MathVis-Fine: Aligning Visual Supervision with Necessity via Progressive Dependency-Guided Training for Multimodal Mathematical Reasoning
arXiv:2606.17888v1 Announce Type: new Abstract: Chain-of-Thought (CoT) reasoning has extended from purely linguistic domains to multimodal scenarios; however, existing approaches often treat visual inputs as homogeneous or auxiliary signals, failing to capture the intricate and sample-specific dependencies between text and images in mathematical problem-solving. This gives rise to two core issues: first, the supervisory signals for visual content are generalized and coarse-grained, lacking adaptation to the actual necessity of visual information in each sample; second, training feedback become
The proliferation of multimodal AI models necessitates more refined training techniques to handle complex interdependencies, especially in intricate reasoning tasks like mathematics.
This research addresses a critical limitation in current multimodal AI, improving their ability to accurately interpret and utilize visual information in complex reasoning, which is essential for advanced AI agents.
AI models will be able to better align visual input with textual necessity in reasoning, leading to more robust and accurate mathematical problem-solving capabilities.
- · AI researchers
- · Multimodal AI developers
- · SaaS companies leveraging advanced reasoning AI
- · AI systems with coarse-grained visual understanding
Improved performance of multimodal AI in tasks requiring detailed visual and textual reasoning.
Accelerated development of AI agents capable of solving more complex and real-world math and science problems.
Enhanced automation in fields demanding high-precision multimodal data interpretation and problem-solving.
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Read at arXiv cs.AI