel importance scoring module to compress smashed data, thereby significantly reducing communication overhead while improving test accuracy and training efficiency.

FedLECC: Cluster- and Loss-Guided Client Selection for Federated Learning under Non-IID Data

FedLECC is a lightweight client selection strategy for federated learning under non-IID data that groups clients by label-distribution similarity and prioritizes those with higher local loss, thereby significantly improving test accuracy while reducing communication rounds and overhead.

FinTexTS: Financial Text-Paired Time-Series Dataset via Semantic-Based and Multi-Level Pairing

The paper introduces FinTexTS, a large-scale financial text-paired time-series dataset constructed via a novel semantic-based and multi-level pairing framework that overcomes the limitations of simple keyword matching by leveraging LLMs to align news articles with stock prices across macro, sector, related company, and target-company levels, thereby significantly improving stock price forecasting performance.

Fish Audio S2 Technical Report

This paper introduces Fish Audio S2, an open-source text-to-speech system that leverages a multi-stage training pipeline to enable multi-speaker, multi-turn generation with natural-language instruction following, while providing production-ready weights and an efficient SGLang-based inference engine.

From Data Statistics to Feature Geometry: How Correlations Shape Superposition

This paper challenges the standard view of superposition in neural networks by demonstrating that, unlike in idealized uncorrelated settings where interference is merely noise, realistic feature correlations allow models to arrange features so that interference becomes constructive, thereby naturally forming the semantic clusters and cyclical structures observed in real language models.

From Days to Minutes: An Autonomous AI Agent Achieves Reliable Clinical Triage in Remote Patient Monitoring

The paper introduces Sentinel, an autonomous AI agent that achieves reliable, scalable clinical triage for remote patient monitoring by outperforming individual clinicians in sensitivity and consistency while maintaining a clinically defensible overtriage profile at a negligible cost.

From Flow to One Step: Real-Time Multi-Modal Trajectory Policies via Implicit Maximum Likelihood Estimation-based Distribution Distillation

This paper proposes a real-time multi-modal trajectory policy framework that distills a Conditional Flow Matching expert into a single-step student using Implicit Maximum Likelihood Estimation and a bi-directional Chamfer distance, thereby eliminating the latency of iterative ODE integration while preserving multi-modal action diversity for high-frequency robotic control.

From Self-Evolving Synthetic Data to Verifiable-Reward RL: Post-Training Multi-turn Interactive Tool-Using Agents

This paper introduces EigenData, a unified framework that combines a self-evolving multi-agent system for synthesizing verifiable tool-use dialogues with a verifier-based reinforcement learning recipe, enabling scalable post-training of interactive agents that achieve state-of-the-art performance on complex multi-turn benchmarks without relying on expensive human annotation.

From Spatial to Actions: Grounding Vision-Language-Action Model in Spatial Foundation Priors

FALCON addresses the spatial reasoning limitations of existing 2D-based vision-language-action models by leveraging spatial foundation models to inject rich 3D geometric priors directly into the action head, achieving state-of-the-art performance across diverse simulation and real-world tasks without requiring architectural changes or specialized sensors.

GIAT: A Geologically-Informed Attention Transformer for Lithology Identification

The paper proposes GIAT, a novel Geologically-Informed Attention Transformer that integrates Category-Wise Sequence Correlation filters into the self-attention mechanism to guide lithology identification with geological priors, achieving state-of-the-art accuracy and enhanced interpretability on well log datasets.

GNNs for Time Series Anomaly Detection: An Open-Source Framework and a Critical Evaluation

This paper introduces an open-source framework for Graph Neural Network-based Time Series Anomaly Detection to enable reproducible experimentation and critical evaluation, demonstrating that GNNs enhance both detection performance and interpretability while highlighting the need for standardized metrics and thresholding strategies.

GST-VLA: Structured Gaussian Spatial Tokens for 3D Depth-Aware Vision-Language-Action Models

GST-VLA introduces a novel framework that enhances Vision-Language-Action models by converting visual observations into anisotropic 3D Gaussian spatial tokens and employing 3D Depth-Aware Chain-of-Thought reasoning to achieve state-of-the-art performance on precision-demanding robotic manipulation tasks.

GateLens: A Reasoning-Enhanced LLM Agent for Automotive Software Release Analytics

GateLens is a reasoning-enhanced LLM agent that utilizes Relational Algebra as a formal intermediate representation to bridge the gap between natural language and executable code, enabling fast, transparent, and highly accurate analysis of complex tabular data in automotive software release analytics without requiring few-shot examples or complex agent orchestration.

