Anomalous video understanding aims to identify abnormal events in videos and interpret their semantic meanings beyond simple anomaly detection. Recent video large language models (Video-LLMs) have demonstrated promising zero-shot capabilities for this task, yet their performance remains limited due to insufficient adap...
Hui-Ning Li, Yu-Xiang Duan, Ji-Yang Tan et al.· 0 citations
DiagLoop is presented, a counterfactual data flywheel that converts codified physical relations or clinical guidelines, authored once per mechanism family, into training supervision beyond recorded cases, and improves strict path correctness over the strongest conventional baseline.
CoEvo is an oracle-grounded self-evolution framework where a single model alternates between Proposer and Solver, and enables an 8B LLM to sustain self-evolution, surpassing distillation baselines and the strongest proprietary reference on path correctness.
AgenticVAU is presented, a training-free multi-agent framework that casts VAU as an explore--verify process, where the system first discovers potential anomalies and then verifies them through targeted observations until sufficient evidence is collected.
Yu-Xiang Duan, Hui-Ning Li, Aonian Li et al.· 2 citations
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