arXiv AI August 6, 2026 By Shuo Liu, Huixiang Cai, Weiru Zhang, Xiaoyi Zeng

GeoReward: Mitigating Contextual Variable Overestimation in Vision-Language Models for Cross-Market Preference Prediction

arXiv:2608. 04504v1 Announce Type: cross Abstract: Vision-language models excel in many multimodal tasks but remain prone to a subtle yet impactful failure mode: they tend to overestimate dominant visual-textual cues while underestimating sparse but decision-critical contextual variables.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jun 2

Visual Persuasion: What Influences Decisions of Vision-Language Models?

arXiv:2602. 15278v2 Announce Type: replace-cross Abstract: The web is littered with images, once created for human consumption and now increasingly interpreted by agents using vision-language models (VLMs).

By Manuel Cherep, Pranav M R, Pattie Maes, Nikhil Singh

llms agents diffusion multimodal safety

arXiv AI

Jun 19

The Hidden Evolution of Disguised Visual Context inside the VLM

arXiv:2606. 20077v1 Announce Type: cross Abstract: Visual tokens enter Large Language Models (LLMs) as raw, foreign signals.

By Wish Suharitdamrong, Tony Alex, Muhammad Awais, Sara Atito

llms multimodal benchmarks

Hugging Face Trending Papers

Jul 30

Scaling Vision-Language Models Is Not Enough to Mitigate Bias

Vision-Language Models (VLMs) such as CLIP are now foundational to multimodal systems, yet their robustness to spurious correlations remains poorly understood at scale. We present the first large-scale empirical study of 194 publicly available VLMs, including 16 model families, covering a wide range of model sizes, 24 training datasets, and three evaluation benchmarks, namely ImageNet (overall performance), CelebA (typical single-attribute bias), and UrbanCars (complex multi-attribute biases).

llms multimodal benchmarks safety

arXiv Machine Learning

Jun 4

Stateful Visual Encoders for Vision-Language Models

arXiv:2606. 04433v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly used in multi-image, multi-turn agentic settings where decisions depend on visual changes.

By Zirui Wang, Junwei Yu, Adam Yala, David M. Chan, Joseph E. Gonzalez, Trevor Darrell

llms agents fine-tuning multimodal

arXiv AI

Jul 20

Debiasing Text-to-Image Evaluation via Implicit Cultural Alignment Reward Modeling

arXiv:2607. 15740v1 Announce Type: cross Abstract: As Text-to-Image (T2I) systems rapidly advance, evaluating the cultural authenticity of synthesized content has become increasingly important for fair and trustworthy generative AI.

By Bo-An Chang, Yu-Chih Chen

llms diffusion nlp reinforcement-learning multimodal benchmarks safety

arXiv AI

Aug 7

Reducing Hallucination in Vision-Language Models via Stage-wise Preference Optimization under Distribution Shift

arXiv:2605. 16411v2 Announce Type: replace-cross Abstract: Hallucination remains a fundamental challenge in vision-language models (VLMs), where autoregressive generation may produce linguistically plausible yet physically inconsistent or visually ungrounded responses due to likelihood maximization under joint probabilistic modeling.

By Qinwu Xu

llms computer-vision reinforcement-learning multimodal benchmarks safety

Related stories

Visual Persuasion: What Influences Decisions of Vision-Language Models?

The Hidden Evolution of Disguised Visual Context inside the VLM

Scaling Vision-Language Models Is Not Enough to Mitigate Bias

Stateful Visual Encoders for Vision-Language Models

Debiasing Text-to-Image Evaluation via Implicit Cultural Alignment Reward Modeling

Reducing Hallucination in Vision-Language Models via Stage-wise Preference Optimization under Distribution Shift