多智能体协作中的思想通信
Thought Communication in Multiagent Collaboration
AI总结：
本文提出思想通信范式，通过潜变量模型识别智能体间共享与私有思想，实现直接心智交互，实验证明其协作优势。
摘要：
自然语言长期以来促进了人类的合作，但其有损、模糊和间接的特性限制了集体智能的潜力。尽管机器不受这些约束，但大多数基于LLM的多智能体系统仍仅依赖自然语言，交换令牌或其嵌入。为了超越语言，我们引入了一种新范式——思想通信，使智能体能够直接进行心智对心智的交互，类似于心灵感应。为了以原则性的方式揭示这些潜在思想，我们将该过程形式化为一个通用的潜变量模型，其中智能体状态由底层思想的未知函数生成。我们证明，在没有辅助信息的非参数设置下，任意一对智能体之间的共享和私有潜在思想都可以被识别。此外，思想共享的全局结构，包括哪些智能体共享哪些思想以及这些关系如何结构化，也可以以理论保证的方式恢复。在已建立理论的指导下，我们开发了一个框架，在通信之前从所有智能体中提取潜在思想，并为每个智能体分配相关的思想及其共享模式。这一范式自然超越了LLM，适用于所有模态，因为大多数观测数据都源于隐藏的生成过程。在合成和真实世界基准上的实验验证了该理论，并展示了思想通信的协作优势。我们希望这项工作能揭示利用隐藏世界的潜力，因为许多挑战仅通过表面观察无法解决，无论计算或数据规模如何。
英文摘要：
Natural language has long enabled human cooperation, but its lossy, ambiguous, and indirect nature limits the potential of collective intelligence. While machines are not subject to these constraints, most LLM-based multi-agent systems still rely solely on natural language, exchanging tokens or their embeddings. To go beyond language, we introduce a new paradigm, thought communication, which enables agents to interact directly mind-to-mind, akin to telepathy. To uncover these latent thoughts in a principled way, we formalize the process as a general latent variable model, where agent states are generated by an unknown function of underlying thoughts. We prove that, in a nonparametric setting without auxiliary information, both shared and private latent thoughts between any pair of agents can be identified. Moreover, the global structure of thought sharing, including which agents share which thoughts and how these relationships are structured, can also be recovered with theoretical guarantees. Guided by the established theory, we develop a framework that extracts latent thoughts from all agents prior to communication and assigns each agent the relevant thoughts, along with their sharing patterns. This paradigm naturally extends beyond LLMs to all modalities, as most observational data arise from hidden generative processes. Experiments on both synthetic and real-world benchmarks validate the theory and demonstrate the collaborative advantages of thought communication. We hope this work illuminates the potential of leveraging the hidden world, as many challenges remain unsolvable through surface-level observation alone, regardless of compute or data scale.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
