Society of Mind and Multi-Agent Debate

Minsky's 1986 premise — intelligence is a society of specialists — gets rediscovered every decade. In 2023 Du et al. turned it into a concrete algorithm: multiple LLM instances propose answers, read each other's answers, critique, and update. Over N rounds they converge on a consensus that beats zero-shot CoT and reflection on six reasoning and factuality tasks. Two findings matter: both **multiple agents** and **multiple rounds** contribute independently. The society beats a single-agent monologue; the multi-round exchange beats one-shot voting.

Learn + Build 1 hr Python (stdlib)

Prerequisites: Phase 16 · 04 (Primitive Model)

#Problem

Self-consistency — sample one model many times and take the majority answer — is the cheapest reasoning improvement you can bolt on. It works, but it saturates fast. You can double your samples and not see another meaningful jump.

Debate breaks the saturation. Instead of N independent samples from one model, N agents read each other's reasoning and revise. The correlation between samples drops (they are no longer i.i.d.), and the convergence point is often correct where i.i.d. voting was confidently wrong.

#Problem

Keep going. The rest of this lesson is one step away.