@inproceedings{shi-etal-2026-tokenrouter,
title = "{T}oken{R}outer: Uncertainty-Guided Token-Level Routing for Efficient {LLM} Reasoning",
author = "Shi, Zhenning and
Zhu, Yijia and
Shi, Junhan and
Zhang, Xun and
Miao, Congcong and
Xie, Guorui and
Jiang, Yong and
Li, Qing",
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.1891/",
pages = "40295--40307",
ISBN = "979-8-89176-462-0",
abstract = "Large Language Models (LLMs) are strong at multi-step reasoning, but running them for every generated token is expensive. Small language models (SLMs) are much cheaper, yet less reliable at the hardest reasoning steps. Existing routing methods mostly make a single query-level decision, assuming that response difficulty is uniform once decoding begins. For chain-of-thought reasoning, however, difficulty varies during generation, making token-level routing a more appropriate granularity. This setting introduces three challenges: token-level uncertainty is noisy, local routing decisions must satisfy global efficiency constraints, and frequent switching can disrupt reasoning coherence. We present TokenRouter, a black-box runtime controller for uncertainty-guided token-level routing between small and large language models. TokenRouter combines a robust entropy detector, a self-calibrating control policy, and a dynamic intervention commitment mechanism for stable switching over difficult spans. Across three reasoning benchmarks, TokenRouter maintains over 99{\%} of pure-LLM accuracy while reducing large-model activation to 56.2{\%}{--}76.0{\%} of generated tokens. Code is available at https://github.com/sznnzs/TokenRouter."
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<abstract>Large Language Models (LLMs) are strong at multi-step reasoning, but running them for every generated token is expensive. Small language models (SLMs) are much cheaper, yet less reliable at the hardest reasoning steps. Existing routing methods mostly make a single query-level decision, assuming that response difficulty is uniform once decoding begins. For chain-of-thought reasoning, however, difficulty varies during generation, making token-level routing a more appropriate granularity. This setting introduces three challenges: token-level uncertainty is noisy, local routing decisions must satisfy global efficiency constraints, and frequent switching can disrupt reasoning coherence. We present TokenRouter, a black-box runtime controller for uncertainty-guided token-level routing between small and large language models. TokenRouter combines a robust entropy detector, a self-calibrating control policy, and a dynamic intervention commitment mechanism for stable switching over difficult spans. Across three reasoning benchmarks, TokenRouter maintains over 99% of pure-LLM accuracy while reducing large-model activation to 56.2%–76.0% of generated tokens. Code is available at https://github.com/sznnzs/TokenRouter.</abstract>
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%0 Conference Proceedings
%T TokenRouter: Uncertainty-Guided Token-Level Routing for Efficient LLM Reasoning
%A Shi, Zhenning
%A Zhu, Yijia
%A Shi, Junhan
%A Zhang, Xun
%A Miao, Congcong
%A Xie, Guorui
%A Jiang, Yong
%A Li, Qing
%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 shi-etal-2026-tokenrouter
%X Large Language Models (LLMs) are strong at multi-step reasoning, but running them for every generated token is expensive. Small language models (SLMs) are much cheaper, yet less reliable at the hardest reasoning steps. Existing routing methods mostly make a single query-level decision, assuming that response difficulty is uniform once decoding begins. For chain-of-thought reasoning, however, difficulty varies during generation, making token-level routing a more appropriate granularity. This setting introduces three challenges: token-level uncertainty is noisy, local routing decisions must satisfy global efficiency constraints, and frequent switching can disrupt reasoning coherence. We present TokenRouter, a black-box runtime controller for uncertainty-guided token-level routing between small and large language models. TokenRouter combines a robust entropy detector, a self-calibrating control policy, and a dynamic intervention commitment mechanism for stable switching over difficult spans. Across three reasoning benchmarks, TokenRouter maintains over 99% of pure-LLM accuracy while reducing large-model activation to 56.2%–76.0% of generated tokens. Code is available at https://github.com/sznnzs/TokenRouter.
%U https://aclanthology.org/2026.findings-emnlp.1891/
%P 40295-40307
Markdown (Informal)
[TokenRouter: Uncertainty-Guided Token-Level Routing for Efficient LLM Reasoning](https://aclanthology.org/2026.findings-emnlp.1891/) (Shi et al., Findings 2026)
ACL
- Zhenning Shi, Yijia Zhu, Junhan Shi, Xun Zhang, Congcong Miao, Guorui Xie, Yong Jiang, and Qing Li. 2026. TokenRouter: Uncertainty-Guided Token-Level Routing for Efficient LLM Reasoning. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 40295–40307, Budapest, Hungary. Association for Computational Linguistics.