ICLR

Know Thy Reasoner: Not All Language Models Explore Alike

你的模型多样性，而非方法，决定推理策略

Moulik Choraria, Argyrios Gerogiannis, Anirban Das, Supriyo Chakraborty, Sourya Basu, Sambit Sahu, Lav R. Varshney

机构 * UIUC（伊利诺伊大学香槟分校） ； Capital One 纠错

AI总结 本文提出模型多样性影响推理策略，通过理论框架分析推理不确定性，验证了不同模型在深度精炼和并行采样中的表现差异。

Comments This is a full-length extension of the workshop paper that appeared in the ICLR 2026 Workshop on LLM Reasoning

Does the Data Processing Inequality Reflect Practice? On the Utility of Low-Level Tasks

数据处理不等式是否反映实践？论低级任务的有用性

Roy Turgeman, Tom Tirer

机构 * Faculty of Engineering Bar-Ilan University（巴伊兰大学工程学院） 纠错

AI总结 本文研究低级处理（如去噪、编码）如何提升分类性能，证明在有限样本下存在预处理可提高准确率，并通过实验验证理论趋势。

Comments ICLR 2026 (camera-ready). Code is available at: https://github.com/serveroy/process-before-you-classify

Journal ref The Fourteenth International Conference on Learning Representations (ICLR 2026)