Thought Communication in Multiagent Collaboration
Authors: name: Yujia Zheng, name: Zhuokai Zhao, name: Zijian Li, name: Yaqi Xie, name: Mingze Gao, name: Lizhu Zhang, name: Kun Zhang

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.