Hugging Face Trending Papers June 4, 2026

Cosine Misleads: Auxiliary Losses Reshape Vision Language Models, Not Their Latents

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Latent visual reasoning (LVR) inserts supervised latent tokens between perception and answer generation in vision-language models (VLMs). The field uses alignment between these latents and their visual targets, i.

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arXiv AI

Jun 2

Beyond Visual Memory: Mechanistic Diagnostics of Latent Visual Reasoning

arXiv:2606. 01287v1 Announce Type: cross Abstract: Recent latent visual reasoning methods achieve substantial gains by inserting continuous latent tokens into multimodal language models.

By Garvin Guo, Yu Chen, Xiang Wang, Shuai Li, Xinpei Zhao, Huaxing Liu, Shuai Dong

llms multimodal benchmarks

arXiv AI

Sep 10

Reason Through the Latent! Making Latent Visual Reasoning Necessary

The paper introduces Causal Visual Recurrent Reasoning (CVRR), a method that forces multimodal models to rely on latent visual states by using recurrent computation as the sole image‑conditioned path to prediction. CVRR initializes recurrence from the question hidden state after a pretrained vision‑language model has processed the image, repeatedly updates this state while re‑reading the same visual evidence, and removes all other visual traces before decoding. Experiments on several benchmarks show that CVRR maintains strong performance while other latent reasoners lose visual competence, and causal interventions confirm that predictions depend on the recurrent visual trajectory.

By Suhyeong Park, Junha Jung, Jaewoo Kang

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 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 Computation and Language

Sep 2

Slow to See, Slow to Suppress: Understanding the Effects of Modality in Context-Memory Conflicts

arXiv:2609.00293v1 Announce Type: new Abstract: We investigate how vision-language models (VLMs) handle context-memory conflicts; that is, situations in which the model is given information in contex...

By Athulith Paraselli, Etha Tianze Hua, Ellie Pavlick

llms rag multimodal safety

arXiv AI

Jun 26

From Hallucination to Grounding: Diagnosing Visual Spatial Intelligence via CRISP

arXiv:2606. 26535v1 Announce Type: cross Abstract: Current VLM evaluations often conflate language priors with genuine spatial reasoning.

By Zhixing Li, Yinan Yu

multimodal safety

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Related stories

Beyond Visual Memory: Mechanistic Diagnostics of Latent Visual Reasoning

Reason Through the Latent! Making Latent Visual Reasoning Necessary

Diagnosing Visual Ignorance in Vision-Language Models

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

Slow to See, Slow to Suppress: Understanding the Effects of Modality in Context-Memory Conflicts

From Hallucination to Grounding: Diagnosing Visual Spatial Intelligence via CRISP