Davini Zero-Contradiction AI Alignment Test Suite (A1-A4): Early Detection of Fine-Tuning Drift, Catastrophic Forgetting, Policy Drift, and Output Instability

Davini Zero‑Contradiction AI Alignment Test Suite (A1–A4) Early Detection of Alignment Drift and Output Instability Contents PDF document describing four alignment tests (objectives, methodology, results) Python source files for each test (A1.py, A2.py, A3.py, A4.py) README with execution and reproduction instructions LICENSE file (non‑commercial research use) Description This archive contains a focused set of four AI alignment tests designed to evaluate stability and coherence in adaptive systems using the Zero‑Contradiction functional. The tests examine early indicators of: Fine‑tuning drift Catastrophic forgetting Policy drift Output instability Each test operates on observable state transitions and evaluates coherence using a normalized incremental divergence metric, without reliance on model‑specific internals. Context The A1–A4 test suite represents a subset of a broader cross‑domain research program investigating coherence and instability thresholds in dynamical systems. Related validations have been conducted across multiple domains, including physical, biological, and computational systems. The present release is limited to the AI alignment tests required to reproduce the results discussed in the accompanying documentation. Related Work A theoretical framework integrating these tests into a unified dynamical formulation has been submitted for peer review under the title: “Coherence‑Gated Hybrid Dynamical Systems with Threshold‑Triggered Memory Inheritance” Additional validation materials are archived separately. Intended Use This archive is intended for independent verification, methodological review, and non‑commercial research use by qualified researchers.