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

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Edward Y Chang

Multi-LLM Agent Collaborative Intelligence

Name: Multi-LLM Agent Collaborative Intelligence
Price: 176.25 AUD
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ISBN: 9798400731976

The Path to Artificial General Intelligence

Edward Y Chang

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English

Association for Computing Machinery
12 January 2026

Expert & knowledge-based systems; Natural language & machine translation

Series: ACM Books

Summary
Details

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.

By: Edward Y Chang
Imprint: Association for Computing Machinery
Dimensions: Height: 235mm, Width: 191mm, Spine: 33mm
Weight: 1.234kg
ISBN: 9798400731976
Series: ACM Books
Pages: 598
Publication Date: 12 January 2026
Audience: General/trade , ELT Advanced
Format: Hardback
Publisher's Status: Active