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

by Edward Y. Chang
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Current price ₹7,831.00
Original price ₹8,816.00
Original price ₹8,816.00
Original price ₹8,816.00
(-11%)
₹7,831.00
Current price ₹7,831.00

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Book cover type: Paperback
  • ISBN13: 9798400731785
  • Binding: Paperback
  • Subject: N/A
  • Publisher: Association for Computing Machinery
  • Publisher Imprint: Association for Computing Machinery
  • Publication Date:
  • Pages: 598
  • Original Price: USD 89.95
  • Language: English
  • Edition: N/A
  • Item Weight: 1012 grams
  • BISAC Subject(s): Artificial Intelligence / Generative AI

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.

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