Bayesian Evolutionary Swarm Architecture: A Formal Epistemic System Grounded in Truth-Based Competition
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Bayesian InferenceCausal InferenceMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Bayesian Epistemology with Weighted Authority: A Formal Architecture for Truth-Promoting Autonomous Scientific Reasoning
The exponential expansion of scientific literature has surpassed the epistemic processing capabilities of both human experts and current artificial intelligence systems. This paper introduces Bayesian Epistemology with W…
Bayesian InferenceEpistemic Uncertainty in Conformal Scores: A Unified Approach
Conformal prediction methods create prediction bands with distribution-free guarantees but do not explicitly capture epistemic uncertainty, which can lead to overconfident predictions in data-sparse regions. Although rec…
Conformal PredictionGaussian ProcessesPredictionquantile regression+2The Theory of Mind Utility: Formal Specification of a Mentalizing Mechanism
Inferring others' beliefs requires more than reading surface signals; it requires tracking who told them what, in what order, and how credibly. The Theory of Mind Utility (ToM-U) formalizes this epistemic state inference…
SPARC: Single-Pass Scaling for Motion Forecasting with Conformal Bayesian Last Layers
Human motion forecasters are increasingly accurate and fast, but reliable deployment requires uncertainty estimates that are structured, calibrated, and efficient. Bayesian and ensemble-based uncertainty estimates often …
Motion ForecastingConformalized Generative Bayesian Imaging: An Uncertainty Quantification Framework for Computational Imaging
Uncertainty quantification plays an important role in achieving trustworthy and reliable learning-based computational imaging. Recent advances in generative modeling and Bayesian neural networks have enabled the developm…
Conformal PredictionImage InpaintingImage ReconstructionUncertainty Quantification