58 papers
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.
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.
This study evaluates a low-cost FMCW MIMO radar for non-contact vital sign monitoring, revealing that while it achieves high accuracy for average respiratory and heart rates at an optimal distance of 70 cm with sufficient chirp counts, it faces significant limitations in capturing high-resolution beat-to-beat and breath-to-breath variability.
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.
This paper presents a policy-aware, cross-layer auditing methodology that successfully identifies Starlink service tiers and throttling states by analyzing distinct signatures in goodput, RTT, and an internal-to-user ratio derived from aligned terminal telemetry and host-side probes, without requiring operator visibility.
This paper presents a study of a large language model-powered "sighted guide" for blind and low vision users in social virtual reality, revealing that participants adapt their interaction from a tool-based approach when alone to a companionable relationship in the presence of others, thereby offering key design recommendations for future accessible VR guides.
KV-Lock is a training-free framework for DiT-based video diffusion models that dynamically adjusts background key-value locking and classifier-free guidance scales based on hallucination detection to simultaneously enhance foreground quality and maintain background consistency.
This paper introduces MM-tau-p, 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.
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.
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.
Scale-Plan is a scalable framework that leverages large language models to filter irrelevant perceptual information and construct compact, task-relevant representations from natural language instructions, thereby enabling efficient and reliable long-horizon planning for heterogeneous multi-robot teams while outperforming existing baselines on the new MAT2-THOR benchmark.
The paper introduces ReDON, a novel recurrent diffractive optical neural processor that overcomes the limitations of static passive masks by employing reconfigurable, self-modulated nonlinearity inspired by gated linear units, thereby significantly enhancing computational expressivity and task performance on image benchmarks with minimal power overhead.
This paper presents a comprehensive, physically interpretable dielectric model of human skin across sub-terahertz and terahertz frequencies by integrating multi-Debye relaxation theory with effective medium formulations to predict frequency-dependent permittivity for non-invasive diagnostic and imaging applications.
This study utilizes THz time-domain spectroscopy to characterize the frequency-dependent dielectric properties of pork skin tissue across the 0.1–11 THz range, providing critical data for modeling intra-body nanosensor networks.
This paper presents a comprehensive systematization of knowledge on Self-Sovereign Digital Identities (SSDI) by analyzing 80 sources to identify six major adoption challenges, evaluating 47 academic publications and 12 real-world applications to reveal that self-sovereignty is a spectrum, and outlining future research directions to accelerate the shift from centralized to self-sovereign identity systems.
This paper proposes a unified multi-axis concentration modulation framework (MAxCM) and its ratio-based subclass (MAxRSK) to enhance spectral efficiency and error performance in mobile molecular communication systems by enabling robust, channel-independent decoding in dynamic environments.
This paper introduces TimeSpot, a comprehensive benchmark comprising 1,455 real-world images from 80 countries designed to evaluate the limited geo-temporal reasoning capabilities of current vision-language models in predicting location, time, and environmental context from visual evidence alone.
PASS is a scalable framework for pairwise-constrained k-means clustering that optimizes a small working subset while formally certifying the feasibility of cannot-link constraints via list-coloring, thereby enabling efficient classical and quantum solutions with verifiable repair mechanisms for infeasible instances.
This paper addresses the sub-wavelength sensing accuracy limitations in bistatic wireless systems caused by clock asynchronism by deriving a quantitative mapping between distorted channel ratios and ideal features, enabling a robust framework that leverages signal amplitude to reconstruct fine-grained displacement details with nearly an order-of-magnitude improvement.
The paper proposes TA-RNN-Medical-Hybrid, a time-aware and interpretable deep learning framework that integrates continuous-time encoding, SNOMED-based disease representations, and a hierarchical dual-level attention mechanism to accurately predict ICU mortality risk while providing clinically meaningful explanations.