Phionyx runtime slashes AI overhead 31% with deterministic governance

Phionyx introduces a paradigm shift in AI engineering by treating large language model (LLM) outputs as noisy sensor measurements rather than authoritative decisions. The architecture enforces deterministic state evolution via a structured state vector governed by deterministic equations, enabling reproducible behavior critical for auditability and governance. Its three-layer design includes a 46-block deterministic evaluation kernel, a unified safety layer for pre-response control and privacy enforcement, and a semantic time-based memory system with impact-weighted cache eviction. This governance-first approach ensures that every step of the AI's decision process is predictable and verifiable.

Experimental validation on single-instance deployments demonstrates a 31% reduction in computational overhead compared to post-hoc filtering (at 30% unsafe input ratio), a 24% improvement in high-value data retention over LRU (72% vs. FIFO at same cache capacity), and perfect determinism with zero variance in control signals across 100 repeated runs (hash-verified). Additionally, zero unplanned restarts were recorded during testing. These results position Phionyx as a robust foundation for building reliable, auditable AI systems in regulated environments like finance and healthcare. The reference implementation and reproducibility artifacts are publicly available on GitHub and Zenodo.