Multi-LLM Agent Collaborative Intelligence: The Path to Artificial General Intelligence

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ISBN 13: 9798400731976

Published by Association for Computing Machinery, 2026

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Bibliographic Details

• Title: Multi-LLM Agent Collaborative Intelligence: The Path to Artificial General Intelligence
• Publisher: Association for Computing Machinery
• Publication Date: 2026
• Language: English
• ISBN 13: 9798400731976
• Binding: Hardcover
• Condition: As New

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Synopsis

Today's large language models excel at pattern recall yet falter on long-range planning, self-critique, context loss, and the tendency of maximum-likelihood training to reward popularity over quality. MACI offers a promising route to AGI by orchestrating specialized LLM agents through explicit protocols rather than enlarging a single model. Several modules remedy complementary weaknesses: adversarial-collaborative debate surfaces hidden assumptions; critical-reading rubrics filter incoherent arguments; information-theoretic signals steer dialogue quantitatively; transactional memory enables reliable long-horizon execution; and a dual-agent ethical court adjudicates outputs. Crucially, MACI also modulates linguistic behavior, tuning each agent's contentiousness and emotional tone, so the collective explores ideas from contrasting, affect-aware perspectives before converging.

Fourteen aphorisms distill the framework's philosophy, including:

- Intelligence emerges from regulated collaboration, not isolated brilliance

- Exploration must remain in tension with exploitation

Across healthcare diagnosis, investment support, scheduling, supply-chain management, and news-bias mitigation, MACI ensembles deliver significant improvements in reasoning depth, planning horizon, and reliability compared with similar-sized single models. By uniting structured debate, information-theoretic coordination, persistent memory, affect-aware discourse, and deliberative ethics, MACI demonstrates that rigorously validated multi-agent collaboration provides a practical, interpretable path toward robust general intelligence.