Beyond Aggregate - Using Information Theory to Forge True Teamwork in Multi-Agent LLMs
Ep. 02

Beyond Aggregate - Using Information Theory to Forge True Teamwork in Multi-Agent LLMs

Episode description

When does a group of AI agents stop being a simple “collection” and start acting like a true “team”? We talk a lot about “collective intelligence” in AI, but how can we even measure it? In this segment, we dive into a new paper that tackles this question head-on. Using a clever guessing game where AI agents can’t communicate, researchers test what it takes for them to spontaneously coordinate. We discuss their new framework for measuring synergy and the surprising discovery about what kind of prompting - like invoking a ‘Theory of Mind’ - is needed to create a team that’s truly greater than the sum of its parts.

Attribution

This podcast is based on the following research paper.

Title: Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models

Authors: Kai Zhang, Xiangchao Chen, Bo Liu, Tianci Xue, Zeyi Liao, et al.

Source: arXiv:2510.08558 [cs.LG]