ai.hackcv
论文精选 65arXiv

Do Latent Channels Actually Communicate? A Causal Audit of Latent Multi-Agent LLM· 大模型多代理系统中的潜在通信

Latent communication in large language model (LLM)-based multi-agent systems (MAS) transmits continuous internal representations instead of text, but greater representational capacity does not establish that the receiver uses task-relevant information. End-task performance alone also cannot reveal whether an observed effect depends on message presence, content generated for the evaluated example, or information supplied by a separate agent. We introduce a causal audit that applies controlled message replacements at the boundary where the sender-produced representation enters the receiver. Four message settings support five measurements of encoded sender information, receiver sensitivity to message presence and identity, the task value of example-specific content, and the additional value s

AI 解读论文

研究大模型多代理系统中潜在通信的有效性和因果关系

核心方法
通过引入因果审计方法,控制消息替换,评估接收者对消息存在和内容的敏感性
适合谁读
研究者、工程师
要解决的问题
探讨多代理系统中的潜在通信是否真正传递了任务相关的信息
关键实验
设计了四种消息设置,进行了五项关键测量
主要贡献
提出了一种新的因果审计方法,用于更精确地测量多代理系统中潜在通信的效果
意义与局限
研究揭示了潜在通信在多代理系统中的实际作用,为未来的设计和优化提供了依据。但方法主要针对特定任务,可能需要进一步扩展。
领域:cs.AI作者:Huixiang Zhang、Mahzabeen Emu
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