Progressive Multimodal Reasoning via Active Retrieval
Authors: name: Guanting Dong, name: Chenghao Zhang, name: Mengjie Deng, name: Yutao Zhu, name: Zhicheng Dou, name: Ji-Rong Wen

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). Our approach begins with the development of a unified
retrieval module that retrieves key supporting insights for solving complex
reasoning problems from a hybrid-modal retrieval corpus. To bridge the gap in
automated multimodal reasoning verification, we employ the MCTS algorithm
combined with an active retrieval mechanism, which enables the automatic
generation of step-wise annotations. This strategy dynamically retrieves key
insights for each reasoning step, moving beyond traditional beam search
sampling to improve the diversity and reliability of the reasoning space.
Additionally, we introduce a process reward model that aligns progressively to
support the automatic verification of multimodal reasoning tasks. Experimental
results across three complex multimodal reasoning benchmarks confirm the
effectiveness of the AR-MCTS framework in enhancing the performance of various
multimodal models. Further analysis demonstrates that AR-MCTS can optimize
sampling diversity and accuracy, yielding reliable multimodal reasoning.