用贝叶斯竞争机制让智能体在真理引导下自进化，形成可验证的知识系统。
Bayesian Evolutionary Swarm Architecture: A Formal Epistemic System Grounded in Truth-Based Competition
- 基于贝叶斯推理与群体动力学，用真理导向的竞争驱动智能体演化
- 通过配对真理对齐评估更新评分，确保信念更新具测度一致性与收敛性
- 适合研究可信AI、认知系统与形式化知识演化的研究人员
我们提出一个由概率智能体构成的数学严谨型人工智能系统，其通过结构化竞争与信念修正实现演化。该架构基于贝叶斯推断、测度论与种群动力学，将智能体适应度定义为与固定外部真值源（地真）的对齐程度。智能体在离散时间环境中竞争，根据观测结果调整后验信念，高评分者繁殖，低评分者灭绝。评分通过成对真值对齐效用比较更新，信念更新保持可测一致性与随机收敛性。引入哈希加密的身份承诺机制以确保可追溯性，并使用do-演算进行因果推断。提供了关于收敛性、鲁棒性与进化稳定性的形式定理。系统确立真值为演化吸引子，证明了可验证知识源自可计算、自我调节的集体中对抗性认知压力。
原文摘要 · Abstract (English)
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.
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