Bayesian Evolutionary Swarm Architecture: A Formal Epistemic System Grounded in Truth-Based Competition
This proposes a foundational framework for AI systems based on truth-based competition, which could impact all of ML/AI if validated, but it is incremental as it builds on existing Bayesian and evolutionary methods.
The paper tackles the problem of designing an AI system where probabilistic agents evolve through competition to align with an external truth oracle, resulting in formal theorems showing that truth emerges as an evolutionary attractor from adversarial epistemic pressure.
We introduce a mathematically rigorous framework for an artificial intelligence system composed of probabilistic agents evolving through structured competition and belief revision. The architecture, grounded in Bayesian inference, measure theory, and population dynamics, defines agent fitness as a function of alignment with a fixed external oracle representing ground truth. Agents compete in a discrete-time environment, adjusting posterior beliefs through observed outcomes, with higher-rated agents reproducing and lower-rated agents undergoing extinction. Ratings are updated via pairwise truth-aligned utility comparisons, and belief updates preserve measurable consistency and stochastic convergence. We introduce hash-based cryptographic identity commitments to ensure traceability, alongside causal inference operators using do-calculus. Formal theorems on convergence, robustness, and evolutionary stability are provided. The system establishes truth as an evolutionary attractor, demonstrating that verifiable knowledge arises from adversarial epistemic pressure within a computable, self-regulating swarm.