@inproceedings{ma-etal-2026-communication,
title = "Communication Policy Evolution for Proactive {LLM} Agents",
author = "Ma, Xinbei and
Qiu, Jiyang and
Yao, Yao and
Wu, Zheng and
Lu, Yijie and
Qu, Xiangmou and
Yin, Jiaxin and
Lou, Xingyu and
Wang, Jun and
Liu, Weiwen and
Zhang, Weinan and
Zhang, Zhuosheng and
Zhao, Hai",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2038/",
pages = "46467--46495",
ISBN = "979-8-89176-459-0",
abstract = "LLM agents have rapidly evolved into autonomous systems, yet a persistent information gap remains between users and agents: communication is costly, while users' identical preferences further limit information exchange. To investigate how agents should communicate across modalities, this paper formalizes Communication Policy, establishes textual and UI-based policies, and then evaluates communication policies across diverse environments, personas, and model combinations. Building on information asymmetry for proactive agents, we set up two complementary settings, User{--}Agent and Planner{--}Executor. Experimental results reveal complementary strengths between interaction channels: text-based interaction often facilitates task performance, while structured UI improves agents' response quality and persona compliance. Motivated by that, a hybrid method that combines these advantages, we further propose Communication Policy Evolution (CPE), a self-evolution framework for refining communication policies through rollout and prompt-level evolution. Without model modification, CPE achieves the best task success across multiple settings using prompt refinement alone. Our findings identify communication behavior as a critical yet underexplored design dimension for LLM agents."
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<abstract>LLM agents have rapidly evolved into autonomous systems, yet a persistent information gap remains between users and agents: communication is costly, while users’ identical preferences further limit information exchange. To investigate how agents should communicate across modalities, this paper formalizes Communication Policy, establishes textual and UI-based policies, and then evaluates communication policies across diverse environments, personas, and model combinations. Building on information asymmetry for proactive agents, we set up two complementary settings, User–Agent and Planner–Executor. Experimental results reveal complementary strengths between interaction channels: text-based interaction often facilitates task performance, while structured UI improves agents’ response quality and persona compliance. Motivated by that, a hybrid method that combines these advantages, we further propose Communication Policy Evolution (CPE), a self-evolution framework for refining communication policies through rollout and prompt-level evolution. Without model modification, CPE achieves the best task success across multiple settings using prompt refinement alone. Our findings identify communication behavior as a critical yet underexplored design dimension for LLM agents.</abstract>
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%0 Conference Proceedings
%T Communication Policy Evolution for Proactive LLM Agents
%A Ma, Xinbei
%A Qiu, Jiyang
%A Yao, Yao
%A Wu, Zheng
%A Lu, Yijie
%A Qu, Xiangmou
%A Yin, Jiaxin
%A Lou, Xingyu
%A Wang, Jun
%A Liu, Weiwen
%A Zhang, Weinan
%A Zhang, Zhuosheng
%A Zhao, Hai
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F ma-etal-2026-communication
%X LLM agents have rapidly evolved into autonomous systems, yet a persistent information gap remains between users and agents: communication is costly, while users’ identical preferences further limit information exchange. To investigate how agents should communicate across modalities, this paper formalizes Communication Policy, establishes textual and UI-based policies, and then evaluates communication policies across diverse environments, personas, and model combinations. Building on information asymmetry for proactive agents, we set up two complementary settings, User–Agent and Planner–Executor. Experimental results reveal complementary strengths between interaction channels: text-based interaction often facilitates task performance, while structured UI improves agents’ response quality and persona compliance. Motivated by that, a hybrid method that combines these advantages, we further propose Communication Policy Evolution (CPE), a self-evolution framework for refining communication policies through rollout and prompt-level evolution. Without model modification, CPE achieves the best task success across multiple settings using prompt refinement alone. Our findings identify communication behavior as a critical yet underexplored design dimension for LLM agents.
%U https://aclanthology.org/2026.emnlp-main.2038/
%P 46467-46495
Markdown (Informal)
[Communication Policy Evolution for Proactive LLM Agents](https://aclanthology.org/2026.emnlp-main.2038/) (Ma et al., EMNLP 2026)
ACL
- Xinbei Ma, Jiyang Qiu, Yao Yao, Zheng Wu, Yijie Lu, Xiangmou Qu, Jiaxin Yin, Xingyu Lou, Jun Wang, Weiwen Liu, Weinan Zhang, Zhuosheng Zhang, and Hai Zhao. 2026. Communication Policy Evolution for Proactive LLM Agents. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 46467–46495, Budapest, Hungary. Association for Computational Linguistics.