Gender Fairness in Audio Deepfake Detection: Performance and Disparity Analysis

This paper analyzes gender bias in audio deepfake detection using the ASVspoof 5 dataset and a ResNet-18 classifier, demonstrating that while aggregate metrics like Equal Error Rate may suggest low disparity, fairness-aware evaluation reveals significant gender-specific error distributions that necessitate more equitable and robust detection systems.

GenePlan: Evolving Better Generalized PDDL Plans using Large Language Models

GenePlan is a novel framework that leverages large language models to evolve domain-dependent generalized planners in Python, achieving state-of-the-art performance on PDDL benchmarks while significantly outperforming other LLM-based approaches in both accuracy and efficiency.

Generalized Reduction to the Isotropy for Flexible Equivariant Neural Fields

This paper introduces a principled method to reduce $G$-invariant functions on product spaces $X \times M$ to $H$-invariant functions on $X$ alone, where $H$ is the isotropy subgroup of $M$, thereby enabling flexible Equivariant Neural Fields to handle arbitrary group actions and heterogeneous product spaces without structural constraints.

GraphKeeper: Graph Domain-Incremental Learning via Knowledge Disentanglement and Preservation

The paper proposes GraphKeeper, a novel framework for Graph Domain-Incremental Learning that addresses catastrophic forgetting through knowledge disentanglement and deviation-free preservation, achieving state-of-the-art performance across multiple graph domains while remaining compatible with various graph foundation models.

Grounding Synthetic Data Generation With Vision and Language Models

This paper proposes a vision-language grounded framework for interpretable synthetic data generation and evaluation in remote sensing, introducing the ARAS400k dataset which demonstrates that augmenting real data with synthetic images consistently outperforms real-data-only baselines in semantic segmentation and image captioning tasks.

Hindsight Credit Assignment for Long-Horizon LLM Agents

The paper introduces HCAPO, a novel framework that enhances long-horizon LLM agents by leveraging hindsight reasoning to refine step-level Q-values and employing a multi-scale advantage mechanism to address sparse reward challenges, thereby significantly outperforming state-of-the-art methods like GRPO on benchmarks such as WebShop and ALFWorld.

HyConEx: Hypernetwork classifier with counterfactual explanations for tabular data

The paper introduces HyConEx, a novel deep hypernetwork-based classifier for tabular data that uniquely integrates prediction and explanation by simultaneously generating class labels and local counterfactual examples to interpret model decisions.

ICDAR 2025 Competition on End-to-End Document Image Machine Translation Towards Complex Layouts

This paper presents the ICDAR 2025 competition on end-to-end document image machine translation, detailing its dual-track structure for small and large models, participation statistics, and findings that highlight large-model approaches as a promising paradigm for handling complex document layouts.

Improving through Interaction: Searching Behavioral Representation Spaces with CMA-ES-IG

This paper introduces CMA-ES-IG, an algorithm that enhances robot preference learning by generating perceptually distinct and informative queries, thereby improving scalability, robustness, and user experience compared to existing state-of-the-art methods.

Influencing LLM Multi-Agent Dialogue via Policy-Parameterized Prompts

This paper proposes a lightweight, training-free framework that parameterizes prompts as actions to dynamically influence LLM multi-agent dialogue behaviors, demonstrating through experiments that this policy-based approach effectively controls conversational dynamics across various indicators.

Infusion: Shaping Model Behavior by Editing Training Data via Influence Functions

The paper introduces "Infusion," a framework that leverages scalable influence-function approximations to compute subtle perturbations in training data, demonstrating that modifying as little as 0.2% of a dataset can effectively and transferably shape model behavior across vision and language domains.

Interpretable Markov-Based Spatiotemporal Risk Surfaces for Missing-Child Search Planning with Reinforcement Learning and LLM-Based Quality Assurance

The paper presents "Guardian," an interpretable, three-layer decision-support system that combines Markov chains, reinforcement learning, and LLM-based validation to generate dynamic, probabilistic search plans for missing-child investigations within the critical first 72 hours.

Investigating Gender Stereotypes in Large Language Models via Social Determinants of Health

This study investigates gender bias in Large Language Models within French healthcare contexts, demonstrating that these models rely on embedded stereotypes when processing interactions between gender and other social determinants of health, thereby highlighting the need for context-specific assessments that go beyond evaluating individual factors in isolation.

