arXiv AI June 30, 2026 By Taihang Zhen, Jialiang Hong, Kai Chen, Guang Yang, Junlan Feng, Wenpeng Zhu, Jing Huo, Yang Gao, Depeng Wang, Haitao Wan, Xi Yang, Fanyu Meng, Yuyao Zhang, Ji Qi, Xiangyu Zhou

Reconsidering Overthinking: Penalizing Internal and External Redundancy in CoT Reasoning

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arXiv:2508. 02178v3 Announce Type: replace Abstract: Large reasoning models (LRMs) often exhibit overthinking, producing verbose Chain-of-Thought (CoT) traces that increase inference cost and obscure the underlying reasoning process.

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reinforcement-learning

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Towards Efficient Large Language Reasoning Models via Extreme-Ratio Chain-of-Thought Compression

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Can Reasoning Path still be Effective as Input? Bridging Post-Reasoning to Chain-of-Thought Compression

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1d ago

Interpreting Reasoning of Large Language Models via Partial Information Decomposition

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llms rag fine-tuning safety

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OS-Pruner: Pruning Chains-of-Thought of Reasoning Models via Optimal Stopping

arXiv:2607. 11089v1 Announce Type: new Abstract: Large Language Models (LLMs) have achieved remarkable success in complex reasoning tasks through Chain-of-Thought (CoT) prompting.

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2d ago

Think Right: Learning to Mitigate Under-Over Thinking via Adaptive, Attentive Compression

arXiv:2510.01581v2 Announce Type: replace-cross Abstract: Recent thinking models are capable of solving complex reasoning tasks by scaling test-time compute, but this scaling should be allocated in l...

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reinforcement-learning

arXiv AI

Jul 14

Valid $\ne$ Necessary: Diagnosing Latent Inefficiency in Chain-of-Thought

arXiv:2607. 11266v1 Announce Type: new Abstract: Chain-of-Thought (CoT) prompting has significantly advanced the reasoning capabilities of Large Language Models (LLMs), yet it often incurs substantial computational costs due to over-reasoning: the generation of redundant, verbose, or irrelevant steps.

By Daeyeop Lee, Hwanjo Yu

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