Long-form video understanding remains challenging for video agents due to the mismatch between query demands and evidence acquisition strategies. Although recent planning-before-perception methods outperform query-agnostic pipelines, they often rely on a single dominant strategy, either generation-based strategy or ret...
Tianyu Wang, Xu-Ying Wu, Yuxiang Ma et al.· 2 citations
Video-HolmesV2 is presented, a novel benchmark designed for Deep Audio-Visual Coupling that enforces an Evidence-Based Evaluation, requiring models to justify answers with precise spatio-temporal audio-visual evidence, thereby reducing confounding effects of guessing and hallucinated evidence.
Zhao-Yang Wei, Zi-Peng Wang, Yu-She Cao et al.· 0 citations
Micro-dramas, characterized by ultra-short durations and hyper-dense storylines, pose unique challenges for video understanding that conventional benchmarks fail to address. To bridge this gap, we introduce M-Drama, the first large-scale bilingual benchmark for micro-drama comprehension, featuring over 35K instances ac...
Yi-Xin Qin, Shi-Zhe Chen, Zhixuan Yu et al.· 0 citations
The Causal-Generative Dual-Judge (CGDJ) is introduced, auditing World Model Consistency from two perspectives, and analysis exposes audio-visual misalignment, where models verbalize correct causal logic more reliably than they render it, challenging the"world simulator"narrative.
This study formalizes Autonomous Agentic Data Engineering, a novel task designed to evaluate LLMs as autonomous data engineers that drive model specialization through end-to-end data curation, and charts a path toward agent-driven model specialization.
Experiments on perception-intensive visual reasoning benchmarks show that LUT outperforms previous latent reasoning methods and remains competitive with latent-text interleaved methods with lower annotation cost.
J. Kang, Siyue Chen, Ming-Da Li et al.· 1 citation
Dynamic Evidence-Guided Preference Optimization (DEPO) is proposed, a new framework that enables evidence-aware and adaptive preference learning for Med-LVLMs and introduces Multi-Modal Evidence Perturbation (MEP) to suppress non-causal textual and visual shortcuts and Dispre-ferred Evidence Resampling (DER) to continu...
Zixuan Huang, Zhihong Zhu, Xiaolong Liu et al.· Annual Meeting of the Associ...· 0 citations
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