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    <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. While existing reasoning step evaluators effectively detect logical fallacies and factual errors, our analysis reveals a critical blind spot: they fail to penalize “valid but inefficient” reasoning steps that inflate token usage without contributing to the solution. To systematically diagnose this limitation, we introduce RIV-GSM8K, a diagnostic benchmark injected with five distinct types of inefficiencies, including circular reasoning and excessive decomposition. Diagnostic experiments reveal that state-of-the-art evaluators struggle to distinguish these inefficiencies from necessary reasoning. To address this gap, we propose CAID (Context-Aware Information Density), a training-free metric grounded in information theory that identifies low-utility steps. To validate the metric’s practical utility, we apply it within PACE, a post-hoc compression strategy. Additional control experiments show that the gains of PACE are not explained by trivial pruning: compared with random step removal and PRM-based compression baselines, it preserves accuracy at substantially higher compression rates. Empirical results on GSM8K, StrategyQA, and ARC-Challenge demonstrate that PACE reduces token consumption by 31–53% while maintaining accuracy, confirming that CAID successfully distills informational “froth” from reasoning chains without compromising deductive validity.</abstract>
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%0 Conference Proceedings
%T Valid Necessary: Diagnosing Latent Inefficiency in Chain-of-Thought
%A Lee, Daeyeop
%A Yu, Hwanjo
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F lee-yu-2026-valid
%X 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. While existing reasoning step evaluators effectively detect logical fallacies and factual errors, our analysis reveals a critical blind spot: they fail to penalize “valid but inefficient” reasoning steps that inflate token usage without contributing to the solution. To systematically diagnose this limitation, we introduce RIV-GSM8K, a diagnostic benchmark injected with five distinct types of inefficiencies, including circular reasoning and excessive decomposition. Diagnostic experiments reveal that state-of-the-art evaluators struggle to distinguish these inefficiencies from necessary reasoning. To address this gap, we propose CAID (Context-Aware Information Density), a training-free metric grounded in information theory that identifies low-utility steps. To validate the metric’s practical utility, we apply it within PACE, a post-hoc compression strategy. Additional control experiments show that the gains of PACE are not explained by trivial pruning: compared with random step removal and PRM-based compression baselines, it preserves accuracy at substantially higher compression rates. Empirical results on GSM8K, StrategyQA, and ARC-Challenge demonstrate that PACE reduces token consumption by 31–53% while maintaining accuracy, confirming that CAID successfully distills informational “froth” from reasoning chains without compromising deductive validity.
%R 10.18653/v1/2026.findings-acl.1942
%U https://aclanthology.org/2026.findings-acl.1942/
%U https://doi.org/10.18653/v1/2026.findings-acl.1942
%P 38998-39014

Markdown (Informal)

[Valid Necessary: Diagnosing Latent Inefficiency in Chain-of-Thought](https://aclanthology.org/2026.findings-acl.1942/) (Lee & Yu, Findings 2026)

• Valid Necessary: Diagnosing Latent Inefficiency in Chain-of-Thought (Lee & Yu, Findings 2026)

ACL

• Daeyeop Lee and Hwanjo Yu. 2026. Valid Necessary: Diagnosing Latent Inefficiency in Chain-of-Thought. In Findings of the Association for Computational Linguistics: ACL 2026, pages 38998–39014, San Diego, California, United States. Association for Computational Linguistics.