AI 中文总结
研究智能分布式系统中确定性逐比特复制不足的问题，提出认知状态复制（ESR）方法，将复制边界从数据可见性转移到知识可见性，定义相关安全性概念并给出协议，通过原型设计和模拟验证了可行性及能减少二次认知故障。
AI 中文摘要
在分布式系统中，经典的状态机复制（SMR）模型假设正确的副本执行确定性转换以产生相同的逐比特状态。然而，智能分布式系统的兴起——其中自主、随机和模型驱动的智能体编排基础设施——带来了确定性逐比特复制不足的场景。使用生成模型运行的副本可能表现出不同的推理路径、摘要和令牌边界，但能达成语义等效且正确的操作决策。强制这些随机参与者逐比特一致会降低执行灵活性、导致上下文失忆并限制性能。我们认为在这种情况下，副本应在信念上达成一致，而非比特。我们提出了认知状态复制（ESR），这是一种用于智能分布式系统的信念复制层，它将复制边界从数据可见性转移到知识可见性。我们将认知节点状态形式化为一对K = (L, B)，将确定性、不可变的证据日志（L）与随机、不断演变的信念谱系（B）分开。为了控制执行安全性，我们定义了语义线性izability，它要求操作在验证器界定的语义兼容性度量内反映最新提交的操作含义，以及有界最终一致性，它在公平交付、单调证据、有界验证器干扰和收缩嫁接算子下界定预期的语义差异。我们概述了使用结构化认知增量传播派生见解的协议，并形式化了可验证语义回滚，以从信念谱系中修剪错误前提而不引起上下文失忆。我们对ESR进行了原型设计，并报告了初步模拟结果，这些结果表明在所陈述的假设下是可行的，并说明了二次认知故障的减少。
英文摘要
In distributed systems, the classical State Machine Replication (SMR) model assumes that correct replicas execute deterministic transitions to yield identical bitwise states. However, the rise of agentic distributed systems -- where autonomous, stochastic, and model-driven agents orchestrate infrastructure -- presents scenarios where deterministic, bitwise replication is insufficient. Replicas operating with generative models may exhibit divergent reasoning paths, summaries, and token boundaries, yet reach semantically equivalent and correct operational decisions. Forcing bitwise agreement across these stochastic participants degrades execution flexibility, induces context amnesia, and limits performance. We argue that in such settings replicas should agree on belief, not bits. We propose Epistemic State Replication (ESR), a belief-replication layer for agentic distributed systems that shifts the replication boundary from data visibility to knowledge visibility. We formalize the epistemic node state as a pair K = (L, B) separating the deterministic, immutable evidence log (L) from the stochastic, evolving belief lineage (B). To govern execution safety, we define Semantic Linearizability, which requires operations to reflect the latest committed operational meaning within a verifier-bounded semantic compatibility metric, and Bounded Eventual Coherence, which bounds expected semantic divergence under fair delivery, monotonic evidence, bounded verifier disturbance, and a contractive graft operator. We outline protocols for propagating derived insights using structured epistemic deltas, and formalize Verifiable Semantic Rollbacks to prune faulty premises from belief lineages without inducing context amnesia. We prototype ESR and report preliminary simulation results that show feasibility under the stated assumptions and illustrate reductions in secondary cognitive faults.
Comments16 pages, 4 tables
