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    <abstract>Large Reasoning Models (LRMs) show remarkable capabilities in complex reasoning tasks through Chain-of-Thought (CoT) mechanisms. However, their reasoning processes frequently suffer from overthinking, generating inefficient patterns and substantial overhead without improving quality. Existing pruning approaches typically rely on computationally expensive external teacher models or aggregated sequence-level metrics, failing to capture the fine-grained, intrinsic cognitive state of individual reasoning steps. In this paper, we propose ThinkLeap, a novel confidence-guided optimization framework that identifies and retains only critical cognitive leaps within reasoning chains. Our approach is grounded in the observation that a model’s internal confidence dynamics serve as a fine-grained structural signal: rising confidence often reflects evidence accumulation, while declining confidence signals doubt or redundancy. By leveraging these dynamics, ThinkLeap autonomously replaces inefficient steps with ellipsis markers, preserving the logical flow while signaling omitted deliberation. Extensive experiments across three challenging reasoning benchmarks demonstrate that ThinkLeap establishes a new paradigm for efficient reasoning, achieving an average 19.4% reduction in reasoning length while maintaining comparable accuracy.</abstract>
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%0 Conference Proceedings
%T ThinkLeap: Reasoning Optimization Through Confidence-Guided Cognitive Leap Extraction
%A Zhu, Yijia
%A Shi, Zhenning
%A Shi, Junhan
%A Zhang, Xun
%A Jiang, Yong
%A Li, Qing
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F zhu-etal-2026-thinkleap
%X Large Reasoning Models (LRMs) show remarkable capabilities in complex reasoning tasks through Chain-of-Thought (CoT) mechanisms. However, their reasoning processes frequently suffer from overthinking, generating inefficient patterns and substantial overhead without improving quality. Existing pruning approaches typically rely on computationally expensive external teacher models or aggregated sequence-level metrics, failing to capture the fine-grained, intrinsic cognitive state of individual reasoning steps. In this paper, we propose ThinkLeap, a novel confidence-guided optimization framework that identifies and retains only critical cognitive leaps within reasoning chains. Our approach is grounded in the observation that a model’s internal confidence dynamics serve as a fine-grained structural signal: rising confidence often reflects evidence accumulation, while declining confidence signals doubt or redundancy. By leveraging these dynamics, ThinkLeap autonomously replaces inefficient steps with ellipsis markers, preserving the logical flow while signaling omitted deliberation. Extensive experiments across three challenging reasoning benchmarks demonstrate that ThinkLeap establishes a new paradigm for efficient reasoning, achieving an average 19.4% reduction in reasoning length while maintaining comparable accuracy.
%U https://aclanthology.org/2026.emnlp-main.2270/
%P 51628-51641

Markdown (Informal)

[ThinkLeap: Reasoning Optimization Through Confidence-Guided Cognitive Leap Extraction](https://aclanthology.org/2026.emnlp-main.2270/) (Zhu et al., EMNLP 2026)

• ThinkLeap: Reasoning Optimization Through Confidence-Guided Cognitive Leap Extraction (Zhu et al., EMNLP 2026)

ACL

• Yijia Zhu, Zhenning Shi, Junhan Shi, Xun Zhang, Yong Jiang, and Qing Li. 2026. ThinkLeap: Reasoning Optimization Through Confidence-Guided Cognitive Leap Extraction. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 51628–51641, Budapest, Hungary. Association for Computational Linguistics.