arXiv AI

Seeing Is Not Sharing: Some Vision-Language Models Overestimate Common Ground in Asymmetric Dialogue

arXiv:2606. 31719v1 Announce Type: cross Abstract: In collaborative dialogue, shared perception does not guarantee shared interpretation.

arXiv AI

Jun 9

Aligned but Not Partner-Specific: Distinguishing How Multimodal LLM Agents Succeed in Reference Games Without Human-Like Conventions

arXiv:2606. 08081v1 Announce Type: cross Abstract: Repeated reference games test whether interlocutors replace their initially long descriptions with shorter, partner-specific conventions grounded in shared interaction history.

By Po-Ya Angela Wang, Chinmaya Mishra, Asl{\i} \"Ozy\"urek, Paula Rubio-Fern\'andez, Esam Ghaleb

llms agents multimodal safety

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

Jul 21

What Makes Linguistic Representations Good Models of High-Level Visual Perception in the Human Brain?

arXiv:2607. 16214v1 Announce Type: cross Abstract: Image descriptions represented with language models (LMs) predict human brain responses to naturalistic images in high-level visual regions, but the factors driving this predictivity remain unclear.

By Anna Bavaresco, Ina Klari\'c, Raquel Fern\'andez, Marie-Francine Moens

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arXiv AI

Jun 16

Attention, not scale, drives human-AI alignment in multimodal language prediction

arXiv:2308. 06035v4 Announce Type: replace Abstract: Humans routinely draw on visual context to predict upcoming words.

By Viktor Kewenig, Andrew Lampinen, Samuel A. Nastase, Christopher Edwards, Quitterie Lacome D'Elascombe, Akilles Rechardt, Jeremy I Skipper, Gabriella Vigliocco

llms multimodal benchmarks safety

arXiv AI

Jul 15

Evidence Recomposition and Predictive Context Residualization for Visual Attribution in Multimodal Large Language Models

arXiv:2509. 22415v3 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) have achieved strong vision-language performance, yet their token-level visual evidence remains difficult to inspect.

By Jiawei Liang, Jianjie Huang, Ruoyu Chen, Xianghao Jiao, Siyuan Liang, Shiming Liu, Xiaochun Cao

llms multimodal

arXiv AI

Jun 9

IMUG-Bench: Benchmarking Unified Multimodal Models on Interleaved Understanding and Generation

arXiv:2606. 09169v1 Announce Type: new Abstract: In recent years, unified multimodal models (UMMs) have emerged to support both understanding and generation within a single framework.

By Lingyi Meng, Zecong Tang, Haoran Li, Tengju Ru, Zhejun Cui, Weitong Lian, Qi Kang, Hangshuo Cao, Yichen Zhu, Yechi Liu, Kaixuan Wang, Yu-Jie Yuan, Chunwei Wang, Yu Zhang, Bo Dai

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arXiv AI

Aug 11

Vision-Language Grounding as Bidirectional Concept Correspondence

arXiv:2608. 07886v1 Announce Type: cross Abstract: Vision-language grounding connects language to visual content, yet most existing formulations reduce grounding to a unidirectional localization problem: given a prespecified text phrase or category name, identify the corresponding image region.

By Jieyu Zhang, Ziqi Gao, Luke Zettlemoyer, Ranjay Krishna

llms computer-vision multimodal safety

arXiv Machine Learning

Jun 26

Staying VIGILant: Mitigating Visual Laziness via Counterfactual Visual Alignment in MLLMs

arXiv:2606. 26387v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) extend large language models (LLMs) with visual perception, enabling joint reasoning over images and text.

By Xi Xiao, Chen Liu, Chih-Ting Liao, Yunbei Zhang, Qizhen Lan, Yuxiang Wei, Lin Zhao, Janet Wang, Jianyang Gu, Muchao Ye, Tianyang Wang, Hao Xu

llms reinforcement-learning multimodal benchmarks safety

arXiv AI

Aug 6

Reasoning Dynamics and the Limits of Monitoring Modality Reliance in Vision-Language Models

arXiv:2604. 14888v3 Announce Type: replace-cross Abstract: Recent advances in vision language models (VLMs) offer reasoning capabilities, yet how these unfold and integrate visual and textual information remains unclear.

By Danae S\'anchez Villegas, Samuel Lewis-Lim, Nikolaos Aletras, Desmond Elliott

llms multimodal safety

arXiv AI

Jun 17

Plug-and-Adapt: Multimodal Coreference Resolution at First Sight with a Pretrained Alignment Model

arXiv:2606. 17950v1 Announce Type: cross Abstract: Visual information helps resolve ambiguity in coreference resolution, leading to notable performance gains.

By Jinghan Wu, Jing Li, Ivor W. Tsang, Xuetao Zhang

multimodal benchmarks safety

arXiv AI

Jun 3

Decomposing how prompting steers behavior

arXiv:2606. 03093v1 Announce Type: new Abstract: Prompting steers large language models (LLMs) and vision-language models (VLMs) without weight updates, but it remains unclear how instruction changes reshape internal representations to produce behavior.

By Fan L. Cheng, Nikolaus Kriegeskorte

llms multimodal

arXiv AI

Jul 9

T2T-VICL: Cross-Task Visual In-Context Learning via Implicit Text-Driven VLMs

arXiv:2511. 16107v3 Announce Type: replace-cross Abstract: Visual in-context learning (VICL) solves visual tasks by conditioning on a few input-output demonstrations without any model training.

By Shao-Jun Xia, Huixin Zhang, Zhengzhong Tu

llms multimodal safety

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