arXiv AI June 17, 2026 By Yilian Liu, Sicong Leng, Guoshun Nan, Junyi Zhu, Jiayu Huang, Minghao Sun, Xuancheng Zhu, Yisong Chen, Zexian Wei, Xiaofeng Tao

See First, Answer Later: Visual Evidence Pre-Alignment via Sufficiency-Driven RL

Read the original on arXiv AI →

arXiv:2606. 17678v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) integrate strong text reasoning with visual inputs, yet their responses can be inconsistent with the underlying images, indicating ineffective utilization of visual evidence during inference.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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

Sep 16

Layers, Sinks, and Scaling: Adaptive Evidence Selection for Multimodal Large Language Models

arXiv:2609.16795v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) can answer knowledge-intensive visual questions by combining visual evidence from images with facts retrieved...

By Zhenbin Wang, Lei Zhang, Lituan Wang, Wei Huang, Yan Wang, Zhenwei Zhang

llms multimodal benchmarks

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

Aug 10

Debias in Text, Believe Your Eyes: Text-Anchored Cross-Modal Transfer for Visual Counter-Commonsense Reasoning

arXiv:2608. 06938v1 Announce Type: cross Abstract: The visual reasoning ability of multimodal large language models (MLLMs) is crucial for downstream applications, particularly counter-commonsense reasoning, which requires models to reason beyond common assumptions.

By Chen Ling, Hanqian Li, Dongnan Liu, Keyu Qian, Jungang Li, Xinglong liu, Shiyi Wang, Xin Dong, Pengcheng Zhu, Wei Zhou, Linjian Mo, Nai Ding

llms efficiency multimodal benchmarks

arXiv Computation and Language

4d ago

Question-Specific Knowledge Graphs for Efficient Visual Reasoning

arXiv:2609.35942v1 Announce Type: new Abstract: Recent work in visual question answering has shown that vision-language models can exhibit strong reasoning capabilities by translating visual inputs i...

By Ting-Chih Chen, Emile van Krieken, Shujian Yu, Filip Ilievski

llms nlp reinforcement-learning multimodal benchmarks

arXiv AI

Jun 30

Be Faithful When Response: Returning Fluent and Grounded Answers for Vision-Language Models Reinforcement Learning

arXiv:2606. 29984v1 Announce Type: new Abstract: Reinforcement Learning (RL) is an important paradigm for improving the reasoning capabilities of Vision-Language Models (VLMs).

By Peng, Lee, Yin Zhang, Yanglin Zhang, Haonan Wu, Zishan Liu, Ruoxi Zang, Xin Zhu, Jiayin Zheng, Jian Yao, Zefeng Ji, Fei Ma

llms reinforcement-learning multimodal benchmarks

One email a morning, machine-written

Related stories

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

Layers, Sinks, and Scaling: Adaptive Evidence Selection for Multimodal Large Language Models

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

Debias in Text, Believe Your Eyes: Text-Anchored Cross-Modal Transfer for Visual Counter-Commonsense Reasoning

Question-Specific Knowledge Graphs for Efficient Visual Reasoning

Be Faithful When Response: Returning Fluent and Grounded Answers for Vision-Language Models Reinforcement Learning