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[2505.13529] BARREL: Boundary-Aware Reasoning for Factual and Reliable LRMs
Summary
The paper presents BARREL, a framework designed to enhance the factual reliability of Large Reasoning Models (LRMs) by addressing overconfidence in answers through boundary-aware reasoning.
Why It Matters
As LRMs become integral in various applications, ensuring their factual reliability is crucial. This research highlights the need for improved reasoning mechanisms to mitigate incorrect answers and enhance user trust in AI systems.
Key Takeaways
- BARREL addresses overconfidence in LRMs by promoting boundary-aware reasoning.
- The framework significantly improves factual reliability from 39.33% to 61.48%.
- Identifies and mitigates two common reasoning errors: last-minute guessing and second-thought spiraling.
- Maintains accuracy comparable to models fine-tuned on reasoning data.
- Encourages the development of more reliable System 2 LRMs.
Computer Science > Artificial Intelligence arXiv:2505.13529 (cs) [Submitted on 18 May 2025 (v1), last revised 25 Feb 2026 (this version, v2)] Title:BARREL: Boundary-Aware Reasoning for Factual and Reliable LRMs Authors:Junxiao Yang, Jinzhe Tu, Haoran Liu, Xiaoce Wang, Chujie Zheng, Zhexin Zhang, Shiyao Cui, Caishun Chen, Tiantian He, Hongning Wang, Yew-Soon Ong, Minlie Huang View a PDF of the paper titled BARREL: Boundary-Aware Reasoning for Factual and Reliable LRMs, by Junxiao Yang and 11 other authors View PDF HTML (experimental) Abstract:Recent advances in Large Reasoning Models (LRMs) have shown impressive capabilities in mathematical and logical reasoning. However, current LRMs rarely admit ignorance or respond with "I don't know". Instead, they often produce incorrect answers while showing undue confidence, raising concerns about their factual reliability. In this work, we identify two pathological reasoning patterns characterized by overthinking that contribute to the overconfident and incorrect answers: last-minute guessing and second-thought spiraling. To address these issues, we propose BARREL-a novel framework that promotes concise and boundary-aware factual reasoning. Our experiments show that BARREL-training increases the reliability of DeepSeek-R1-Distill-Llama-8B from 39.33% to 61.48%, while still achieving accuracy comparable to models finetuned on reasoning data generated by R1. These results demonstrate that our pilot study is inspiring to build more rel...
