@inproceedings{zhang-etal-2026-bridging,
title = "Bridging Modality Disconnect in Self-Reflection via Closed-Loop Visually Grounded Verification",
author = "Zhang, Haoyu and
Gao, Zhi and
Li, Pengxiang and
Zhang, Xintong and
Wu, Yuwei and
Gao, Rui and
Gao, Mingyang and
Chen, Xuanyan and
Cen, Anqian and
Sun, Che and
Jia, Yunde",
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.2008/",
pages = "45747--45788",
ISBN = "979-8-89176-459-0",
abstract = "Self-reflection has become a key mechanism for improving reasoning in Vision-Language Models (VLMs), yet this corrective mechanism often fails when resolving complex fine-grained visual ambiguities. This performance degradation stems from the issue of modality disconnect in self-reflection: most existing models execute self-reflection either within textual or latent space, lacking an explicit mechanism to align textual reasoning with visual evidence. In this paper, we propose MIRROR, a closed-loop visual reflection framework comprising four steps: response generation, error identification, region-based visual verification, and revision. In this cycle, the model first generates an initial response, identifies uncertain logical statements in the response that require visual verification, then grounds them in relevant image regions, and finally revises based on the visual evidence. We construct a multi-turn visual reflection dataset ReflectV, which empowers the model with such a reflective capability. Extensive experiments across diverse multimodal benchmarks show that MIRROR improves performance and reduces visual hallucinations by an average of about 7.2{\%}, demonstrating the advantage of transforming self-reflection from open-loop textual revision into closed-loop, visually grounded verification."
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<abstract>Self-reflection has become a key mechanism for improving reasoning in Vision-Language Models (VLMs), yet this corrective mechanism often fails when resolving complex fine-grained visual ambiguities. This performance degradation stems from the issue of modality disconnect in self-reflection: most existing models execute self-reflection either within textual or latent space, lacking an explicit mechanism to align textual reasoning with visual evidence. In this paper, we propose MIRROR, a closed-loop visual reflection framework comprising four steps: response generation, error identification, region-based visual verification, and revision. In this cycle, the model first generates an initial response, identifies uncertain logical statements in the response that require visual verification, then grounds them in relevant image regions, and finally revises based on the visual evidence. We construct a multi-turn visual reflection dataset ReflectV, which empowers the model with such a reflective capability. Extensive experiments across diverse multimodal benchmarks show that MIRROR improves performance and reduces visual hallucinations by an average of about 7.2%, demonstrating the advantage of transforming self-reflection from open-loop textual revision into closed-loop, visually grounded verification.</abstract>
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%0 Conference Proceedings
%T Bridging Modality Disconnect in Self-Reflection via Closed-Loop Visually Grounded Verification
%A Zhang, Haoyu
%A Gao, Zhi
%A Li, Pengxiang
%A Zhang, Xintong
%A Wu, Yuwei
%A Gao, Rui
%A Gao, Mingyang
%A Chen, Xuanyan
%A Cen, Anqian
%A Sun, Che
%A Jia, Yunde
%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 zhang-etal-2026-bridging
%X Self-reflection has become a key mechanism for improving reasoning in Vision-Language Models (VLMs), yet this corrective mechanism often fails when resolving complex fine-grained visual ambiguities. This performance degradation stems from the issue of modality disconnect in self-reflection: most existing models execute self-reflection either within textual or latent space, lacking an explicit mechanism to align textual reasoning with visual evidence. In this paper, we propose MIRROR, a closed-loop visual reflection framework comprising four steps: response generation, error identification, region-based visual verification, and revision. In this cycle, the model first generates an initial response, identifies uncertain logical statements in the response that require visual verification, then grounds them in relevant image regions, and finally revises based on the visual evidence. We construct a multi-turn visual reflection dataset ReflectV, which empowers the model with such a reflective capability. Extensive experiments across diverse multimodal benchmarks show that MIRROR improves performance and reduces visual hallucinations by an average of about 7.2%, demonstrating the advantage of transforming self-reflection from open-loop textual revision into closed-loop, visually grounded verification.
%U https://aclanthology.org/2026.emnlp-main.2008/
%P 45747-45788
Markdown (Informal)
[Bridging Modality Disconnect in Self-Reflection via Closed-Loop Visually Grounded Verification](https://aclanthology.org/2026.emnlp-main.2008/) (Zhang et al., EMNLP 2026)
ACL
- Haoyu Zhang, Zhi Gao, Pengxiang Li, Xintong Zhang, Yuwei Wu, Rui Gao, Mingyang Gao, Xuanyan Chen, Anqian Cen, Che Sun, and Yunde Jia. 2026. Bridging Modality Disconnect in Self-Reflection via Closed-Loop Visually Grounded Verification. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 45747–45788, Budapest, Hungary. Association for Computational Linguistics.