@inproceedings{wang-etal-2026-pats,
title = "{PATS}: Process-Level Adaptive Thinking Mode Switching",
author = "Wang, Yi and
Liu, Junxiao and
Zhang, Shimao and
Chen, Jiajun and
Huang, Shujian",
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.780/",
pages = "16503--16519",
ISBN = "979-8-89176-462-0",
abstract = "Current large-language models (LLMs) typically adopt a fixed reasoning strategy, either simple or complex, regardless of reasoning complexity. This neglect of variation in task and reasoning process complexity leads to an imbalance between performance and efficiency. Existing methods attempt to implement training-free fast-slow thinking system switching to handle problems of varying difficulty, but are limited by coarse-grained solution-level strategy adjustments. To address this issue, we propose a novel reasoning paradigm: Process-Level Adaptive Thinking Mode Switching (PATS), which enables LLMs to dynamically adjust their thinking mode based on the difficulty of each reasoning step, optimizing the balance between accuracy and computational efficiency. Our approach integrates Process Reward Models (PRMs) with Beam Search, incorporating progressive mode switching and bad-step penalty mechanisms based on thresholds derived from a data-driven methodology. Experiments on diverse mathematical and non-mathematical benchmarks across various model families and scales, with PRMs of varying quality demonstrate that our methodology achieves high accuracy while maintaining moderate token usage. This study emphasizes the significance of process-level, difficulty-aware thinking mode adaptation, offering insights into efficient inference for LLMs."
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<abstract>Current large-language models (LLMs) typically adopt a fixed reasoning strategy, either simple or complex, regardless of reasoning complexity. This neglect of variation in task and reasoning process complexity leads to an imbalance between performance and efficiency. Existing methods attempt to implement training-free fast-slow thinking system switching to handle problems of varying difficulty, but are limited by coarse-grained solution-level strategy adjustments. To address this issue, we propose a novel reasoning paradigm: Process-Level Adaptive Thinking Mode Switching (PATS), which enables LLMs to dynamically adjust their thinking mode based on the difficulty of each reasoning step, optimizing the balance between accuracy and computational efficiency. Our approach integrates Process Reward Models (PRMs) with Beam Search, incorporating progressive mode switching and bad-step penalty mechanisms based on thresholds derived from a data-driven methodology. Experiments on diverse mathematical and non-mathematical benchmarks across various model families and scales, with PRMs of varying quality demonstrate that our methodology achieves high accuracy while maintaining moderate token usage. This study emphasizes the significance of process-level, difficulty-aware thinking mode adaptation, offering insights into efficient inference for LLMs.</abstract>
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%0 Conference Proceedings
%T PATS: Process-Level Adaptive Thinking Mode Switching
%A Wang, Yi
%A Liu, Junxiao
%A Zhang, Shimao
%A Chen, Jiajun
%A Huang, Shujian
%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 wang-etal-2026-pats
%X Current large-language models (LLMs) typically adopt a fixed reasoning strategy, either simple or complex, regardless of reasoning complexity. This neglect of variation in task and reasoning process complexity leads to an imbalance between performance and efficiency. Existing methods attempt to implement training-free fast-slow thinking system switching to handle problems of varying difficulty, but are limited by coarse-grained solution-level strategy adjustments. To address this issue, we propose a novel reasoning paradigm: Process-Level Adaptive Thinking Mode Switching (PATS), which enables LLMs to dynamically adjust their thinking mode based on the difficulty of each reasoning step, optimizing the balance between accuracy and computational efficiency. Our approach integrates Process Reward Models (PRMs) with Beam Search, incorporating progressive mode switching and bad-step penalty mechanisms based on thresholds derived from a data-driven methodology. Experiments on diverse mathematical and non-mathematical benchmarks across various model families and scales, with PRMs of varying quality demonstrate that our methodology achieves high accuracy while maintaining moderate token usage. This study emphasizes the significance of process-level, difficulty-aware thinking mode adaptation, offering insights into efficient inference for LLMs.
%U https://aclanthology.org/2026.findings-emnlp.780/
%P 16503-16519
Markdown (Informal)
[PATS: Process-Level Adaptive Thinking Mode Switching](https://aclanthology.org/2026.findings-emnlp.780/) (Wang et al., Findings 2026)
ACL
- Yi Wang, Junxiao Liu, Shimao Zhang, Jiajun Chen, and Shujian Huang. 2026. PATS: Process-Level Adaptive Thinking Mode Switching. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 16503–16519, Budapest, Hungary. Association for Computational Linguistics.