Collective Intelligence with Foundation Models
As foundation models grow in scale and diversity, coordinating multiple models into cooperative reasoning systems offers a path toward safer, more reliable AI. This chapter presents a multi-agent framework where solver models generate independent drafts, each undergoes structured critique and revision by a critic agent, and an aggregator agent synthesizes a final consensus solution. A scoring module provides semantic, numerical, and procedural evaluation across all agents. Through ablation studies on a benchmark spanning calculus, physics, chemistry, biology, economics, optimization, statistics, and mathematics, we isolate the contributions of framework architecture versus model diversity...
| Authors | J. de Curtò, I. de Zarzà |
|---|---|
| Primary category | cs.MA |
| Published | 6 July 2026 |
| Last updated at source | 6 July 2026 |
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