VisualTrust introduces a counterfactual benchmark to evaluate how multimodal large language models handle on‑screen claims that may conflict with visual evidence. The benchmark contains 5,063 cases across 14 claim‑fact classes, each featuring a question paired with three video versions: no claim, accurate claim, and misleading claim. Results show a drop in accuracy from 90.1% to 51.9% when a misleading claim is present, and a new training‑free agent, DEAR, improves accuracy under misleading claims to 81.8–85.2% while reducing trap‑answer selection.
By Huachao Zhu, Junjielong Xu, Sicheng Li, Liang Yang, Mingtao Chen, Zhichao Hu, Li Jiang, Pinjia He
arXiv:2608.31005v1 Announce Type: new
Abstract: Existing long-video agents acquire evidence through one uniform behavior, ignoring whether the required evidence is concentrated, requires broad occurr...
By Can Zhang, Baofeng Zhang, Xiaotian Han, Junyuan Shang, Yuchen Ding, Shuohuan Wang, Dianhai Yu, Ruirui Li
arXiv:2607. 18080v1 Announce Type: cross Abstract: Multimodal video misinformation detection is commonly formulated as a holistic video-understanding task, where the entire video and its associated content are processed and judged in a single pass.
By Haochen Zhao, Yongxiu Xu, Xinkui Lin, Dong Xie, Jiarui Lu, Yuqi Qian, Yubin Wang, Hongbo Xu, Gaopeng Gou
The paper introduces PACE, a factor-guided, progressive framework for acquiring evidence in long-video question answering. PACE first indexes clip-level descriptions using question-derived factors, then refines evidence retrieval with contrastive cues derived from candidate answers. On the MMR‑V dataset, PACE achieves 42.6% accuracy and recovers 66.9% of annotated cues, outperforming direct inference and prior agentic baselines, and shows consistent improvements across several long-video benchmarks.
By Baixuan Xu, Yinyui Xu, Tianshi Zheng, Zhaowei Wang, Weiqi Wang, Haochen Shi, Jiayu Liu, Qing Zong, Xiyu Ren, Xinyu Geng, Zhitao He, Yangqiu Song
arXiv:2608.05592v2 Announce Type: replace
Abstract: Multimodal Large Language Models (MLLMs) have made strong progress in video understanding, yet long videos remain difficult: the visual token budge...
By Ziling Huang, Shin'ichi Satoh
Multimodal video misinformation detection is commonly formulated as a holistic video-understanding task, where the entire video and its associated content are processed and judged in a single pass. However, real-world misinformation often exhibits a sparse and compositional evidence structure: a reliable decision may depend on only a few coupled clues, while most video content contributes limited additional information.
arXiv:2606. 24797v1 Announce Type: cross Abstract: Recent advances in Video Large Language Models (Video-LLMs) have yielded promising performance on video question answering (VideoQA).
By Linpeng Huang, Weixing Chen, Zexin Chen, Yang Liu, Liang Lin
The paper introduces TRACE, a training‑free agent for long‑video understanding that grounds answers in raw visual clips and builds an evidence bundle until the answer stabilises. It also presents VES‑Bench, a 600‑question benchmark over 348 public long videos that audits whether decoded frames cover all necessary evidence intervals at three strictness levels. TRACE achieves high accuracy on VES‑Bench (63.5% audit accuracy) and remains competitive on other video‑understanding benchmarks while using far fewer frames than uniform decoding.
By Pengyiang Liu, Junbo Niu, Xiaoyang Hu, Zhongyue Shi, Zitian Wang, Linjiang Huang, Si Liu
The paper introduces S‑MME, a benchmark for diagnosing shortcut‑biased behavior in Multimodal Large Language Models (MLLMs) on long‑video question answering. It proposes CLEAR, a training‑free framework that estimates and mitigates bias by treating the full video‑question pair as a complete view and intervening on hidden states during inference. Experiments on five benchmarks demonstrate that CLEAR improves prediction accuracy and robustness across different MLLMs.
By Xusheng Liang, Jianqiao Sun, Hao Zhang, Yulei Niu, Hengshuang Zhao, Jiawei Ma
arXiv:2604.01569v2 Announce Type: replace
Abstract: Video multimodal large language models achieve strong results on existing benchmarks, but answer accuracy alone does not establish whether they can...
By Jiahao Meng, Yue Tan, Qi Xu, Haochen Wang, Zhongwei Ren, Weisong Liu, Yuhao Wang, Renrui Zhang, Xiangtai Li, Haodong Duan, Yunhai Tong, Ming-Hsuan Yang
EvaOmni is a new evidence‑grounded benchmark for audio‑visual reasoning, comprising 392 videos and 1,027 human‑annotated question‑answer pairs that span 15 capabilities and three difficulty levels. The benchmark requires models to provide both an answer and the supporting audio‑visual evidence intervals, and introduces Coverage Grounded (C.G.) and Bottleneck Grounded (B.G.) scores to evaluate evidence identification and temporal localization. Across 24 Omni LLMs, Gemini3.7‑Flash achieved the highest overall accuracy, yet its B.G. scores reveal significant room for improvement in grounded reasoning.
By Xiaoxing Hu, Wenxi Yao, Chaoyuan Shi, Ziqi Ye, Xiaorong Guo, Peixuan Li, Zhuoran Sun, Ziyang Gong, Peiyuan Zhang, Mingxin Liu, Shuran Ma, Hongbo Liu, Xiaomin Yu, Yu Tian, Wentao Wang, Zhenjie Yang, Jian Hu, Yunhang Shen, Haoyu Cao, Xue Yang
The paper proposes a training‑free detector that uses sentence‑level context sensitivity to identify unsupported content in retrieval‑augmented generation (RAG) answers. By re‑scoring each sentence with full context, no context, and each chunk removed, the method flags the chunk whose removal most reduces a sentence’s likelihood as the likely source. Evaluated on RAGTruth, TofuEval, and RAGBench, the detector outperforms answer‑level faithfulness scores, achieving AUCs up to 0.745 and matching per‑chunk fact‑checkers while using only a fraction of the compute required by large‑language‑model judges.
By Mohamed Aly Bouke