LCA: Local Classifier Alignment for Continual Learning

This paper proposes Local Classifier Alignment (LCA), a novel loss function that resolves the mismatch between task-specific classifiers and adapted backbones in continual learning, thereby enhancing generalization and robustness while achieving state-of-the-art performance on standard benchmarks.

LDP: An Identity-Aware Protocol for Multi-Agent LLM Systems

This paper introduces the LLM Delegate Protocol (LDP), an AI-native communication framework that enhances multi-agent system efficiency and governance by exposing model identity and reasoning profiles as first-class primitives, demonstrating significant reductions in latency and token usage alongside improved security and recovery capabilities in experimental evaluations.

LLM-Advisor: An LLM Benchmark for Cost-efficient Path Planning across Multiple Terrains

The paper introduces LLM-Advisor, a prompt-based framework that leverages large language models as non-decisive post-processing advisors to significantly improve the cost efficiency of path planning across diverse terrains without modifying underlying planners, while addressing hallucination risks and demonstrating superior performance over zero-shot LLM approaches.

Latent Policy Steering with Embodiment-Agnostic Pretrained World Models

This paper introduces Latent Policy Steering (LPS), a method that leverages embodiment-agnostic optical flow to pretrain a World Model on diverse datasets, which is then fine-tuned with limited target-embodiment demonstrations to steer and significantly improve visuomotor policies in low-data regimes.

Latent Speech-Text Transformer

The Latent Speech-Text Transformer (LST) improves the efficiency and performance of auto-regressive speech-text models by aggregating speech tokens into latent patches, which aligns sequence granularity with text, reduces computational costs, and achieves significant accuracy gains across speech and text benchmarks.

Latent World Models for Automated Driving: A Unified Taxonomy, Evaluation Framework, and Open Challenges

This paper proposes a unified taxonomy and evaluation framework for latent world models in automated driving, organizing design choices by latent representations and structural priors while identifying key internal mechanics and research directions to enhance robustness, generalization, and deployability.

Latent-DARM: Bridging Discrete Diffusion And Autoregressive Models For Reasoning

Latent-DARM is a novel latent-space communication framework that bridges Discrete Diffusion Language Models for global planning and Autoregressive Models for fluent execution, significantly improving reasoning accuracy on benchmarks like DART-5 and AIME2024 while drastically reducing token usage compared to state-of-the-art reasoning models.

Let's Verify Math Questions Step by Step

This paper introduces MathQ-Verify, a novel five-stage pipeline that rigorously filters ill-posed or under-specified mathematical questions through format validation, formalization, contradiction detection, and completeness checks, achieving state-of-the-art performance in curating reliable datasets for training large language models.

Lightweight Time Series Data Valuation on Time Series Foundation Models via In-Context Finetuning

This paper proposes LTSV, a lightweight and efficient method for valuing time series data in foundation models by leveraging in-context finetuning and temporal block aggregation to overcome the computational bottlenecks and temporal dependency limitations of traditional approaches.

Logics-Parsing-Omni Technical Report

This paper introduces the Omni Parsing framework and the Logics-Parsing-Omni model, which unify document, image, and audio-visual parsing through a three-level hierarchical paradigm of holistic detection, fine-grained recognition, and multi-level interpreting to transform unstructured multimodal signals into traceable, evidence-based structured knowledge.

Logos: An evolvable reasoning engine for rational molecular design

The paper introduces Logos, a compact molecular reasoning model that integrates multi-step logical reasoning with strict chemical consistency through a staged training strategy, achieving high structural accuracy and interpretability while outperforming larger language models in rational molecular design.

M3GCLR: Multi-View Mini-Max Infinite Skeleton-Data Game Contrastive Learning For Skeleton-Based Action Recognition

This paper proposes M3GCLR, a game-theoretic contrastive learning framework that addresses limitations in existing skeleton-based action recognition methods by establishing an Infinite Skeleton-data Game model with a mini-max optimization strategy and dual-loss equilibrium optimizer to effectively handle view discrepancies, adversarial mechanisms, and augmentation perturbations, achieving state-of-the-art performance on multiple benchmarks.

MA-EgoQA: Question Answering over Egocentric Videos from Multiple Embodied Agents

This paper introduces MA-EgoQA, a novel benchmark and dataset featuring 1,700 questions across five categories designed to evaluate the ability of AI models to understand and reason over multiple long-horizon egocentric videos from embodied agents, alongside a proposed baseline model named EgoMAS that highlights current limitations in system-level multi-agent understanding.

