@inproceedings{luo-etal-2026-autocrat,
title = "{A}uto{CRAT}: Within-trajectory Joint Control of Stochasticity and Compute for {LLM} Reasoning",
author = "Luo, Hanjun and
Liu, Qiushi and
Zhang, Jingya and
Pang, Haihong and
Wen, Jiaheng and
Ma, Yifei. and
Yao, Yu and
Zhang, Chengxi and
Zhang, Hanrong and
Chen, Yankai and
Salam, Hanan",
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.974/",
pages = "20592--20612",
ISBN = "979-8-89176-462-0",
abstract = "Large language models (LLMs) achieve strong reasoning performance, which depends critically on inference-time decisions. Yet these decisions are commonly handled by static, one-size-fits-all policies, limiting adaptation to diverse tasks and reasoning stages. Recent adaptive methods partially address this limitation, but they primarily adapt either decoding stochasticity (how the model explores) or reasoning compute (how long the model reasons) in isolation, leaving their interaction within a single reasoning trajectory unmodeled. To address this challenge, we shift toward a within-trajectory joint control view, and instantiate it in AutoCRAT, a decoder-side controller for frozen backbones. Using only signals available during decoding, AutoCRAT jointly adjusts sampling stochasticity and reasoning budget during generation. AutoCRAT operates over a discrete action space and updates control decisions only at semantic boundaries, improving stability while remaining responsive to the evolving reasoning process. Comprehensive evaluation across 6 benchmarks demonstrates that AutoCRAT (I) uses 13.8-52.7{\%} fewer inference tokens on average than recommended static configurations, (II) surpasses recommended static and adaptive single-axis baselines by 1.5-4.5{\%} in relative accuracy, and (III) enjoys strong cross-backbone transferability."
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<abstract>Large language models (LLMs) achieve strong reasoning performance, which depends critically on inference-time decisions. Yet these decisions are commonly handled by static, one-size-fits-all policies, limiting adaptation to diverse tasks and reasoning stages. Recent adaptive methods partially address this limitation, but they primarily adapt either decoding stochasticity (how the model explores) or reasoning compute (how long the model reasons) in isolation, leaving their interaction within a single reasoning trajectory unmodeled. To address this challenge, we shift toward a within-trajectory joint control view, and instantiate it in AutoCRAT, a decoder-side controller for frozen backbones. Using only signals available during decoding, AutoCRAT jointly adjusts sampling stochasticity and reasoning budget during generation. AutoCRAT operates over a discrete action space and updates control decisions only at semantic boundaries, improving stability while remaining responsive to the evolving reasoning process. Comprehensive evaluation across 6 benchmarks demonstrates that AutoCRAT (I) uses 13.8-52.7% fewer inference tokens on average than recommended static configurations, (II) surpasses recommended static and adaptive single-axis baselines by 1.5-4.5% in relative accuracy, and (III) enjoys strong cross-backbone transferability.</abstract>
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%0 Conference Proceedings
%T AutoCRAT: Within-trajectory Joint Control of Stochasticity and Compute for LLM Reasoning
%A Luo, Hanjun
%A Liu, Qiushi
%A Zhang, Jingya
%A Pang, Haihong
%A Wen, Jiaheng
%A Ma, Yifei.
%A Yao, Yu
%A Zhang, Chengxi
%A Zhang, Hanrong
%A Chen, Yankai
%A Salam, Hanan
%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 luo-etal-2026-autocrat
%X Large language models (LLMs) achieve strong reasoning performance, which depends critically on inference-time decisions. Yet these decisions are commonly handled by static, one-size-fits-all policies, limiting adaptation to diverse tasks and reasoning stages. Recent adaptive methods partially address this limitation, but they primarily adapt either decoding stochasticity (how the model explores) or reasoning compute (how long the model reasons) in isolation, leaving their interaction within a single reasoning trajectory unmodeled. To address this challenge, we shift toward a within-trajectory joint control view, and instantiate it in AutoCRAT, a decoder-side controller for frozen backbones. Using only signals available during decoding, AutoCRAT jointly adjusts sampling stochasticity and reasoning budget during generation. AutoCRAT operates over a discrete action space and updates control decisions only at semantic boundaries, improving stability while remaining responsive to the evolving reasoning process. Comprehensive evaluation across 6 benchmarks demonstrates that AutoCRAT (I) uses 13.8-52.7% fewer inference tokens on average than recommended static configurations, (II) surpasses recommended static and adaptive single-axis baselines by 1.5-4.5% in relative accuracy, and (III) enjoys strong cross-backbone transferability.
%U https://aclanthology.org/2026.findings-emnlp.974/
%P 20592-20612
Markdown (Informal)
[AutoCRAT: Within-trajectory Joint Control of Stochasticity and Compute for LLM Reasoning](https://aclanthology.org/2026.findings-emnlp.974/) (Luo et al., Findings 2026)
ACL
- Hanjun Luo, Qiushi Liu, Jingya Zhang, Haihong Pang, Jiaheng Wen, Yifei. Ma, Yu Yao, Chengxi Zhang, Hanrong Zhang, Yankai Chen, and Hanan Salam. 2026. AutoCRAT: Within-trajectory Joint Control of Stochasticity and Compute for LLM Reasoning. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 20592–20612, Budapest, Hungary. Association for Computational Linguistics.