Enabling Agents to Communicate Entirely in Latent Space

The introduction of Interlat marks a significant advancement in AI communication, enabling agents to interact in latent space rather than relying on natural language, which often constrains the depth of information exchange. By leveraging the last hidden states of language models, Interlat facilitates a more nuanced and efficient communication method. Experiments indicate that this approach not only outperforms traditional fine-tuned chain-of-thought prompting but also enhances collaborative problem-solving capabilities. The additional compression process accelerates inference while preserving performance, highlighting the method's efficiency. These findings underscore the potential of latent communication to foster exploratory behavior among agents, paving the way for future research in AI inter-agent communication.