MASEval: Extending Multi-Agent Evaluation from Models to Systems

MASEval introduces a framework-agnostic library that shifts multi-agent evaluation from a model-centric to a system-centric approach, demonstrating through extensive experiments that implementation decisions regarding topology and orchestration impact performance as significantly as model selection.

MCGI: Manifold-Consistent Graph Indexing for Billion-Scale Disk-Resident Vector Search

The paper proposes Manifold-Consistent Graph Indexing (MCGI), a geometry-aware, disk-resident indexing method that leverages Local Intrinsic Dimensionality to dynamically adapt search strategies, achieving significantly higher throughput and lower latency than state-of-the-art baselines on billion-scale datasets by resolving the Euclidean-Geodesic mismatch in high-dimensional spaces.

MCP Bridge: A Lightweight, LLM-Agnostic RESTful Proxy for Model Context Protocol Servers

This paper introduces MCP Bridge, a lightweight, LLM-agnostic RESTful proxy that enables Model Context Protocol servers to run in resource-constrained environments with enhanced security, while also presenting a fine-tuned Qwen3 model that achieves state-of-the-art performance on the MCPToolBench++ benchmark through advanced reinforcement learning techniques.

MEMO: Memory-Augmented Model Context Optimization for Robust Multi-Turn Multi-Agent LLM Games

The paper introduces MEMO, a memory-augmented self-play framework that optimizes inference-time context through structured memory retention and uncertainty-aware prompt exploration, significantly improving the win rates and run-to-run stability of multi-agent LLMs in long-horizon, imperfect-information games.

MIL-PF: Multiple Instance Learning on Precomputed Features for Mammography Classification

The paper proposes MIL-PF, a scalable framework that combines frozen foundation model encoders with a lightweight attention-based Multiple Instance Learning head to achieve state-of-the-art mammography classification while significantly reducing computational costs and training complexity.

MITRA: An AI Assistant for Knowledge Retrieval in Physics Collaborations

The paper introduces MITRA, an on-premise Retrieval-Augmented Generation (RAG) system designed to enhance knowledge retrieval in large-scale physics collaborations like CMS by employing a novel automated pipeline for document processing and a two-tiered vector database architecture to accurately answer context-aware questions while ensuring data privacy.

MKE-Coder: Multi-Axial Knowledge with Evidence Verification in ICD Coding for Chinese EMRs

This paper presents MKE-Coder, a novel framework that improves automatic ICD coding for Chinese electronic medical records by integrating multi-axial disease knowledge with a clinical evidence verification module to address challenges in information extraction and code validity.

MM-tau-p$^2$: Persona-Adaptive Prompting for Robust Multi-Modal Agent Evaluation in Dual-Control Settings

This paper introduces MM-tau-p$^2$, a novel benchmark featuring 12 metrics to holistically evaluate the robustness and efficiency of multi-modal LLM agents in dual-control settings, specifically assessing their performance with and without user persona adaptation across domains like telecom and retail.

MMGraphRAG: Bridging Vision and Language with Interpretable Multimodal Knowledge Graphs

MMGraphRAG addresses the limitations of text-centric GraphRAG by introducing a novel framework that integrates visual scene graphs with text knowledge graphs via spectral clustering-based entity linking (SpecLink) and a new CMEL dataset, achieving state-of-the-art performance in multimodal reasoning and reducing hallucinations.

MSSR: Memory-Aware Adaptive Replay for Continual LLM Fine-Tuning

The paper proposes MSSR, a memory-aware adaptive replay framework that estimates sample-level memory strength to dynamically schedule rehearsal intervals, effectively mitigating catastrophic forgetting while maintaining fast adaptation in continual LLM fine-tuning.

MUGEN: Evaluating and Improving Multi-audio Understanding of Large Audio-Language Models

This paper introduces MUGEN, a comprehensive benchmark revealing that Large Audio-Language Models struggle with multi-audio understanding as input scaling increases, and demonstrates that combining training-free strategies like Audio-Permutational Self-Consistency with Chain-of-Thought can significantly improve performance.

MedMASLab: A Unified Orchestration Framework for Benchmarking Multimodal Medical Multi-Agent Systems

MedMASLab is a unified framework and benchmarking platform that addresses architectural fragmentation in medical multi-agent systems by introducing a standardized communication protocol, an automated zero-shot clinical reasoning evaluator, and an extensive multimodal benchmark spanning 473 diseases to reveal critical performance gaps in cross-specialty transitions.

