3462 papers
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.
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.
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.
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.
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.
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.
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.
This paper introduces Test-time Ego-Exo Adaptation for Action Anticipation (TEA), 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.
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.
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.
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.
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.
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.
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.
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.
This paper presents Gap-ETH-tight randomized approximation schemes for the Traveling Salesperson Problem and Steiner Tree in -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.
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.
This paper improves the running time of -approximation algorithms for -median and -means clustering in low-dimensional Euclidean spaces to $2^{\tilde{O}(1/\varepsilon)^{d-1}} \cdot n \cdot \text{polylog}(n)1/\varepsilond$ is essentially optimal.
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.
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.