The rapid proliferation of highly realistic AI-Generated Content (AIGC) necessitates robust and interpretable detection mechanisms. However, existing detectors are predominantly confined to single modalities and provide binary outputs without reasoning. While Multimodal Large Language Models (MLLMs) present a promising...
Hong-Wei Niu, Yun-Peng Luo, Han-Jun Li et al.· 0 citations
The method repurposes DR-RL trajectories, which naturally contain search histories, visited webpages, evidence snippets, and final-answer supervision, and replaces the compact snippets and webpage summaries in each trajectory with the full contents of their corresponding URLs, producing substantially longer multi-docum...
Zi-Han Wang, Hao Wang, Bo Jiang et al.· 0 citations
MoHallBench is presented, a benchmark for diagnosing motion hallucination in VideoLLMs that systematically evaluates three major sources of hallucination: co-occurrence priors, sequential inference, and similarity confusion, and confirms that stronger priors and finer-grained similarity substantially amplify hallucinat...
Sihan Chen, Jiale Li, Jianghang Lin et al.· 0 citations
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