Federated learning enables medical institutions to train a global model without sharing data, yet feature heterogeneity from diverse scanners or protocols remains challenging. Existing representation-based methods face two limitations: 1) Incomplete Contextual Representation Learning: single-layer or coupled representa...
Xing-Yue Zhao, Wen-Ke Huang, Ling-Hao Zhuang et al.· 0 citations
ChemArgus is presented, a competition-level chemistry benchmark drawn from chemistry competition material, textbooks, and research papers, whose scoring replaces the answer endpoint with three measurement properties, aiming to guide language models step by step on their path toward chemistry expertise, one corrected st...
Han-Yao Guo, Mo Cui, Zi-Hang Tan et al.· 0 citations
DriveCache is proposed, a training-free, action-aware controller that uses planned motion to allocate reuse across scenes and dynamic programming to place it across denoising steps under a calibrated response budget, which improves the overall fidelity-efficiency trade-off over evaluated cache methods.
Jianchun Yang, Jian Liang, Xian-Da Guo et al.· 0 citations
It is suggested that LLMs differ not only in baseline risk disposition, but also in the risk signals they respond to and the flexibility with which they adjust, providing a behavioural basis for auditing risk-sensitive decision-making in interactive settings.
Xuan-Kun Rong, Wenke Huang, Bo Du et al.· arXiv.org· 0 citations
Federated Learning (FL) has demonstrated a promising future in privacy-friendly collaboration but it faces the data heterogeneity problem. Knowledge Distillation (KD) can serve as an effective method to address this issue. However, challenges arise from the unreliability of existing distillation methods in multi-domain...
Yue Yuan, Wenke Huang, Frank Wan et al.· Neural Information Processin...· 1 citation
TrustVLA is introduced, a mechanism-guided inference-time defense that adapts the Dirichlet evidence framework from trusted classification to monitor per-token, per-layer epistemic uncertainty in VLA policies, providing a retraining-free, mechanism-guided defense for visual-triggered VLA backdoors.
Pin-Han Fu, Xian-Da Guo, Xue-Tao Li et al.· arXiv.org· 0 citations
Switch-Reasoner is proposed, a GRPO-based framework that learns to adaptively select reasoning modes for MLLMs and introduces a dual-level regulation mechanism that balances the overall use of Thinking Mode and Direct Mode while providing sample-level supervision based on the relative benefit of the two choices.
Yiyang Fang, Pei Fu, Jinjie Li et al.· arXiv.org· 0 citations
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