Phionyx: A Deterministic AI Runtime Architecture with Structured State Management and Pre-Response Governance

Phionyx：一种具有结构化状态管理和响应前治理的确定性 AI 运行时架构

Abstract: We present Phionyx, a deterministic AI runtime architecture derived from the broader Echoism interaction framework that introduces a governance-first approach to AI engineering: treating large language model (LLM) outputs as noisy sensor measurements rather than direct decisions.

摘要： 我们提出了 Phionyx，这是一种源自更广泛的 Echoism 交互框架的确定性 AI 运行时架构。它引入了一种“治理优先”的 AI 工程方法：将大语言模型（LLM）的输出视为带有噪声的传感器测量值，而非直接的决策结果。

Unlike probabilistic agents, Phionyx enforces deterministic state evolution via a structured state vector governed by deterministic state-evolution equations, enabling reproducible behavior in applications requiring auditability and governance.

与概率代理不同，Phionyx 通过由确定性状态演化方程控制的结构化状态向量，强制执行确定性的状态演化，从而在需要可审计性和治理的应用中实现可复现的行为。

The architecture integrates three layers: (1) a deterministic evaluation kernel processing noisy sensor measurements through a canonical 46-block pipeline, (2) a unified safety layer providing pre-response control and architectural privacy enforcement, and (3) a semantic time-based memory system implementing impact-weighted cache eviction.

该架构集成了三个层级：(1) 一个通过规范的 46 块流水线处理噪声传感器测量值的确定性评估内核；(2) 一个提供响应前控制和架构隐私强制执行的统一安全层；以及 (3) 一个实现影响加权缓存淘汰的语义时间基内存系统。

Experimental validation on single-instance deployments demonstrates approximately 31% reduction in computational overhead vs. post-hoc filtering (at 30% unsafe input ratio, simulated cost model) and up to 24% improvement in high-value data retention vs. LRU (72% vs. FIFO, same cache capacity, benchmark-verified), deterministic execution verified across 100 repeated runs with zero variance in control signals (hash-verified), and zero unplanned restarts in single-instance deployment testing (see Appendix C for methodology and scope).

在单实例部署上的实验验证表明，与事后过滤相比，计算开销降低了约 31%（在 30% 不安全输入比例的模拟成本模型下）；高价值数据保留率比 LRU 提高了高达 24%（72% 对比 FIFO，相同缓存容量，经基准测试验证）；在 100 次重复运行中验证了确定性执行，控制信号零偏差（经哈希验证）；且在单实例部署测试中实现了零意外重启（方法论和范围详见附录 C）。

This paper presents the architecture, its analytic structure, and scoped experimental evidence; generalization to distributed or multi-tenant deployments remains future work.

本文介绍了该架构、其分析结构以及范围限定的实验证据；向分布式或多租户部署的推广仍是未来的研究方向。