arXiv AI

Evidence-RL: Towards Evidence-intensive Visual Reasoning

arXiv:2608. 08021v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) should answer from concrete image evidence rather than language priors, dataset shortcuts, or irrelevant visual context.

arXiv AI

Jul 21

Seeing What Is Actually There: PriVE-Bench and PriVE-Tools for Counterfactual Evaluation of Agentic Visual Evidence in VLMs

arXiv:2607. 16311v1 Announce Type: cross Abstract: Vision-language models (VLMs) often answer visual questions using learned language and category priors rather than grounding their predictions in the image itself.

By Jingyu Sun, Jiachen Tu, Yuyang Xue, Yaoxin Jiang, Guoyi Xu, Zhengtao Yao, Rui Qian, Yizheng Sun, Hongpeng Zhou, Jingyuan Sun, Yan Lin

llms agents multimodal benchmarks

arXiv AI

Jun 30

Be Faithful When Response: Returning Fluent and Grounded Answers for Vision-Language Models Reinforcement Learning

arXiv:2606. 29984v1 Announce Type: new Abstract: Reinforcement Learning (RL) is an important paradigm for improving the reasoning capabilities of Vision-Language Models (VLMs).

By Peng, Lee, Yin Zhang, Yanglin Zhang, Haonan Wu, Zishan Liu, Ruoxi Zang, Xin Zhu, Jiayin Zheng, Jian Yao, Zefeng Ji, Fei Ma

llms reinforcement-learning multimodal benchmarks

arXiv AI

Aug 10

Debias in Text, Believe Your Eyes: Text-Anchored Cross-Modal Transfer for Visual Counter-Commonsense Reasoning

arXiv:2608. 06938v1 Announce Type: cross Abstract: The visual reasoning ability of multimodal large language models (MLLMs) is crucial for downstream applications, particularly counter-commonsense reasoning, which requires models to reason beyond common assumptions.

By Chen Ling, Hanqian Li, Dongnan Liu, Keyu Qian, Jungang Li, Xinglong liu, Shiyi Wang, Xin Dong, Pengcheng Zhu, Wei Zhou, Linjian Mo, Nai Ding

llms efficiency multimodal benchmarks

arXiv AI

Jul 14

Evidence-Backed Video Question Answering

arXiv:2607. 11862v1 Announce Type: cross Abstract: Current Video Large Language Models (Video LLMs) excel in question answering (QA) but largely operate as black boxes, providing textual answers without verifiable visual grounding.

By Shijie Wang, Honglu Zhou, Ziyang Wang, Ran Xu, Caiming Xiong, Silvio Savarese, Chen Sun, Juan Carlos Niebles

llms computer-vision nlp fine-tuning benchmarks

arXiv AI

Aug 7

Look Twice: Training-Free Evidence Highlighting for Knowledge-based Visual Question Answering

arXiv:2604. 01280v2 Announce Type: replace-cross Abstract: Knowledge-based Visual Question Answering (KB-VQA) requires Multimodal Large Language Models (MLLMs) to identify and combine fine-grained visual cues with retrieved textual evidence.

By Marco Morini, Sara Sarto, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara

llms nlp multimodal benchmarks

arXiv AI

Jun 17

See First, Answer Later: Visual Evidence Pre-Alignment via Sufficiency-Driven RL

arXiv:2606. 17678v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) integrate strong text reasoning with visual inputs, yet their responses can be inconsistent with the underlying images, indicating ineffective utilization of visual evidence during inference.

By Yilian Liu, Sicong Leng, Guoshun Nan, Junyi Zhu, Jiayu Huang, Minghao Sun, Xuancheng Zhu, Yisong Chen, Zexian Wei, Xiaofeng Tao

llms reinforcement-learning fine-tuning multimodal benchmarks safety

arXiv AI

Jun 19

SPOT-E: Test-Time Entropy Shaping with Visual Spotlights for Frozen VLMs

arXiv:2606. 20244v1 Announce Type: cross Abstract: Vision-language models (VLMs) often underperform on evidence intensive tasks because decisive visual evidence are small, localized, and easy to overlook, leading to failures in evidence readout even when high-level reasoning is intact.

By Bo Yin, Xiaobin Hu, Chengming Xu, Ruolin Shen, Mo Yang, Jiangning Zhang, Peng-Tao Jiang, Cheng Tan, Shuicheng YAN

llms multimodal benchmarks

arXiv AI

Jun 16

Thinking with Visual Grounding

arXiv:2606. 16122v1 Announce Type: new Abstract: Visual thinking should not only sound right; it should show its evidence.

By Junkai Zhang, Yihe Deng, Kai-Wei Chang, Wei Wang

llms agents reinforcement-learning multimodal benchmarks

arXiv Machine Learning

Aug 6

DIVE: Dynamic Iterative Visual Evidence Construction for Efficient Vision-Language Models

arXiv:2608. 04496v1 Announce Type: cross Abstract: Visual inputs in vision-language models (VLMs) are often encoded into substantially longer token sequences than text, making visual tokens a major bottleneck for efficient inference.

By Chen Zhong, Xiao An, Zijie Wang, Jiepan Li, Guangyi Yang, Wei He

llms efficiency multimodal benchmarks

arXiv Machine Learning

Jun 8

Diagnosing Visual Ignorance in Vision-Language Models

arXiv:2606. 06890v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) frequently rely on language priors, producing confident answers that are weakly grounded in visual evidence.

By Runyu Zhou, Qi Zhang, Qixun Wang, Yisen Wang

llms multimodal benchmarks

arXiv AI

Jun 19

ROSE: Benchmarking the Perception-to-Action Gap in Multimodal Models

arXiv:2606. 19965v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) are increasingly expected to act on visual information, yet the same scene may require different actions under different task contexts.

By Yihao Wang, Zijian He, Jie Ren, Keze Wang

llms multimodal benchmarks

arXiv AI

Jun 24

EG-VQA: Benchmarking Verifiable Video Question Answering with Grounded Temporal Evidence

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

llms nlp benchmarks safety

Related stories