发表机构
Phionyx Research（Phionyx研究公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究提出Phionyx确定性人工智能运行时架构，源于Echoism框架，以治理为先。通过结构化状态向量实现确定性状态演化，集成三层架构。实验验证其在单实例部署中可降低计算开销、提高数据保留率，还介绍了架构及实验证据，分布式或多租户部署待后续研究。
AI 中文摘要
我们提出了Phionyx，这是一种源自更广泛的Echoism交互框架的确定性人工智能运行时架构，它引入了一种以治理为先的人工智能工程方法：将大语言模型（LLM）的输出视为有噪声的传感器测量值，而非直接决策。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来强制实现确定性状态演化，从而在需要可审计性和治理的应用中实现可重复行为。该架构集成了三层：（1）一个确定性评估内核，通过一个标准的46块管道处理有噪声的传感器测量值；（2）一个统一的安全层，提供响应前控制和架构隐私保护；（3）一个基于语义时间的内存系统，实现影响加权缓存逐出。单实例部署的实验验证表明，与事后过滤相比，计算开销降低了约31%（在30%不安全输入比率下，模拟成本模型），与LRU相比，高价值数据保留率提高了24%（72%对72%，相同缓存容量，基准验证）。本文介绍了该架构、其分析结构和有范围的实验证据；向分布式或多租户部署的推广仍是未来的工作。
英文摘要
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. 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. 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. 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). This paper presents the architecture, its analytic structure, and scoped experimental evidence; generalization to distributed or multi-tenant deployments remains future work.
Comments27 pages, 4 figures, 5 tables. Reference implementation, reproducibility pack, and evaluation artifacts available via GitHub (https://github.com/halvrenofviryel/phionyx-research) and Zenodo (DOI: 10.5281/zenodo.20027534)
