While natural language is the de facto communication medium for LLM-based agents, it presents a fundamental constraint. The process of downsampling rich, internal latent states into discrete tokens inherently limits the depth and nuance of information that can be transmitted, thereby hindering collaborative problem-solving. Inspired by telepathy, which bypasses symbolic language in communication, we propose Interlat (Inter-agent Latent Space Communication), a paradigm that leverages the continuous last hidden states of an LLM as a representation of its thought for direct communication (termed latent communication). An additional learned compression process further compresses latent communication via latent space reasoning. Experiments demonstrate that Interlat outperforms both fine-tuned chain-of-thought (CoT) prompting and single-agent baselines, even across heterogeneous models, promoting more exploratory behavior and enabling genuine utilization of latent information. Further compression not only substantially accelerates inference by up to 24 times but also maintains competitive performance through an efficient information-preserving mechanism. We position this work as a feasibility study of entirely latent space inter-agent communication, and our results highlight its potential, offering valuable insights for future research.

翻译：尽管自然语言是基于大语言模型（LLM）的智能体之间事实上的通信媒介，但它存在一个根本性限制。将丰富的内部潜在状态下采样为离散标记的过程，本质上限制了可传输信息的深度与细微差别，从而阻碍了协作问题解决。受心灵感应（绕过符号语言进行通信）的启发，我们提出了Interlat（智能体间潜在空间通信），这是一种利用LLM的连续最后隐藏状态作为其思维表征以进行直接通信（称为潜在通信）的范式。一个额外的学习压缩过程通过潜在空间推理进一步压缩潜在通信。实验表明，Interlat在性能上优于微调的思维链（CoT）提示和单智能体基线，即使在异构模型之间也是如此，它能促进更具探索性的行为，并实现潜在信息的真正利用。进一步的压缩不仅将推理速度大幅提升高达24倍，还通过高效的信息保留机制保持了有竞争力的性能。我们将此项工作定位为完全潜在空间智能体间通信的可行性研究，我们的结果凸显了其潜力，为未来研究提供了有价值的见解。