arXiv Computation and Language August 21, 2026 By Yinfeng Wang, Zhiyuan Yao, Zheren Fu, Lei Zhang, Zhendong Mao

When Irrelevant Text Matters: Affine Margin Shifts in Multimodal Large Language Models

Read the original on arXiv Computation and Language →

arXiv:2608. 19208v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) are frequently exposed to auxiliary textual context, the impact of which on visually grounded tasks remains underexplored.

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arXiv AI

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BUZZY: Contrastive Scoring to Mitigate Text-Induced Bias in Multimodal Multiple-Choice QA

arXiv:2603. 28026v2 Announce Type: replace Abstract: Multimodal multiple-choice question answering (MCQA) provides a standardized and objectively measurable setting for evaluating vision-language models (VLMs).

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arXiv Computation and Language

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llms multimodal benchmarks

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arXiv:2606. 19965v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) are increasingly expected to act on visual information, yet the same scene may require different actions under different task contexts.

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llms multimodal benchmarks

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Disentangling Semantic Attention from Structural Bias in the Attention Manifold

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llms multimodal benchmarks safety

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

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llms multimodal safety

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arXiv:2607. 02575v1 Announce Type: cross Abstract: Vision-language models can perform new tasks without parameter updates through in-context learning (ICL), whose core mechanism is utilizing the support set for task induction.

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