arXiv AI June 16, 2026 By Beidi Zhao, Wenlong Deng, Xinting Liao, Yushu Li, Nazim Shaikh, Yao Nie, Xiaoxiao Li

When RAG Hurts: Diagnosing and Mitigating Attention Distraction in Retrieval-Augmented LVLMs

arXiv:2602. 00344v2 Announce Type: replace-cross Abstract: While Retrieval-Augmented Generation (RAG) is one of the dominant paradigms for enhancing Large Vision-Language Models (LVLMs) on knowledge-based VQA tasks, recent work attributes RAG failures to insufficient attention towards the retrieved context, proposing to reduce the attention allocated to image tokens.

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

arXiv AI

Aug 7

Look Twice: Training-Free Evidence Highlighting for Knowledge-based Visual Question Answering

arXiv:2604. 01280v2 Announce Type: replace-cross Abstract: Knowledge-based Visual Question Answering (KB-VQA) requires Multimodal Large Language Models (MLLMs) to identify and combine fine-grained visual cues with retrieved textual evidence.

By Marco Morini, Sara Sarto, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara

llms nlp multimodal benchmarks

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

arXiv Machine Learning

Jul 9

Comparative Study of Domain-adapted VLMs for General Document Visual Question Answering

arXiv:2607. 07179v1 Announce Type: cross Abstract: Document Visual Question Answering (DocVQA) presents a complex multimodal challenge, requiring models to exploit visual, textual, and layout information from documents.

By Miguel Lopez-Duran, Elena Marrero, Julian Fierrez, Marta Robledo-Moreno, Ruben Vera-Rodriguez, Daniel DeAlcala, Aythami Morales, Ruben Tolosana, Oscar Delgado, Alvaro Ortigosa, Javier Ortega-Garcia

llms nlp fine-tuning multimodal

arXiv AI

Jul 28

Disentangling Semantic Attention from Structural Bias in the Attention Manifold

arXiv:2607. 24017v1 Announce Type: cross Abstract: The empirical success of attention mechanism in Multimodal Large Language Models (MLLMs) often obscures its inherent, subtle flaws.

By Pengkun Jiao, Bin Zhu, Jingjing Chen, Yu-gang Jiang

llms multimodal benchmarks safety

arXiv AI

Jun 8

Focus-then-Context: Subject-Centric Progressive Visual Token Reduction for Vision-Language Models

arXiv:2605. 20950v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) face a bottleneck of prohibitive computational costs arising from massive visual token sequences during inference.

By Yulin Zhao, Zheng Zhang

llms multimodal benchmarks

arXiv AI

Jun 15

Mirage Probes: How Vision Models Fake Visual Understanding

arXiv:2606. 13870v1 Announce Type: cross Abstract: Vision-language models (VLMs) can answer image-based questions confidently, and often correctly, even when no image is provided.

By Daniel Ben-Levi, Judah Goldfeder, Weiliang Zhao, Raz Lapid, Amit LeVi, Allen G. Roush, Ravid Shwartz-Ziv, Hod Lipson

llms diffusion multimodal benchmarks

Related stories

Look Twice: Training-Free Evidence Highlighting for Knowledge-based Visual Question Answering

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

Comparative Study of Domain-adapted VLMs for General Document Visual Question Answering

Disentangling Semantic Attention from Structural Bias in the Attention Manifold

Focus-then-Context: Subject-Centric Progressive Visual Token Reduction for Vision-Language Models

Mirage Probes: How Vision Models Fake Visual Understanding