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[2506.13793] Med-REFL: Medical Reasoning Enhancement via Self-Corrected Fine-grained Reflection
Summary
The paper presents Med-REFL, a framework designed to enhance medical reasoning in AI by enabling self-correction through fine-grained reflection, improving performance on medical benchmarks.
Why It Matters
Med-REFL addresses the critical challenge of verifying AI reasoning in high-stakes medical applications, where errors can have serious consequences. By providing a scalable solution to enhance reasoning accuracy, this framework has the potential to significantly improve the reliability of AI in healthcare settings.
Key Takeaways
- Med-REFL enhances AI reasoning in medical contexts through self-correction.
- The framework operates without human labels, using a deterministic structural assessment.
- It shows significant performance improvements on medical benchmarks, outperforming existing models.
- Med-REFL's approach generalizes to other domains, including logical reasoning.
- The framework addresses the verification bottleneck in AI applications.
Computer Science > Artificial Intelligence arXiv:2506.13793 (cs) [Submitted on 11 Jun 2025 (v1), last revised 25 Feb 2026 (this version, v4)] Title:Med-REFL: Medical Reasoning Enhancement via Self-Corrected Fine-grained Reflection Authors:Zongxian Yang, Jiayu Qian, Zegao Peng, Haoyu Zhang, Yu-An Huang, KC Tan, Zhi-An Huang View a PDF of the paper titled Med-REFL: Medical Reasoning Enhancement via Self-Corrected Fine-grained Reflection, by Zongxian Yang and 6 other authors View PDF HTML (experimental) Abstract:Large reasoning models excel in domains like mathematics where intermediate reasoning is straightforward to verify, but struggle to self-correct in medicine fields where evaluating intermediate reasoning is cumbersome and expensive. This verification bottleneck hinders the development of reliable AI reasoners for high-stakes application. Here we propose Med-REFL, a novel framework that learns fine-grained reflection without human labels or model distillation. Med-REFL introduces a deterministic structural assessment of the reasoning space to automatically generate preference data for reflection. By globally evaluating all explored reasoning paths in a tree-of-thoughts, our method quantifies the value of corrective actions, enabling the automated construction of direct preference optimization pairs. This trains the model to recognize and amend its own reasoning fallacies. Extensive experiments show Med-REFL delivers robust gains across diverse models architectures and ...
