Self-Rewarded Multimodal Coherent Reasoning Across Diverse Visual Domains

• URL: http://arxiv.org/abs/2512.22545v1
• Date: Sat, 27 Dec 2025 10:14:14 GMT
• Title: Self-Rewarded Multimodal Coherent Reasoning Across Diverse Visual Domains
• Authors: Jesen Zhang, Ningyuan Liu, Kaitong Cai, Sidi Liu, Jing Yang, Ziliang Chen, Xiaofei Sun, Keze Wang,
• Abstract summary: Multimodal LLMs produce fluent yet unreliable reasoning.<n>We introduce SR-MCR, a lightweight and label-free framework that aligns reasoning.<n> SR-MCR improves both answer accuracy and reasoning coherence across a broad set of visual benchmarks.
• Score: 16.357026482329232
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal LLMs often produce fluent yet unreliable reasoning, exhibiting weak step-to-step coherence and insufficient visual grounding, largely because existing alignment approaches supervise only the final answer while ignoring the reliability of the intermediate reasoning process. We introduce SR-MCR, a lightweight and label-free framework that aligns reasoning by exploiting intrinsic process signals derived directly from model outputs. Five self-referential cues -- semantic alignment, lexical fidelity, non-redundancy, visual grounding, and step consistency -- are integrated into a normalized, reliability-weighted reward that provides fine-grained process-level guidance. A critic-free GRPO objective, enhanced with a confidence-aware cooling mechanism, further stabilizes training and suppresses trivial or overly confident generations. Built on Qwen2.5-VL, SR-MCR improves both answer accuracy and reasoning coherence across a broad set of visual benchmarks; among open-source models of comparable size, SR-MCR-7B achieves state-of-the-art performance with an average accuracy of 81.4%. Ablation studies confirm the independent contributions of each reward term and the cooling module.

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