Edward Y. Chang

Gebundenes Buch

Multi-LLM Agent Collaborative Intelligence

The Path to AGU

Beschreibung

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.

Produktdetails

• Verlag: Association for Computing Machinery
• Seitenzahl: 556
• Erscheinungstermin: 18. November 2025
• Englisch
• Abmessung: 241mm x 196mm x 34mm
• Gewicht: 1219g
• ISBN-13: 9798400731792
• Artikelnr.: 75982791