@inproceedings{dong-etal-2025-progressive,
title = "Progressive Multimodal Reasoning via Active Retrieval",
author = "Dong, Guanting and
Zhang, Chenghao and
Deng, Mengjie and
Zhu, Yutao and
Dou, Zhicheng and
Wen, Ji-Rong",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.180/",
doi = "10.18653/v1/2025.acl-long.180",
pages = "3579--3602",
ISBN = "979-8-89176-251-0",
abstract = "Multi-step multimodal reasoning tasks pose significant challenges for multimodal large language models (MLLMs), and finding effective ways to enhance their performance in such scenarios remains an unresolved issue. In this paper, we propose AR-MCTS, a universal framework designed to progressively improve the reasoning capabilities of MLLMs through Active Retrieval (AR) and Monte Carlo Tree Search (MCTS). AR-MCTS follows the MCTS algorithm and heuristically integrates an active retrieval mechanism during the expansion stage to automatically acquire high-quality step-wise reasoning annotations. Moreover, we further introduce curriculum training objectives to progressively align with a process reward model, ultimately achieving process-level multimodal reasoning verification. Experimental results across three complex multimodal reasoning benchmarks confirm the effectiveness of AR-MCTS. Further analysis demonstrates that it can optimize sampling diversity and accuracy, yielding reliable multimodal reasoning."
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<abstract>Multi-step multimodal reasoning tasks pose significant challenges for multimodal large language models (MLLMs), and finding effective ways to enhance their performance in such scenarios remains an unresolved issue. In this paper, we propose AR-MCTS, a universal framework designed to progressively improve the reasoning capabilities of MLLMs through Active Retrieval (AR) and Monte Carlo Tree Search (MCTS). AR-MCTS follows the MCTS algorithm and heuristically integrates an active retrieval mechanism during the expansion stage to automatically acquire high-quality step-wise reasoning annotations. Moreover, we further introduce curriculum training objectives to progressively align with a process reward model, ultimately achieving process-level multimodal reasoning verification. Experimental results across three complex multimodal reasoning benchmarks confirm the effectiveness of AR-MCTS. Further analysis demonstrates that it can optimize sampling diversity and accuracy, yielding reliable multimodal reasoning.</abstract>
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%0 Conference Proceedings
%T Progressive Multimodal Reasoning via Active Retrieval
%A Dong, Guanting
%A Zhang, Chenghao
%A Deng, Mengjie
%A Zhu, Yutao
%A Dou, Zhicheng
%A Wen, Ji-Rong
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F dong-etal-2025-progressive
%X Multi-step multimodal reasoning tasks pose significant challenges for multimodal large language models (MLLMs), and finding effective ways to enhance their performance in such scenarios remains an unresolved issue. In this paper, we propose AR-MCTS, a universal framework designed to progressively improve the reasoning capabilities of MLLMs through Active Retrieval (AR) and Monte Carlo Tree Search (MCTS). AR-MCTS follows the MCTS algorithm and heuristically integrates an active retrieval mechanism during the expansion stage to automatically acquire high-quality step-wise reasoning annotations. Moreover, we further introduce curriculum training objectives to progressively align with a process reward model, ultimately achieving process-level multimodal reasoning verification. Experimental results across three complex multimodal reasoning benchmarks confirm the effectiveness of AR-MCTS. Further analysis demonstrates that it can optimize sampling diversity and accuracy, yielding reliable multimodal reasoning.
%R 10.18653/v1/2025.acl-long.180
%U https://aclanthology.org/2025.acl-long.180/
%U https://doi.org/10.18653/v1/2025.acl-long.180
%P 3579-3602
Markdown (Informal)
[Progressive Multimodal Reasoning via Active Retrieval](https://aclanthology.org/2025.acl-long.180/) (Dong et al., ACL 2025)
ACL
- Guanting Dong, Chenghao Zhang, Mengjie Deng, Yutao Zhu, Zhicheng Dou, and Ji-Rong Wen. 2025. Progressive Multimodal Reasoning via Active Retrieval. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3579–3602, Vienna, Austria. Association for Computational Linguistics.