← Back to all news
arXiv Computer Vision November 5, 2026 By Jiaqi Deng, Zhe Fu, Yakun Chen, Dichang Zhang, Zonghan Wu, Xiaoshui Huang, Huan Huo, Guandong Xu

Entropogram-guided Visual Realignment Decoding: Capturing the Rhythm of Multimodal Reasoning

Read the original on arXiv Computer Vision →

The Flow has not summarised this story yet — read it at arXiv Computer Vision.

  • llms
  • multimodal
  • benchmarks
  • safety

One email a morning, machine-written

One email a day, machine-written, one click to leave. We never share your address.

Related stories

arXiv AI
Aug 5

Balancing Efficiency and Efficacy: Training-Free Attention-Guided Switching Between Explicit and Latent Thoughts for MLLMs

arXiv:2608. 03450v1 Announce Type: cross Abstract: Reasoning in Multimodal Large Language Models (MLLMs) requires both fine-grained visual perception and rigorous logical deduction.

By Haoqian Kang, Liupeng Li, Kuofeng Gao, Jinpeng Wang, Zhenyu Lu, Bin Chen, Ke Chen, Yaowei Wang
llmsmultimodalbenchmarkssafety
More like this →
arXiv AI
Jul 9

HIVE: Understanding Post-Hallucination Reasoning in Vision Language Models

arXiv:2607. 07507v1 Announce Type: cross Abstract: Hallucinations in vision language models (VLMs) are commonly treated as semantic errors, yet they often arise from partial or ambiguous visual evidence.

By Feng He, Zhenting Wang, Qifan Wang, Qiang Guan, Dongfang Liu, Ruixiang Tang, Qiankun Li
llmsmultimodalsafety
More like this →
arXiv Computer Vision
Oct 26

Seeing Through the Chain: Understanding and Mitigating Hallucinations in Multimodal Large Reasoning Models

arXiv:2602.03380v3 Announce Type: replace Abstract: While multimodal reasoning models (MLRMs) have exhibited impressive capabilities, they remain prone to hallucinations, and effective solutions are...

By Hao Fang, Jinyu Li, Jiawei Kong, Tianqu Zhuang, Kuofeng Gao, Bin Chen, Shu-Tao Xia
multimodalbenchmarkssafety
More like this →
arXiv Computer Vision
Sep 16

What Do Hallucinations Reveal About Multimodal Reasoning? Diagnosing Visual Grounding Failures via Contrastive Decoding Probes

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
llmsmultimodalbenchmarkssafety
More like this →
arXiv Computer Vision
Oct 8

Attention at Rest Stays at Rest: Breaking Visual Inertia to Mitigate Relation Hallucinations

arXiv:2604.01989v4 Announce Type: replace Abstract: While multimodal large language models demonstrate strong entity-level perception, faithfully grounding relational interactions between objects rem...

By Boyang Gong, Yu Zheng, Fanye Kong, Jie Zhou, Jiwen Lu
llmsmultimodalsafety
More like this →
Hugging Face Trending Papers
Aug 31

Fine-Grained Multi Image Object Hallucination Benchmark

Multimodal Large Language Models (MLLMs) are increasingly deployed in multi-image scenarios requiring complex reasoning across visual contexts. However, current MLLMs remain fundamentally limited by o...

llmsmultimodalbenchmarkssafety
More like this →
About Pricing API Newsletter Sources Privacy Terms Refunds Accessibility Provider info Contact RSS

The Flow links to publishers and never republishes their articles. Summaries are machine-generated.

v1.1.0 · 5f852ea