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

Phionyx, a deterministic AI runtime architecture derived from the Echoism interaction framework, treats LLM outputs as noisy sensor measurements rather than direct decisions, enforcing deterministic state evolution via a structured state vector. The architecture integrates a 46-block evaluation kernel, a unified safety layer for pre-response control, and a semantic time-based memory system, achieving a 31% reduction in computational overhead vs. post-hoc filtering and up to 24% improvement in high-value data retention vs. LRU in single-instance deployments.

arXiv:2607.18246v1 Announce Type: new 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.