@inproceedings{zhang-etal-2026-mtrouter,
title = "{MTR}outer: Cost-Aware Multi-Turn {LLM} Routing with History{--}Model Joint Embeddings",
author = "Zhang, Yiqun and
Li, Hao and
Wang, Zihan and
Feng, Shi and
Yang, Xiaocui and
Wang, Daling and
Zhang, Bo and
Bai, Lei and
Hu, Shuyue",
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.2045/",
pages = "44206--44226",
ISBN = "979-8-89176-390-6",
abstract = "Multi-turn, long-horizon tasks are increasingly common for large language models (LLMs), but solving them typically requires many sequential model invocations, accumulating substantial inference costs. Here, we study cost-aware multi-turn LLM routing: selecting which model to invoke at each turn from a model pool, given a fixed cost budget. We propose MTRouter, which encodes the interaction history and candidate models into joint history{--}model embeddings, and learns an outcome estimator from logged trajectories to predict turn-level model utility. Experiments show that MTRouter improves the performance{--}cost trade-off: on ScienceWorld, it surpasses GPT-5 while reducing total cost by 58.7{\%}; on Humanity{'}s Last Exam (HLE), it achieves competitive accuracy while reducing total cost by 43.4{\%} relative to GPT-5, and these gains even carry over to held-out tasks. Further analyses reveal several mechanisms underlying its effectiveness: relative to prior multi-turn routers, MTRouter makes fewer model switches, is more tolerant to transient errors, and exhibits emergent specialization across models.Code: https://github.com/ZhangYiqun018/MTRouter"
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<abstract>Multi-turn, long-horizon tasks are increasingly common for large language models (LLMs), but solving them typically requires many sequential model invocations, accumulating substantial inference costs. Here, we study cost-aware multi-turn LLM routing: selecting which model to invoke at each turn from a model pool, given a fixed cost budget. We propose MTRouter, which encodes the interaction history and candidate models into joint history–model embeddings, and learns an outcome estimator from logged trajectories to predict turn-level model utility. Experiments show that MTRouter improves the performance–cost trade-off: on ScienceWorld, it surpasses GPT-5 while reducing total cost by 58.7%; on Humanity’s Last Exam (HLE), it achieves competitive accuracy while reducing total cost by 43.4% relative to GPT-5, and these gains even carry over to held-out tasks. Further analyses reveal several mechanisms underlying its effectiveness: relative to prior multi-turn routers, MTRouter makes fewer model switches, is more tolerant to transient errors, and exhibits emergent specialization across models.Code: https://github.com/ZhangYiqun018/MTRouter</abstract>
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%0 Conference Proceedings
%T MTRouter: Cost-Aware Multi-Turn LLM Routing with History–Model Joint Embeddings
%A Zhang, Yiqun
%A Li, Hao
%A Wang, Zihan
%A Feng, Shi
%A Yang, Xiaocui
%A Wang, Daling
%A Zhang, Bo
%A Bai, Lei
%A Hu, Shuyue
%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 zhang-etal-2026-mtrouter
%X Multi-turn, long-horizon tasks are increasingly common for large language models (LLMs), but solving them typically requires many sequential model invocations, accumulating substantial inference costs. Here, we study cost-aware multi-turn LLM routing: selecting which model to invoke at each turn from a model pool, given a fixed cost budget. We propose MTRouter, which encodes the interaction history and candidate models into joint history–model embeddings, and learns an outcome estimator from logged trajectories to predict turn-level model utility. Experiments show that MTRouter improves the performance–cost trade-off: on ScienceWorld, it surpasses GPT-5 while reducing total cost by 58.7%; on Humanity’s Last Exam (HLE), it achieves competitive accuracy while reducing total cost by 43.4% relative to GPT-5, and these gains even carry over to held-out tasks. Further analyses reveal several mechanisms underlying its effectiveness: relative to prior multi-turn routers, MTRouter makes fewer model switches, is more tolerant to transient errors, and exhibits emergent specialization across models.Code: https://github.com/ZhangYiqun018/MTRouter
%U https://aclanthology.org/2026.acl-long.2045/
%P 44206-44226
Markdown (Informal)
[MTRouter: Cost-Aware Multi-Turn LLM Routing with History–Model Joint Embeddings](https://aclanthology.org/2026.acl-long.2045/) (Zhang et al., ACL 2026)
ACL
- Yiqun Zhang, Hao Li, Zihan Wang, Shi Feng, Xiaocui Yang, Daling Wang, Bo Zhang, Lei Bai, and Shuyue Hu. 2026. MTRouter: Cost-Aware Multi-Turn LLM Routing with History–Model Joint Embeddings. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 44206–44226, San Diego, California, United States. Association for Computational Linguistics.