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[2601.19001] FROST: Filtering Reasoning Outliers with Attention for Efficient Reasoning
Summary
The paper presents FROST, an innovative method that utilizes attention mechanisms to filter out reasoning outliers, enhancing the efficiency and accuracy of reasoning models.
Why It Matters
FROST addresses the challenge of inefficient reasoning in AI models by leveraging attention weights to prune unnecessary paths, which is crucial for improving performance in natural language processing tasks. The method's empirical validation on benchmarks demonstrates its potential to significantly reduce token usage and improve accuracy, making it relevant for researchers and practitioners in AI and machine learning.
Key Takeaways
- FROST enhances reasoning efficiency by filtering out uncritical paths.
- The method achieves a 69.68% reduction in token usage.
- FROST improves accuracy by 26.70% compared to baseline models.
- Attention outlier metrics show significant improvements with FROST.
- The approach is validated on multiple benchmarks, outperforming existing methods.
Computer Science > Computation and Language arXiv:2601.19001 (cs) [Submitted on 26 Jan 2026 (v1), last revised 24 Feb 2026 (this version, v2)] Title:FROST: Filtering Reasoning Outliers with Attention for Efficient Reasoning Authors:Haozheng Luo, Zhuolin Jiang, Md Zahid Hasan, Yan Chen, Soumalya Sarkar View a PDF of the paper titled FROST: Filtering Reasoning Outliers with Attention for Efficient Reasoning, by Haozheng Luo and 4 other authors View PDF HTML (experimental) Abstract:We propose FROST, an attention-aware method for efficient reasoning. Unlike traditional approaches, FROST leverages attention weights to prune uncritical reasoning paths, yielding shorter and more reliable reasoning trajectories. Methodologically, we introduce the concept of reasoning outliers and design an attention-based mechanism to remove them. Theoretically, FROST preserves and enhances the model's reasoning capacity while eliminating outliers at the sentence level. Empirically, we validate FROST on four benchmarks using two strong reasoning models (Phi-4-Reasoning and GPT-OSS-20B), outperforming state-of-the-art methods such as TALE and ThinkLess. Notably, FROST achieves an average 69.68% reduction in token usage and a 26.70% improvement in accuracy over the base model. Furthermore, in evaluations of attention outlier metrics, FROST reduces the maximum infinity norm by 15.97% and the average kurtosis by 91.09% compared to the base model. Code is available at this https URL Comments: Subjects:...
