arXiv:2605.13156v2 Announce Type: replace
Abstract: Vision-language models (VLMs) have demonstrated remarkable capabilities in bridging visual perception and natural language understanding, enabling...
By Jiaxin Liu, Ding Zhong, Yue Wang, Zhidong Yang, Zhaolu Kang, Guangyuan Dong, Qishi Zhan, Pengcheng Fang, Aofan Liu
The paper examines how hallucinations arise in multi-stage video‑understanding agents by aligning existing benchmarks with the stages of temporal grounding, visual observation, and reasoning. It introduces a causal stage‑intervention protocol that isolates each stage while keeping the downstream task constant, revealing that grounding errors dominate downstream hallucinations and that correct region location matters more than precise temporal overlap. The study also shows that current benchmark scores poorly predict causal sensitivity and can fail under distribution shift, advocating for stage‑aware evaluation methods.
By Shuzhi Gong, Fengze Sun, Yuansan Liu
arXiv:2609.16646v1 Announce Type: new
Abstract: When strong multimodal models are widely available, progress requires new scientific methodologies beyond benchmark scores---using models as instrument...
By Zhipeng Zhao, Wenxu Wang, Peishun Liu, Ruichun Tang
The paper introduces visual adaptations of counterfactual tests—vCT and vCCT—to evaluate whether chain-of-thought explanations in vision‑language models faithfully reflect the visual evidence driving predictions. Using these tests, the authors benchmark eight open‑source VLMs on two datasets and find that CoTs often fail to track visual evidence, sometimes omitting removed objects or mentioning them inconsistently. They also release two new datasets, Counter‑SNLI‑VE and Counter‑A‑OKVQA, consisting of image pairs that differ by a single object to facilitate further research.
By Bayar Menzat, Maximilian S\"uss, Ruizhi Wang, Benno Steinegger, Thomas Lukasiewicz, Oana-Maria Camburu
arXiv:2605.22902v2 Announce Type: replace-cross
Abstract: Generative Vision-Language Models (VLMs) perform well on multimodal reasoning, but how visual inputs are transformed to text remains poorly u...
By Dimitrios Damianos, Leon Voukoutis, Georgios Skyrianos, Vassilis Katsouros, Georgios Paraskevopoulos
The paper introduces Evidential Uncertainty Quantification (EUQ), a fine‑grained method for detecting misbehaviors in large vision‑language models (LVLMs). EUQ interprets model output features as supporting or opposing evidence and aggregates them using Evidence Theory to quantify internal conflict and ignorance in a single forward pass. Experiments on hallucinations, jailbreaks, adversarial attacks, and out‑of‑distribution failures show that EUQ outperforms strong baselines, linking hallucinations to high conflict and OOD failures to high ignorance, and provides layer‑wise dynamics for interpreting internal representations.
By Tao Huang, Rui Wang, Xiaofei Liu, Yi Qin, Li Duan, Liping Jing