Adversarial Multi-Model Orchestration: A Protocol for Constraint-Based Scientific Discovery in Large Language Models

Large Language Models exhibit systematic failure modes—sycophantic drift, hallucination, and premature convergence—that compound catastrophically in interdisciplinary synthesis. We introduce Adversarial Multi-model Orchestration (AMO), an architectural protocol that addresses these limitations through structured, role-locked interaction. AMO assigns independent LLM instances to specialized adversarial roles, engineering diagnostic friction into the synthesis process. Stability in this high-entropy latent space is achieved through an emergent structure that behaves like a six-axis orthogonal geometry, where failure along any single dimension is isolated, preventing global divergence. Central to this architecture is the Human Orchestrator, functioning as the Executive Epistemic Controller. The Orchestrator acts as the high-level regulator of the manifold: interpreting friction signals, pruning invalid trajectories, and maintaining global coherence through strategic constraint injection. We demonstrate AMO’s efficacy through the resolution of the 2004 USS Nimitz UAP incident. By forcing a multi-model committee to reconcile heterogeneous data against physical conservation laws, the system converged on a novel, substrate-independent synthesis—Plasma Pareidolia. This work suggests that Artificial General Intelligence (AGI) may not require new architecture, but rather a new architecture of interaction. This work forms part of a broader research program examining how rendering, constraint, and convergence emerge in uncertain physical, cognitive, and social systems. Research Program Invitation - https://grodriguez6.github.io/amo-collab