Func-R1: Incentivizing Mathematical Function Reasoning in Multimodal Large Language Models
Performing deliberate mathematical reasoning in visual contexts is a hallmark of advanced Multimodal Large Language Models (MLLMs) and requires a sophisticated synthesis of perceptual grounding and symbolic logic. However, in the realm of mathematical functions, our investigation reveals a critical modality interference phenomenon: even advanced models, while performing textual computational reasoning, tend to disregard or misinterpret essential visual cues. To address this challenge, we propose Func-R1, which synergistically harmonizes precise visual perception and rigorous logical reasoning. Concretely, built upon an explicitly decoupled architecture, we employ a hierarchical post-train...
| Authors | Mingze Yin, Xiaohan Wang, Dian Li et al. |
|---|---|
| Primary category | cs.CL |
| Published | 13 September 2026 |
| Last updated at source | 5 October 2026 |
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