Bayesian Evolutionary Swarm Architecture: A Formal Epistemic System Grounded in Truth-Based Competition

TL;DR

This paper presents a rigorous Bayesian framework for an AI system where probabilistic agents evolve through structured competition and belief revision, aiming to converge towards truth via evolutionary dynamics.

Contribution

It introduces a formal, mathematically grounded architecture combining Bayesian inference, cryptographic identities, and causal inference to model truth-seeking agent evolution.

Findings

Proves convergence and stability of the belief evolution process.

Demonstrates truth as an evolutionary attractor.

Ensures traceability and robustness through cryptographic commitments.

Abstract

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.