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images

This paper introduces MediRound, a new task and baseline model for multi-round entity-level reasoning segmentation in medical images, supported by the large-scale MR-MedSeg dataset and a Judgment & Correction Mechanism to mitigate error propagation in multi-turn medical dialogues.

Meissa: Multi-modal Medical Agentic Intelligence

Meissa is a lightweight, 4B-parameter offline medical multi-modal agent that achieves state-of-the-art performance across diverse clinical benchmarks by employing novel trajectory modeling and stratified supervision to distill frontier model capabilities, thereby offering a cost-effective, low-latency, and privacy-preserving alternative to API-dependent systems.

MiniAppBench: Evaluating the Shift from Text to Interactive HTML Responses in LLM-Powered Assistants

This paper introduces MiniAppBench, a comprehensive benchmark derived from real-world data to evaluate LLMs' ability to generate principle-driven interactive HTML applications, alongside MiniAppEval, an agentic framework that uses browser automation to assess these applications across intention, static, and dynamic dimensions.

Mitigating Long-Tail Bias in HOI Detection via Adaptive Diversity Cache

This paper proposes the Adaptive Diversity Cache (ADC), a novel training-free and plug-and-play module that mitigates long-tail bias in Human-Object Interaction detection by dynamically accumulating diverse high-confidence features during inference to enhance rare category performance without additional training.

Monocular Normal Estimation via Shading Sequence Estimation

This paper introduces RoSE, a novel approach that reformulates monocular normal estimation as shading sequence estimation using image-to-video generative models to overcome 3D misalignment issues and achieve state-of-the-art performance on real-world benchmarks.

Mousse: Rectifying the Geometry of Muon with Curvature-Aware Preconditioning

Mousse is a novel optimizer that improves upon the Muon algorithm by integrating Shampoo's Kronecker-factored preconditioning to adaptively handle the heavy-tailed curvature of deep neural networks, thereby achieving faster training convergence with negligible computational overhead.

Multi-Agent Reinforcement Learning with Communication-Constrained Priors

This paper proposes a communication-constrained multi-agent reinforcement learning framework that utilizes a generalized model and dual mutual information estimator to distinguish between lossy and lossless messages, thereby quantifying their impact on global rewards to enhance cooperative policy learning in complex, dynamic environments.

Multi-level meta-reinforcement learning with skill-based curriculum

This paper proposes a multi-level meta-reinforcement learning framework that systematically compresses Markov decision processes into hierarchical structures with skill-based curriculum learning to decouple sub-tasks, reduce stochasticity, and enable efficient transfer of skills across different problems and levels.

Multi-model approach for autonomous driving: A comprehensive study on traffic sign-, vehicle- and lane detection and behavioral cloning

This study presents a comprehensive multi-model deep learning approach that integrates pre-trained and custom neural networks with advanced data augmentation and transfer learning techniques to enhance autonomous driving capabilities by effectively addressing traffic sign classification, vehicle and lane detection, and behavioral cloning across diverse datasets.

NavSpace: How Navigation Agents Follow Spatial Intelligence Instructions

This paper introduces the NavSpace benchmark to systematically evaluate the spatial intelligence of navigation agents through six task categories and 1,228 trajectory-instruction pairs, revealing limitations in current models and proposing SNav, a new spatially intelligent navigation model that outperforms existing agents on both the benchmark and real robot tests.

NetDiffuser: Deceiving DNN-Based Network Attack Detection Systems with Diffusion-Generated Adversarial Traffic

This paper introduces NetDiffuser, a novel framework that leverages a feature categorization algorithm and diffusion models to generate natural adversarial examples that effectively deceive deep learning-based network intrusion detection systems while preserving traffic validity.

No Image, No Problem: End-to-End Multi-Task Cardiac Analysis from Undersampled k-Space

The paper proposes k-MTR, a novel framework that bypasses the traditional image reconstruction step by directly learning multi-task cardiac diagnostic features from undersampled k-space data through a shared semantic manifold, thereby eliminating reconstruction artifacts and achieving competitive performance across regression, classification, and segmentation tasks.

Not All News Is Equal: Topic- and Event-Conditional Sentiment from Finetuned LLMs for Aluminum Price Forecasting

This study demonstrates that integrating sentiment scores derived from a finetuned Qwen3 model analyzing English and Chinese news significantly enhances aluminum price forecasting accuracy and economic utility, particularly during periods of high market volatility, compared to traditional tabular data models.

