Adjudicative Neural Architectures: Learning via Internal Opposition, Argument Dynamics, and Procedural Stabilisation

This work proposes an alternative internal architecture for artificial intelligence systems in which reasoning is governed not solely by optimization, reward, or gradient descent, but by procedural adjudication under structured internal opposition. Rather than treating disagreement, uncertainty, or contradiction as training noise to be eliminated, the framework formalizes them as functional components of reasoning. Internal agents—implemented as roles rather than identities—advance, defend, attack, and stress-test competing arguments. Outcomes stabilize through procedural exhaustion, cost, and precedent, not through numerical convergence alone. The architecture is motivated by an analogy to judicial and institutional processes: truth is not assumed to emerge from correctness signals, but from sustained survivability under adversarial scrutiny. Importantly, opposition may be deliberately induced, including the strategic introduction of false or adversarial claims, provided such interventions are procedurally constrained and accountable. The document develops: a formal vocabulary for arguments as first-class objects, mechanisms for stability, termination, and verdict formation, procedures for precedent formation and compression, methods for handling frontier cases where no prior “law” exists, safeguards against collusion, corruption, and cheap stabilization, and multiple computational instantiations, including neural embeddings and hybrid governance layers. This work does not claim performance improvements on existing benchmarks. Instead, it introduces a missing architectural dimension—internal governance—intended to complement, not replace, existing learning paradigms. The framework is relevant to research on alignment, robustness, interpretability, long-horizon reasoning, scientific discovery systems, and AI safety. The contribution is conceptual and architectural in nature, offering a mechanically specified foundation for future empirical and theoretical work.