arXiv AI June 26, 2026 By Haoxiang Sun, Tao Wang, Li Yuan, Jian Zhao, Jiancheng Lv

From Structure to Synergy: A Survey of Vision-Language Perception Paradigm Evolution in Multimodal Large Language Models

arXiv:2606. 26196v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have recently made remarkable progress in unifying vision-language understanding and reasoning, especially following the introduction of models such as OpenAI's O-series and DeepSeek's R-series, which have driven a paradigm shift toward perception-centric intelligence.

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

arXiv AI

Jun 3

Vision Inference Former: Sustaining Visual Consistency in Multimodal Large Language Models

arXiv:2605. 18160v2 Announce Type: replace-cross Abstract: In recent years, multimodal large language models (MLLMs) have achieved remarkable progress, primarily attributed to effective paradigms for integrating visual and textual information.

By Xinpeng Dong, Min Zhang, Kairong Han, Xu Tan, Fei Wu, Kun Kuang

llms computer-vision multimodal benchmarks safety

arXiv AI

Jun 3

MUSE: A Unified Agentic Harness for MLLMs

arXiv:2606. 03005v1 Announce Type: cross Abstract: Despite rapid progress, multimodal large language models (MLLMs) still fail on tasks that humans solve effortlessly, such as navigating a grid maze from a screenshot or selecting the correct puzzle piece.

By Jianglin Lu, Hailing Wang, Xu Ma, Qihua Dong, Mingyuan Zhang, Yizhou Wang, Yun Fu

llms agents multimodal benchmarks

arXiv AI

Jun 2

DenseMLLM: Standard Multimodal LLMs for Dense Prediction

arXiv:2602. 14134v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) have demonstrated exceptional capabilities in high-level visual understanding.

By Yi Li, Hongze Shen, Lexiang Tang, Xin Li, Xinpeng Ding, Yinsong Liu, Deqiang Jiang, Xing Sun, Xiaomeng Li

llms computer-vision multimodal benchmarks

arXiv AI

Jun 2

Towards Understanding Modality Interaction in Multimodal Language Models via Partial Information Decomposition

arXiv:2606. 00959v1 Announce Type: new Abstract: Understanding modality interaction in multimodal large language models (MLLMs) is central to reliable deployment.

By Wanlong Fang, Tianle Zhang, Wen Tao, Alvin Chan

llms multimodal benchmarks safety

arXiv Machine Learning

Jul 2

VisReason: A Large-Scale Dataset for Visual Chain-of-Thought Reasoning

arXiv:2511. 17731v2 Announce Type: replace-cross Abstract: Chain-of-Thought (CoT) prompting has proven remarkably effective for eliciting complex reasoning in large language models (LLMs).

By Lingxiao Li, Yifan Wang, Xinyan Gao, Chen Tang, Xiangyu Yue, Chenyu You

llms fine-tuning multimodal benchmarks safety

arXiv AI

Jun 26

What We are Missing in Multimodal LLM Evaluation?

arXiv:2606. 26348v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) can process diverse inputs, e.

By Po-han Li, Shenghui Chen, Sandeep Chinchali, Ufuk Topcu

llms multimodal benchmarks

Related stories

Vision Inference Former: Sustaining Visual Consistency in Multimodal Large Language Models

MUSE: A Unified Agentic Harness for MLLMs

DenseMLLM: Standard Multimodal LLMs for Dense Prediction

Towards Understanding Modality Interaction in Multimodal Language Models via Partial Information Decomposition

VisReason: A Large-Scale Dataset for Visual Chain-of-Thought Reasoning

What We are Missing in Multimodal LLM Evaluation?