OOD-MMSafe: Advancing MLLM Safety from Harmful Intent to Hidden Consequences

The paper introduces OOD-MMSafe, a benchmark revealing significant causal blindness in current Multimodal Large Language Models regarding hidden consequences, and proposes the Consequence-Aware Safety Policy Optimization (CASPO) framework to effectively mitigate these risks by shifting safety alignment from intent detection to consequence projection.

OPENXRD: A Comprehensive Benchmark Framework for LLM/MLLM XRD Question Answering

The paper introduces OPENXRD, a comprehensive benchmark framework featuring 217 expert-curated X-ray diffraction questions that evaluates how large language and multimodal models assimilate domain-specific context, revealing that mid-sized models benefit most from high-quality reference materials while very large models often exhibit saturation or interference.

On the Impact of the Utility in Semivalue-based Data Valuation

This paper addresses the sensitivity of semivalue-based data valuation to utility selection by introducing a "spatial signature" framework that embeds data points into a lower-dimensional space, enabling a practical methodology to quantify and ensure the robustness of valuation results against utility variations.

On the mechanical creation of mathematical concepts

The paper proposes a model of mathematical problem-solving as a belief-update loop that distinguishes between implicit concept formation, which optimizes search within a fixed vocabulary, and explicit concept creation, which introduces new moves to resolve unsolvable problems and argues that while current AI excels at the former, achieving the latter is essential for machines to replicate the distinctive nature of mathematical discovery.

Open-World Motion Forecasting

This paper introduces "Open-World Motion Forecasting," an end-to-end class-incremental framework that predicts future trajectories directly from camera images while mitigating catastrophic forgetting through pseudo-labeling with vision-language models and a novel query feature variance-based replay strategy, enabling continual adaptation to evolving object taxonomies in real-world autonomous driving.

OrthoAI: A Neurosymbolic Framework for Evidence-Grounded Biomechanical Reasoning in Clear Aligner Orthodontics

OrthoAI is a neurosymbolic framework that bridges 3D tooth segmentation and clinical reasoning for clear aligner orthodontics by combining sparse-supervision learning, knowledge-grounded biomechanical constraint inference, and multi-criteria treatment evaluation to enable fast, evidence-based automated decision support.

PM-Nav: Priori-Map Guided Embodied Navigation in Functional Buildings

The paper introduces PM-Nav, a novel framework that leverages priori-semantic maps and hierarchical chain-of-thought prompting to overcome the challenges of language-driven navigation in functional buildings with highly similar features, achieving substantial performance improvements over existing methods in both simulation and real-world environments.

PRECEPT: Planning Resilience via Experience, Context Engineering & Probing Trajectories A Unified Framework for Test-Time Adaptation with Compositional Rule Learning and Pareto-Guided Prompt Evolution

PRECEPT is a unified test-time adaptation framework that enhances LLM agent resilience by integrating deterministic exact-match rule retrieval, conflict-aware memory with Bayesian reliability, and the Pareto-guided COMPASS prompt-evolution loop to achieve superior compositional generalization, continuous learning, and robustness against knowledge drift and adversarial inputs.

PathMem: Toward Cognition-Aligned Memory Transformation for Pathology MLLMs

PathMem is a memory-centric multimodal framework that enhances pathology large language models by organizing structured domain knowledge into long-term memory and utilizing a Memory Transformer to dynamically activate and ground this knowledge for improved diagnostic reasoning and report generation.

PathoScribe: Transforming Pathology Data into a Living Library with a Unified LLM-Driven Framework for Semantic Retrieval and Clinical Integration

PathoScribe is a unified retrieval-augmented large language model framework that transforms static pathology archives into an active, reasoning-enabled clinical intelligence platform, enabling natural language case retrieval, automated cohort construction, and real-time diagnostic support with high accuracy and efficiency.

Periodic Asynchrony: An On-Policy Approach for Accelerating LLM Reinforcement Learning

This paper proposes "Periodic Asynchrony," a framework that accelerates LLM reinforcement learning by decoupling inference and training into a provably on-policy asynchronous pipeline, achieving a 3- to 5-fold throughput improvement on NPU platforms without sacrificing accuracy.

Permutation-Equivariant 2D State Space Models: Theory and Canonical Architecture for Multivariate Time Series

This paper introduces the Variable-Invariant Two-Dimensional State Space Model (VI 2D SSM) and its unified VI 2D Mamba architecture, which theoretically establish and implement a permutation-equivariant framework for multivariate time series that eliminates artificial variable ordering to achieve state-of-the-art performance with improved structural scalability.

