贝叶斯充分压缩还不够：通信如何帮助多智能体系统？
原标题：Bayes-Sufficient Compression Is Not Enough: How Does Communication Help Multi-Agent Systems?
AI 摘要
该论文提出「接收者相对有界协调」框架，研究多智能体LLM系统中发送者向执行者传递短消息何时优于直接提供原始上下文。作者将消息价值分解为接收者增益减去协议税，并指出即使贝叶斯充分压缩也可能因执行者无法使用其表面形式而失败。在六个基准上，同一Qwen协议提升了ContextBench联合准确率却降低了ToolSandbox表现，固定消息重放揭示了内容恢复正确但动作闭合失败的现象。
正文节选
Bayes-Sufficient Compression Is Not Enough: How Does Communication Help Multi-Agent Systems? Abstract Multi-agent LLM systems pair a sender with broad context and an executor with a limited local view. We study when a short message improves the executor’s next decision, when raw context is preferable, and when a stronger sender helps. Our framework, receiver-relative bounded coordination, expresses message utility as receiver gain minus protocol tax. Compression beats raw context when tax savi