Multi-LLM Agent Collaborative Intelligence: The Path to Artificial General Intelligence
Author(s): Edward Y. Chang(Collection III)
Multi-LLM Agent Collaborative Intelligence (MACI) advances a simple thesis: general intelligence will arise from orchestrated cooperation among specialized language-model agents, not from ever-larger monoliths. Over five years of research we have layered eight capabilities, reflective reasoning, critical self-evaluation, structured debate, information-theoretic dialogue control, affect-aware style modulation, cultural-ethical adjudication, iterative "think-validate" loops, and transactional long-horizon planning, into one cohesive architecture with shared memory and rollback. Fourteen design aphorisms distil the core insight: intelligence emerges from regulated collaboration, balancing exploratory disagreement with convergent synthesis. Experiments in planning, clinical diagnosis, and policy analysis show that MACI ensembles consistently exceed single-model baselines in reasoning depth, planning horizon, and ethical reliability, without adding parameters. Orchestrated multi-agent systems, rigorously validated at each step, offer a practical, interpretable path toward robust AGI.
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