Multi-LLM Agent Collaborative Intelligence: The Path to Artificial General Intelligence (Quantitative and AI Foundations in Perception and Consciousness)

By: Edward Y Chang

Multi-LLM Agent Collaborative Intelligence: The Path to AGI presents the MACI framework, a systems-level approach to achieving artificial general intelligence (AGI) through structured collaboration among large language models (LLMs). Instead of pursuing ever-larger models, the book argues that intelligence emerges when multiple LLMs coordinate through distinct roles, memory systems, and regulatory mechanisms. MACI is built on seven architectural foundations: Modeling Consciousness as Functional Regulation MACI does not attempt to replicate human consciousness, but it implements core regulatory functions such as goal prioritization, inhibition, and contextual modulation. This enables the system to filter impulsive outputs, suppress irrelevant reasoning paths, and adapt behavior according to ethical, contextual, and task-based constraints. Balancing Exploration and Exploitation MACI agents are designed to generate novel insights while remaining grounded in strong priors from pretraining. By combining generative creativity with statistical reliability, the system navigates between innovation and coherence, avoiding both hallucination and stagnation. Modulating Linguistic Behavior MACI dynamically adjusts tone, emotional framing, and argumentative posture to match the agent's assigned role and debate context. Contentious exchanges encourage breadth and perspective diversity, while conciliatory tones support convergence, synthesis, and resolution. Establishing Checks and Balances for Ethical Alignment Ethical reasoning is distributed across Executive (knowledge), Legislative (rules), and Judicial (interpretation) roles, modeled after institutional governance. This modular structure prevents ethical compromise in knowledge generation and supports contextual moral reasoning without retraining the foundational models. Integrating Persistent Memory Without memory beyond the context window, LLMs forget goals, assumptions, and constraints over time. MACI integrates the SAGA framework to preserve state, track dependencies, and enable long-horizon planning, rollback, and system-level coherence. Structuring Reasoning through the Socratic Method MACI agents engage in iterative questioning and challenge each other's assumptions, mirroring the structure of Socratic dialogue. The CRIT module evaluates each step for logical soundness, causal support, and credibility, ensuring that reasoning unfolds with rigor and transparency. Enabling Perspective Discovery and Polydisciplinary Synthesis MACI reframes reasoning not as a search for singular "truth" but as a process of surfacing well-supported perspectives across domains. LLMs' training across diverse texts enables unexpected synthesis, revealing insights that span fields such as medicine, philosophy, and engineering. The framework includes components such as CRIT, SocraSynth, EVINCE, SagaLLM, and the DIKE-ERIS model for ethical governance. These enable MACI to support reflective reasoning, maintain memory across sessions, align outputs to contextual ethics, and synthesize knowledge across domains. The book concludes with fourteen aphorisms that distill the philosophical and cognitive principles behind MACI, advancing the view that AGI will emerge not from isolated, ever-larger models, but from intelligent systems capable of reasoning, regulating, and collaborating effectively. Ultimately, AGI may not be the product of singular brilliance, but of systems designed for structured, synergistic collaboration--just as human progress has always advanced through shared insight, dialogue, and collective refinement.

• Format: Hardcover
• Language: English
• ISBN: B0F5NP83W6
• ISBN13: 9798280509467
• Release Date: April 2025
• Publisher: Independently published
• Length: 549 Pages
• Dimensions: 10.2" x 1.6" x 7.2"