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Decoupled Proactive Safety Monitoring is formalized along three dimensions: whether to intervene, when to intervene, and what the risk is, to address proactive intervention limitations in Large Language Models.
Evaluation across 18 proprietary and open-source agents uncovers a Right-Answer-Wrong-Reason effect and reveals that hallucination subclasses cluster rather than scatter, forming distinct agentic framework, legal task, and category profiles validate the diagnostic power of LexAgentHallu for evaluating agentic hallucina...
This work introduces MINT-Safe, an open-source visual multi-turn training dataset comprising 11,270 multi-image dialogues and 500 refusal VQA pairs, and proposes TAD-Align, a dialogue safety alignment framework centered on a turn-aware dual-objective reward function.
SkillProx is introduced, a proximal-gradient-inspired forward--backward framework that couples closed-loop diagnostic evolution with utility-aware proximal refinement and demonstrates the complementary effects of closed-loop diagnosis and proximal refinement.
Omni-RewardBench is introduced, the first benchmark for comprehensive evaluation of ORMs across modalities and demonstrates that current OLLMs fall short as reward models, revealing several common failure modes such as perception failure, modality dominance failure, and cross-modal fusion failure.
S1-Omni is trained on S1-Omni-Corpus, which covers 200 scientific tasks and contains millions of reasoning samples, and is evaluated on over 60 scientific benchmarks, providing a practical path toward unified scientific modeling.
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