@inproceedings{lv-etal-2026-reflect,
title = "Reflect to Inform: Boosting Multimodal Reasoning via Information-Gain-Driven Verification",
author = "Lv, Shuai and
Liu, Chang and
Tang, Feng and
Chen, Rui and
Yuan, Yu-Jie and
Zhou, Aojun and
Zhang, Kui and
Yang, Xi and
Song, Yangqiu",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1193/",
pages = "27318--27342",
ISBN = "979-8-89176-459-0",
abstract = "Multimodal Large Language Models (MLLMs) achieve strong multimodal reasoning performance, yet we identify a recurring failure mode in long-form generation: as outputs grow longer, models progressively drift away from image evidence and fall back on textual priors, resulting in ungrounded reasoning and hallucinations. Interestingly, Based on attention analysis, we find that MLLMs have a latent capability for late-stage visual verification that is present but not consistently activated. Motivated by this observation, we propose Visual Re-Examination (VRE), a self-evolving training framework that enables MLLMs to autonomously perform visual introspection during reasoning without additional visual inputs. Rather than distilling visual capabilities from a stronger teacher, VRE promotes iterative self-improvement by leveraging the model itself to generate reflection traces, making visual information actionable through information gain. Extensive experiments across diverse multimodal benchmarks demonstrate that VRE consistently improves reasoning accuracy and perceptual reliability, while substantially reducing hallucinations, especially in long-chain settings."
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<abstract>Multimodal Large Language Models (MLLMs) achieve strong multimodal reasoning performance, yet we identify a recurring failure mode in long-form generation: as outputs grow longer, models progressively drift away from image evidence and fall back on textual priors, resulting in ungrounded reasoning and hallucinations. Interestingly, Based on attention analysis, we find that MLLMs have a latent capability for late-stage visual verification that is present but not consistently activated. Motivated by this observation, we propose Visual Re-Examination (VRE), a self-evolving training framework that enables MLLMs to autonomously perform visual introspection during reasoning without additional visual inputs. Rather than distilling visual capabilities from a stronger teacher, VRE promotes iterative self-improvement by leveraging the model itself to generate reflection traces, making visual information actionable through information gain. Extensive experiments across diverse multimodal benchmarks demonstrate that VRE consistently improves reasoning accuracy and perceptual reliability, while substantially reducing hallucinations, especially in long-chain settings.</abstract>
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%0 Conference Proceedings
%T Reflect to Inform: Boosting Multimodal Reasoning via Information-Gain-Driven Verification
%A Lv, Shuai
%A Liu, Chang
%A Tang, Feng
%A Chen, Rui
%A Yuan, Yu-Jie
%A Zhou, Aojun
%A Zhang, Kui
%A Yang, Xi
%A Song, Yangqiu
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F lv-etal-2026-reflect
%X Multimodal Large Language Models (MLLMs) achieve strong multimodal reasoning performance, yet we identify a recurring failure mode in long-form generation: as outputs grow longer, models progressively drift away from image evidence and fall back on textual priors, resulting in ungrounded reasoning and hallucinations. Interestingly, Based on attention analysis, we find that MLLMs have a latent capability for late-stage visual verification that is present but not consistently activated. Motivated by this observation, we propose Visual Re-Examination (VRE), a self-evolving training framework that enables MLLMs to autonomously perform visual introspection during reasoning without additional visual inputs. Rather than distilling visual capabilities from a stronger teacher, VRE promotes iterative self-improvement by leveraging the model itself to generate reflection traces, making visual information actionable through information gain. Extensive experiments across diverse multimodal benchmarks demonstrate that VRE consistently improves reasoning accuracy and perceptual reliability, while substantially reducing hallucinations, especially in long-chain settings.
%U https://aclanthology.org/2026.emnlp-main.1193/
%P 27318-27342
Markdown (Informal)
[Reflect to Inform: Boosting Multimodal Reasoning via Information-Gain-Driven Verification](https://aclanthology.org/2026.emnlp-main.1193/) (Lv et al., EMNLP 2026)
ACL
- Shuai Lv, Chang Liu, Feng Tang, Rui Chen, Yu-Jie Yuan, Aojun Zhou, Kui Zhang, Xi Yang, and Yangqiu Song. 2026. Reflect to Inform: Boosting Multimodal Reasoning via Information-Gain-Driven Verification. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 27318–27342, Budapest, Hungary. Association for Computational Linguistics.