Phionyx — Runtime Evidence & Measurement
Papers, datasets and runnable labs for verifiable AI runtime governance, evidence and measurement.
Paper • 2608.21363 • PublishedNote Protocol for signed, per-decision evidence in AI runtime governance.
phionyx/airep-embedded-evaluation-profile
Viewer • Updated • 1 • 93Note Distribution mirror of the experimental AIREP Embedded Evaluation Profile v0.1 — not a benchmark or training dataset. An evidence contract for evaluator identity, target/access/run provenance, explicit NOT_RUN/NOT_MEASURED states, evidence digests, disclosures and verification references. Carrier: AIREP 0.2. Profile validation is not an assurance result and does not certify safety or evaluator independence.
AIREP Evidence Lab
🔬Interactive verification lab for AIREP runtime evidence.
Note Runnable laboratory for inspecting what signed runtime evidence does—and does not—establish.
phionyx/airep-evidence-cases
Viewer • Updated • 17 • 52Note Reusable AIREP evidence, verifier and negative test cases for runtime-governance record verification.
AIREP Evaluation Evidence
🧾LightEval results → AIREP evaluation evidence exporter
Note Interactive LightEval results → AIREP Embedded Evaluation Profile exporter (current parser: LightEval results_*.json; Inspect/OpenEvals are future targets, not parsed). Produces a profile payload and content-addressed evidence manifest; declared context is recorded, not verified; establishes only digests and schema validation — not evaluation truth, safety or independence.
Phionyx: A Deterministic AI Runtime Architecture with Structured State Management and Pre-Response Governance
Paper • 2607.18246 • PublishedNote Deterministic AI runtime architecture with structured state and pre-response governance.
Measurement Axioms Lab
🔬Scoped measurement checks with frozen reference evidence
Note Runnable lab for separating measurement validity, non-measurement and NOT_MEASURED outcomes.
phionyx/measurement-axioms-cases
Viewer • Updated • 73 • 89Note Source-pinned cases and reference runs that keep expected and observed measurement outcomes separate.