Phionyx: A Deterministic AI Runtime Architecture with Structured State Management and Pre-Response Governance
Phionyx is a deterministic AI runtime architecture derived from the broader Echoism interaction framework, introducing a governance-first approach that treats LLM outputs as noisy sensor measurements. It enforces deterministic state evolution via a structured state vector, integrating three layers: a deterministic evaluation kernel, a unified safety layer, and a semantic time-based memory system. Experimental results show an approximately 31% reduction in computational overhead vs. post-hoc filtering and up to 24% improvement in high-value data retention vs. LRU, with deterministic execution verified over 100 runs.
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[Submitted on 4 May 2026]
Title:Phionyx: A Deterministic AI Runtime Architecture with Structured State Management and Pre-Response Governance
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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. 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.
Comments: 27 pages, 4 figures, 5 tables. Reference implementation, reproducibility pack, and evaluation artifacts available via GitHub (this https URL) and Zenodo (DOI: https://doi.org/10.5281/zenodo.20027534)
Subjects:
Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
ACM classes: I.2.0; D.2.4
Cite as: arXiv:2607.18246 [cs.AI]
(or arXiv:2607.18246v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.18246
arXiv-issued DOI via DataCite
Submission history
From: Ali Toygar Abak [view email] [v1] Mon, 4 May 2026 18:35:31 UTC (19 KB)
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