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

Edward Y. Chang

ISBN 13: 9798400731976

Published by Association for Computing Machinery, 2026

About this Item

Multi-LLM Agent Collaborative Intelligence | The Path to Artificial General Intelligence | Edward Y. Chang | Buch | Englisch | 2026 | Association for Computing Machinery | EAN 9798400731976 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Seller Inventory # 134464069

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: Buch
• Condition: Neu
• Dimensions: 241W x 196H x 36D millimeters 241 millimeters width by 196 millimeters height by 36 millimeters depth
• Weight: 1295 grams
• Seller catalogs:
  • Bücher

About this title

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.