arXiv AI June 19, 2026 By Bo Yin, Xiaobin Hu, Chengming Xu, Ruolin Shen, Mo Yang, Jiangning Zhang, Peng-Tao Jiang, Cheng Tan, Shuicheng YAN

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

Read the original on arXiv AI →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Aug 11

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.

By Haojie Huang, Xinlei Yu, Chengming Xu, Zhangquan Chen, Cheng Yang, Qingdong He, Yu Yang, Jiangning Zhang, Xiaobin Hu

llms reinforcement-learning multimodal benchmarks

Hugging Face Trending Papers

Jun 29

From Accuracy to Visual Dependence: Auditing and Filtering Modality Collapse in Traffic VideoQA

High benchmark accuracy does not guarantee genuine use of visual evidence. We study this problem in traffic accident Video Question Answering (VideoQA), where correct answers should depend on scene-specific visual evidence but may instead be inferred from textual shortcuts.

llms nlp multimodal benchmarks safety

arXiv Computer Vision

Sep 22

Seeing is not Enough: Vision-Language Models Perceive Evidence but Fail to Act

arXiv:2609.22588v1 Announce Type: new Abstract: Vision-language models (VLMs) perform strongly on visual question answering benchmarks, yet often make decisions that contradict visual evidence they h...

By Yuyang Dai, Bofei Huang, Hongbo Zhang, Haoran Xie

llms nlp 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 AI

Jul 28

Are Prompt Optimizers Blind? Cross-Modal Visual Feedback for Automatic Prompt Optimization

arXiv:2607. 24354v1 Announce Type: new Abstract: Automatic prompt optimization (APO) has been widely adopted to adapt vision-language models (VLMs) to downstream tasks without weight updates, yielding promising results.

By Haoyue Liu, Xiaoyu Ma, Ye Chen, Yuexian Zou, Xiaoying Tang

llms multimodal benchmarks

arXiv Computer Vision

Sep 25

Looks the Same, Answers Differently: Flip-Direction Steering for Robust Vision-Language Reasoning

The paper introduces FlipDir, a training‑free inference‑time technique that mitigates answer flips in vision‑language models by steering hidden states along a low‑rank subspace derived from contrastive image pairs. It employs a margin‑based gate to attenuate steering only during uncertain decoding steps, thereby restoring original predictions while keeping stable ones unchanged. The authors also present VisFlip, a benchmark framework that evaluates models across nine dataset‑variation combinations in scientific reasoning, robot‑scene understanding, and medical VQA, showing that FlipDir consistently outperforms existing methods on recovery and preservation metrics.

By Yeonsung Jung, Joonhyun Jeong, Hoang Pham, Joowon Kim, Yoonsik Park, Viet Dac Lai, Eunho Yang

llms robotics multimodal benchmarks