@inproceedings{cheng-etal-2026-stars,
title = "{STARS}: State-Aware Reasoning Stopping via Hidden-State Spectral Analysis",
author = "Cheng, Aaron T. and
Wu, Junda and
Yu, Sheldon and
Wang, Rui and
Rossi, Ryan A. and
Zhou, Sizhe and
Han, Jiawei and
McAuley, Julian and
Yao, Lina and
Yu, Tong",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1790/",
pages = "38289--38306",
ISBN = "979-8-89176-462-0",
abstract = "Chain-of-thought (CoT) reasoning in large language models often produces redundant steps that increase generation cost and obscure when reasoning has effectively completed. Existing stopping methods rely on output-level or token-level signals, overlooking internal representation dynamics that reflect reasoning progress. We propose STARS (STate-Aware Reasoning Stopping), a framework that leverages spectral analysis of hidden-state representations to detect transitions in reasoning phases. Our key insight is that structural changes in the representation space capture shifts in reasoning states, enabling early and reliable stopping decisions. To address representation instability, where semantically similar steps can induce different hidden states, we focus on spectral structure rather than raw activations, improving robustness to context-dependent drift. Experiments on four mathematical and logical reasoning benchmarks show that STARS reduces generation cost by over 45{\%} under fixed token budgets while improving accuracy over standard CoT baselines."
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<abstract>Chain-of-thought (CoT) reasoning in large language models often produces redundant steps that increase generation cost and obscure when reasoning has effectively completed. Existing stopping methods rely on output-level or token-level signals, overlooking internal representation dynamics that reflect reasoning progress. We propose STARS (STate-Aware Reasoning Stopping), a framework that leverages spectral analysis of hidden-state representations to detect transitions in reasoning phases. Our key insight is that structural changes in the representation space capture shifts in reasoning states, enabling early and reliable stopping decisions. To address representation instability, where semantically similar steps can induce different hidden states, we focus on spectral structure rather than raw activations, improving robustness to context-dependent drift. Experiments on four mathematical and logical reasoning benchmarks show that STARS reduces generation cost by over 45% under fixed token budgets while improving accuracy over standard CoT baselines.</abstract>
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%0 Conference Proceedings
%T STARS: State-Aware Reasoning Stopping via Hidden-State Spectral Analysis
%A Cheng, Aaron T.
%A Wu, Junda
%A Yu, Sheldon
%A Wang, Rui
%A Rossi, Ryan A.
%A Zhou, Sizhe
%A Han, Jiawei
%A McAuley, Julian
%A Yao, Lina
%A Yu, Tong
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F cheng-etal-2026-stars
%X Chain-of-thought (CoT) reasoning in large language models often produces redundant steps that increase generation cost and obscure when reasoning has effectively completed. Existing stopping methods rely on output-level or token-level signals, overlooking internal representation dynamics that reflect reasoning progress. We propose STARS (STate-Aware Reasoning Stopping), a framework that leverages spectral analysis of hidden-state representations to detect transitions in reasoning phases. Our key insight is that structural changes in the representation space capture shifts in reasoning states, enabling early and reliable stopping decisions. To address representation instability, where semantically similar steps can induce different hidden states, we focus on spectral structure rather than raw activations, improving robustness to context-dependent drift. Experiments on four mathematical and logical reasoning benchmarks show that STARS reduces generation cost by over 45% under fixed token budgets while improving accuracy over standard CoT baselines.
%U https://aclanthology.org/2026.findings-emnlp.1790/
%P 38289-38306
Markdown (Informal)
[STARS: State-Aware Reasoning Stopping via Hidden-State Spectral Analysis](https://aclanthology.org/2026.findings-emnlp.1790/) (Cheng et al., Findings 2026)
ACL
- Aaron T. Cheng, Junda Wu, Sheldon Yu, Rui Wang, Ryan A. Rossi, Sizhe Zhou, Jiawei Han, Julian McAuley, Lina Yao, and Tong Yu. 2026. STARS: State-Aware Reasoning Stopping via Hidden-State Spectral Analysis. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 38289–38306, Budapest, Hungary. Association for Computational Linguistics.