---
title: "Multi-Agent LLMs"
type: concept
source: https://www.jemoka.com/posts/kbhmulti_agent_llms/
confidence: high
status: active
---

Background originally, multi-agent team pre-assigns roles LLMs are heterogeneous, but they are treated homogeneously problem decomposition is hard Eval synergy weak synergy: team &gt;= average member strong synergy: team &gt;= best member Human teams reliably achieve strong synergy IFF when expert identity is given (e.g., the teams easily know who is the expert).
Dataset NASA moon survival / lost at seay Rank 15 items by importance
Student body president Different people are given different information + shared info. Hidden-profile (shared info + unique info must be paired to reveal the right one.)
Protocols LMs evolutionarily optimize collaboration protocol use it to solve problems with standards etc.