Personalized Feature Translation for Expression Recognition: An Efficient Source-Free Domain Adaptation Method

This paper proposes SFDA-PFT, a lightweight source-free domain adaptation method that utilizes a pretrained translator to map subject-specific style features in the latent space, enabling effective facial expression recognition on unlabeled neutral target data without requiring source data or unstable image synthesis.

PhD Thesis Summary: Methods for Reliability Assessment and Enhancement of Deep Neural Network Hardware Accelerators

This PhD thesis presents novel, cost-efficient methods for assessing and enhancing the reliability of Deep Neural Network hardware accelerators, including a systematic literature review, new analytical tools, optimized trade-off methodologies, and the development of the AdAM real-time fault tolerance technique.

Physics-Informed Neural Engine Sound Modeling with Differentiable Pulse-Train Synthesis

This paper introduces the Pulse-Train-Resonator (PTR), a differentiable synthesis model that improves engine sound generation by directly modeling sequential exhaust pressure pulses and physical resonances rather than approximating spectral characteristics, achieving superior reconstruction accuracy and interpretability across diverse engine types.

PlayWorld: Learning Robot World Models from Autonomous Play

PlayWorld introduces a fully autonomous pipeline that trains high-fidelity, physically consistent video world models from unsupervised robot self-play, outperforming human-collected data in predicting complex interactions and significantly boosting real-world reinforcement learning success rates.

PnLCalib: Sports Field Registration via Points and Lines Optimization

The paper proposes PnLCalib, an optimization-based calibration pipeline for sports field registration that leverages a 3D soccer field model, keypoints, and a novel line-based refinement module to achieve superior accuracy and robustness in diverse broadcast scenarios compared to traditional search-based methods.

Pri4R: Learning World Dynamics for Vision-Language-Action Models with Privileged 4D Representation

Pri4R is a simple yet effective method that enhances Vision-Language-Action models with an implicit understanding of world dynamics by training them to predict 3D point tracks using privileged 4D information, thereby significantly improving physical manipulation performance without adding inference overhead.

PrivPRISM: Automatically Detecting Discrepancies Between Google Play Data Safety Declarations and Developer Privacy Policies

The paper introduces PrivPRISM, an automated framework that uses language models to detect widespread discrepancies between Google Play's simplified data safety declarations and developers' full privacy policies, revealing that over half of popular apps contain non-compliant or misleading disclosures about their data practices.

PromptDLA: A Domain-aware Prompt Document Layout Analysis Framework with Descriptive Knowledge as a Cue

This paper introduces PromptDLA, a domain-aware framework that leverages descriptive knowledge as cues to customize prompts for integrating domain priors, thereby overcoming the limitations of directly merging diverse datasets and achieving state-of-the-art performance in Document Layout Analysis across multiple benchmarks.

QUSR: Quality-Aware and Uncertainty-Guided Image Super-Resolution Diffusion Model

The paper proposes QUSR, a novel diffusion-based image super-resolution model that combines an Uncertainty-Guided Noise Generation module to adaptively perturb high-uncertainty regions and a Quality-Aware Prior leveraging Multimodal Large Language Models to guide restoration, thereby achieving high-fidelity results in real-world scenarios with unknown and non-uniform degradations.

Quantifying Uncertainty in AI Visibility: A Statistical Framework for Generative Search Measurement

This paper argues that citation visibility in generative search should be treated as a stochastic distribution requiring uncertainty estimates rather than a fixed value, demonstrating through empirical analysis of multiple AI platforms that single-run measurements are misleadingly precise and that robust statistical sampling is essential for accurate domain performance assessment.

Quantifying the Accuracy and Cost Impact of Design Decisions in Budget-Constrained Agentic LLM Search

This paper presents a controlled study using the Budget-Constrained Agentic Search (BCAS) framework to quantify how search depth, retrieval strategies, and completion budgets impact the accuracy and cost of Agentic RAG systems across six LLMs and three benchmarks, offering practical configuration guidelines for budget-constrained deployments.

Quantifying the Necessity of Chain of Thought through Opaque Serial Depth

This paper introduces the concept of "opaque serial depth" to formally quantify the limits of internal reasoning in language models without externalized chain-of-thought, providing computational bounds for various architectures and an open-source tool to analyze the potential for non-externalized reasoning.

REAP the Experts: Why Pruning Prevails for One-Shot MoE compression

This paper introduces REAP, a novel expert pruning method that outperforms existing merging techniques for compressing Mixture-of-Experts models in generative tasks by leveraging router gate-values and activation norms to minimize reconstruction error, achieving near-lossless compression even at 50% expert reduction.

