2982 papers
This paper addresses the challenge of providing individualized feedback in large-scale university badminton classes by introducing a new benchmark dataset of 7,200 annotated video clips and a baseline model that automatically evaluates student movement quality against professional demonstrations, achieving promising performance metrics for intelligent physical education assistance.
This paper proposes that effective reasoning in large language models emerges not merely from low-dimensional inference manifolds, but from a specific constrained structural regime balancing expressivity, compression, and information preservation, enabling a new label-free diagnostic framework based on internal geometric dynamics.
This paper introduces Aux-AI, a federated independent oversight framework featuring a decoupled Trust-Decision Core that ensures trust-aware industrial autonomy by effectively detecting hardware-induced failures and out-of-distribution shifts with superior accuracy, real-time throughput, and energy efficiency compared to existing safety monitors.
TopoNet is a topology-guided dual-branch framework that enhances volumetric medical image classification by integrating spatially registered topological features derived from persistent homology with appearance-based cues through cross-branch attention, thereby improving accuracy, robustness, and interpretability.
This paper identifies and analyzes CVE-2026-0628, a critical privilege escalation vulnerability in Google Chrome's Gemini Live AI side panel that allows extensions with basic permissions to access sensitive browser-level resources, revealing that 73.9% of analyzed extensions possess the necessary permissions to exploit this security gap.
This study demonstrates that using large language models as editorial assistants to refine physicians' written responses, rather than as autonomous generators, significantly enhances perceived empathy while preserving factual accuracy, supported by novel quantitative metrics for evaluating both emotional tone and medical precision.
The paper introduces GRACE, a novel large-language-model framework that iteratively refines hypotheses and evidence to automatically discover the optimal number of clinical subgroups in heterogeneous, multimodal data without requiring prior specification of cluster counts, as validated across Long COVID and Parkinson's disease cohorts.
This survey systematizes the fragmented landscape of autonomous research by introducing a multi-axis framework for 56 systems and their evidence reliability, revealing a critical gap between the ability to generate research artifacts and the lack of robust verification mechanisms to defend them before release.
This paper introduces MOGA-NAS, a multi-objective evolutionary algorithm that effectively balances predictive performance and model compactness for clinical tabular classification by simultaneously maximizing F1-scores and minimizing parameter counts, resulting in significantly smaller models with superior or comparable accuracy across five public benchmarks.
This paper proposes a federated learning security scheme that combines threshold Paillier homomorphic encryption with a data quality-based selective decryption mechanism and ECDSA signatures to effectively defend against inference and tampering attacks while improving training efficiency by approximately 10%.