Multi-LLM Agent Collaborative Intelligence

<p><strong>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.</strong> 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.</p><p></p><p>Fourteen aphorisms distill the framework's philosophy including:</p><p></p><p>• Intelligence emerges from regulated collaboration not isolated brilliance</p><p>• Exploration must remain in tension with exploitation</p><p></p><p>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.</p><p></p>