MIND: Multi-rationale INtegrated Discriminative Reasoning Framework for Multi-modal Large Models
arXiv:2512.05530v2 Announce Type: replace 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 susceptibility to misleading cues. 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.
The proliferation of multimodal large language models (MLLMs) is exposing critical limitations in their reasoning capabilities, necessitating new frameworks to address these shortcomings.
Improving MLLMs' reasoning with frameworks like MIND is crucial for developing more robust and reliable AI systems, especially for complex, real-world applications.
The paradigm shifts from passive, imitation-based reasoning in MLLMs to a more active, discriminative approach, aiming for higher logical robustness and resistance to misleading cues.
- · AI researchers and developers
- · Companies deploying MLLM-based applications
- · Sectors reliant on sophisticated AI reasoning
- · Developers of less robust, purely imitation-based MLLMs
- · Applications vulnerable to flawed AI reasoning
Introduction of MIND promises more reliable and robust MLLMs capable of complex reasoning tasks.
Enhanced reasoning capabilities in MLLMs could accelerate the development of more capable AI agents and automated decision-making systems.
The increased sophistication of AI reasoning could lead to the automation of higher-cognitive tasks, further collapsing white-collar workflows.
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