@inproceedings{yang-etal-2026-less,
title = "Less is More: Improving {LLM} Reasoning with Minimal Test-Time Intervention",
author = "Yang, Zhen and
Zhang, Mingyang and
Chen, Feng and
Ding, Ganggui and
Hou, Liang and
Tao, Xin and
Chen, Ying-Cong",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.921/",
pages = "20124--20137",
ISBN = "979-8-89176-390-6",
abstract = "Recent progress in large language models (LLMs) has focused on test-time scaling to improve reasoning via increased inference computation, but often at the cost of efficiency. We revisit test-time behavior and uncover a simple yet underexplored phenomenon: reasoning uncertainty is highly localized{---}only a small subset of high-entropy tokens dominantly affects output correctness. Motivated by this, we propose Minimal Test-Time Intervention (MTI), a training-free framework that enhances reasoning accuracy and stability with minimal overhead. MTI includes: (i) Selective CFG intervention, applying classifier-free guidance only at uncertain positions; and (ii) Lightweight negative-prompt guidance, reusing the main model{'}s KV cache to approximate unconditional decoding efficiently. MTI yields consistent gains across general, coding, and STEM tasks{---}e.g., +9.28{\%} average improvement on six benchmarks for DeepSeek-R1-7B and +11.25{\%} on AIME2024 using Ling-mini-2.0{---}while remaining highly efficient."
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<abstract>Recent progress in large language models (LLMs) has focused on test-time scaling to improve reasoning via increased inference computation, but often at the cost of efficiency. We revisit test-time behavior and uncover a simple yet underexplored phenomenon: reasoning uncertainty is highly localized—only a small subset of high-entropy tokens dominantly affects output correctness. Motivated by this, we propose Minimal Test-Time Intervention (MTI), a training-free framework that enhances reasoning accuracy and stability with minimal overhead. MTI includes: (i) Selective CFG intervention, applying classifier-free guidance only at uncertain positions; and (ii) Lightweight negative-prompt guidance, reusing the main model’s KV cache to approximate unconditional decoding efficiently. MTI yields consistent gains across general, coding, and STEM tasks—e.g., +9.28% average improvement on six benchmarks for DeepSeek-R1-7B and +11.25% on AIME2024 using Ling-mini-2.0—while remaining highly efficient.</abstract>
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%0 Conference Proceedings
%T Less is More: Improving LLM Reasoning with Minimal Test-Time Intervention
%A Yang, Zhen
%A Zhang, Mingyang
%A Chen, Feng
%A Ding, Ganggui
%A Hou, Liang
%A Tao, Xin
%A Chen, Ying-Cong
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F yang-etal-2026-less
%X Recent progress in large language models (LLMs) has focused on test-time scaling to improve reasoning via increased inference computation, but often at the cost of efficiency. We revisit test-time behavior and uncover a simple yet underexplored phenomenon: reasoning uncertainty is highly localized—only a small subset of high-entropy tokens dominantly affects output correctness. Motivated by this, we propose Minimal Test-Time Intervention (MTI), a training-free framework that enhances reasoning accuracy and stability with minimal overhead. MTI includes: (i) Selective CFG intervention, applying classifier-free guidance only at uncertain positions; and (ii) Lightweight negative-prompt guidance, reusing the main model’s KV cache to approximate unconditional decoding efficiently. MTI yields consistent gains across general, coding, and STEM tasks—e.g., +9.28% average improvement on six benchmarks for DeepSeek-R1-7B and +11.25% on AIME2024 using Ling-mini-2.0—while remaining highly efficient.
%U https://aclanthology.org/2026.acl-long.921/
%P 20124-20137
Markdown (Informal)
[Less is More: Improving LLM Reasoning with Minimal Test-Time Intervention](https://aclanthology.org/2026.acl-long.921/) (Yang et al., ACL 2026)
ACL
- Zhen Yang, Mingyang Zhang, Feng Chen, Ganggui Ding, Liang Hou, Xin Tao, and Ying-Cong Chen. 2026. Less is More: Improving LLM Reasoning with Minimal Test-Time Intervention. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 20124–20137, San Diego, California, United States. Association for Computational Linguistics.