探索大模型智能体如何优化跨模态沟通策略。
Communication Policy Evolution for Proactive LLM Agents
- 提出通信策略框架，区分文本与界面交互模式。
- 实验显示文本提升任务完成率，界面增强回复质量与角色一致性。
- 设计自演化框架CPE，仅靠提示词优化即达最佳效果。
大模型智能体已发展为自主系统，但用户与智能体间仍存在信息鸿沟：沟通成本高，而用户的相似偏好又限制了信息交换。本文形式化定义通信策略，建立基于文本和界面的通信策略，并在多种环境、人格设定及模型组合下进行评估。通过构建主动型智能体的信息不对称场景，设立用户-代理与规划者-执行者两种互补设置。实验结果揭示不同交互渠道的互补优势：文本交互通常更利于任务完成，而结构化界面则显著提升响应质量与人格契合度。受此启发，提出融合两者优势的混合方法。进一步提出通信策略演化（CPE）框架，通过滚动推演与提示词级演化实现策略自我优化。无需修改模型即可仅凭提示词精炼，在多个设置中达到最优任务成功率。研究发现，通信行为是大模型智能体设计中关键却长期被忽视的维度。
原文摘要 · Abstract (English)
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 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 combines these advantages. We further propose Communication Policy Evolution (CPE), a self-evolution framework for refining communication policies through rollout and prompt-level evolving. 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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