MIND: Multi-rationale INtegrated Discriminative Reasoning Framework for Multi-modal Large Models

• URL: http://arxiv.org/abs/2512.05530v1
• Date: Fri, 05 Dec 2025 08:41:44 GMT
• Title: MIND: Multi-rationale INtegrated Discriminative Reasoning Framework for Multi-modal Large Models
• Authors: Chuang Yu, Jinmiao Zhao, Mingxuan Zhao, Yunpeng Liu, Xiujun Shu, Yuanhao Feng, Bo Wang, Xiangyu Yue,
• Abstract summary: We propose a Multi-rationale INtegrated Discriminative (MIND) reasoning framework.<n>It is designed to endow MLLMs with human-like cognitive abilities of "Understand -> Rethink -> Correct"<n>It achieves a paradigm evolution from passive imitation-based reasoning to active discriminative reasoning.
• Score: 15.860796863065737
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, multimodal large language models (MLLMs) have been widely applied to reasoning tasks. However, they suffer from limited multi-rationale semantic modeling, insufficient logical robustness, and are susceptible to misleading interpretations in complex scenarios. Therefore, we propose a Multi-rationale INtegrated Discriminative (MIND) reasoning framework, which is designed to endow MLLMs with human-like cognitive abilities of "Understand -> Rethink -> Correct", and achieves a paradigm evolution from passive imitation-based reasoning to active discriminative reasoning. Specifically, we introduce a Rationale Augmentation and Discrimination (RAD) paradigm, which automatically and efficiently expands existing datasets by generating diverse rationales, providing a unified and extensible data foundation. Meanwhile, we design a Progressive Two-stage Correction Learning (P2CL) strategy. The first phase enhances multi-rationale positive learning, while the second phase enables active logic discrimination and correction. In addition, to mitigate representation entanglement in the multi-rationale semantic space, we propose a Multi-rationale Contrastive Alignment (MCA) optimization strategy, which achieves semantic aggregation of correct reasoning and boundary separation of incorrect reasoning. Extensive experiments demonstrate that the proposed MIND reasoning framework achieves state-of-the-art (SOTA) performance on multiple public datasets covering scientific, commonsense, and mathematical scenarios. It provides a new perspective for advancing MLLMs towards higher levels of cognitive intelligence. Our code is available at https://github.com/YuChuang1205/MIND

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