# Learning to Communicate Through Implicit Communication Channels

arXiv: https://arxiv.org/abs/2411.01553 · HTML: https://mlsanity.com/paper/2411.01553/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2024.

## Built on by (most cited first, 7 of 7)

- [Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems](https://mlsanity.com/paper/2504.01990/) (2025)
- [Why LLMs Aren't Scientists Yet: Lessons from Four Autonomous Research Attempts](https://mlsanity.com/paper/2601.03315/) (2026)
- [Decentralized Collective World Model for Emergent Communication and Coordination](https://mlsanity.com/paper/2504.03353/) (2025)
- [Unifying Agent Interaction and World Information for Multi-agent Coordination](https://mlsanity.com/paper/2509.25550/) (2025)
- [From Grunts to Lexicons: Emergent Language from Cooperative Foraging](https://mlsanity.com/paper/2505.12872/) (2025)
- [Harnessing Implicit Cooperation: A Multi-Agent Reinforcement Learning Approach Towards Decentralized Local Energy Markets](https://mlsanity.com/paper/2602.16062/) (2026)
- [Multi-Agent Systems: From Classical Paradigms to Large Foundation Model-Enabled Futures](https://mlsanity.com/paper/2604.18133/) (2026)

## Abstract (arXiv)

Effective communication is an essential component in collaborative multi-agent systems. Situations where explicit messaging is not feasible have been common in human society throughout history, which motivate the study of implicit communication. Previous works on learning implicit communication mostly rely on theory of mind (ToM), where agents infer the mental states and intentions of others by interpreting their actions. However, ToM-based methods become less effective in making accurate inferences in complex tasks. In this work, we propose the Implicit Channel Protocol (ICP) framework, which allows agents to communicate through implicit communication channels similar to the explicit ones. ICP leverages a subset of actions, denoted as the scouting actions, and a mapping between information and these scouting actions that encodes and decodes the messages. We propose training algorithms for agents to message and act, including learning with a randomly initialized information map and with a delayed information map. The efficacy of ICP has been tested on the tasks of Guessing Numbers, Revealing Goals, and Hanabi, where ICP significantly outperforms baseline methods through more efficient information transmission.