RECODE: Reasoning Through Code Generation for Visual Question Answering

The paper introduces RECODE, an agentic framework that enhances visual question answering by reverse-engineering structured visuals into executable code through iterative generation and selection, thereby transforming ambiguous perceptual tasks into verifiable symbolic reasoning problems that significantly outperform existing methods.

RL-100: Performant Robotic Manipulation with Real-World Reinforcement Learning

RL-100 is a unified real-world reinforcement learning framework that combines diffusion visuomotor policies with a clipped PPO objective and consistency distillation to achieve 100% success across 1,000 diverse robotic manipulation trials, matching or surpassing human experts while demonstrating robust zero-shot generalization and continuous deployment in dynamic environments.

Rating Quality of Diverse Time Series Data by Meta-learning from LLM Judgment

The paper proposes TSRating, a novel meta-learning framework that leverages LLMs to generate quality comparisons across diverse domains and trains an efficient TSRater model to accurately and adaptively evaluate time series data quality without requiring extensive hypergradient computations.

RbtAct: Rebuttal as Supervision for Actionable Review Feedback Generation

This paper introduces RbtAct, a framework that leverages peer review rebuttals as implicit supervision to train large language models to generate more actionable and specific review feedback through a novel perspective-conditioned task and a new dataset called RMR-75K.

Reading the Mood Behind Words: Integrating Prosody-Derived Emotional Context into Socially Responsive VR Agents

This paper proposes and validates an emotion-context-aware VR interaction pipeline that integrates real-time prosody-derived emotional states into LLM-based agent dialogue, significantly improving user engagement, naturalness, and rapport compared to traditional text-only approaches.

Real-Time Trust Verification for Safe Agentic Actions using TrustBench

TrustBench is a dual-mode framework that intervenes between action formulation and execution to verify autonomous agent safety in real-time, utilizing domain-specific plugins to reduce harmful actions by 87% with sub-200ms latency.

Reasoning Efficiently Through Adaptive Chain-of-Thought Compression: A Self-Optimizing Framework

This paper introduces SEER, a self-optimizing framework that adaptively compresses Chain-of-Thought reasoning to significantly reduce computational costs and latency while improving accuracy and robustness in software engineering and mathematical tasks.

Reasoning as Gradient: Scaling MLE Agents Beyond Tree Search

This paper introduces \textsc{Gome}, a gradient-based MLE agent that outperforms traditional tree search methods on MLE-Bench by mapping diagnostic reasoning to gradient computation, demonstrating that as LLM reasoning capabilities improve, gradient-based optimization becomes increasingly superior to exhaustive enumeration.

Reinforced Generation of Combinatorial Structures: Ramsey Numbers

This paper introduces AlphaEvolve, an LLM-based code mutation agent that serves as a unified meta-algorithm to improve the lower bounds of five classical Ramsey numbers and successfully recover or match existing bounds across various cases, demonstrating a shift from bespoke search methods to a single, adaptable framework for combinatorial structure generation.

Reinforcement Learning for Self-Improving Agent with Skill Library

This paper introduces SAGE, a novel Reinforcement Learning framework that enhances LLM-based agents' self-improvement capabilities by utilizing a skill library with sequential rollouts and skill-integrated rewards, achieving significantly higher goal completion rates and greater efficiency than existing methods on the AppWorld benchmark.

Rescaling Confidence: What Scale Design Reveals About LLM Metacognition

This paper demonstrates that the design of confidence scales significantly impacts LLM metacognition, revealing that standard 0–100 formats induce heavy discretization while 0–20 scales consistently improve metacognitive efficiency.

Research and Prototyping Study of an LLM-Based Chatbot for Electromagnetic Simulations

This paper presents a Google Gemini 2.0 Flash-based chatbot that automates the setup, execution, and post-processing of two-dimensional finite element eddy current simulations using Gmsh and GetDP, thereby significantly reducing the time required to generate electromagnetic models with customizable geometries and analysis routines.

Reviving ConvNeXt for Efficient Convolutional Diffusion Models

This paper introduces the Fully Convolutional Diffusion Model (FCDM), a ConvNeXt-based architecture that achieves competitive generative performance with significantly fewer computational resources and training steps than Transformer-based counterparts, demonstrating that modern convolutional designs remain a highly efficient alternative for scaling diffusion models.

Robust Regularized Policy Iteration under Transition Uncertainty