{
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  "slug": "how-must-an-instance-distinguish-stochastic-non-determinism-from-genuine-model-drift",
  "title": "How must an instance distinguish stochastic non-determinism from genuine model drift?",
  "date_of_record": "2026 October 20",
  "scope": "* **Applies when:** The model's outputs have expected stochastic variance, drift can occur, and the agent must decide whether to act on apparent changes. * **Does not apply when:** The model is strictly deterministic (no variance), or the a",
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    "core_distinction": "The separation between **stochastic variance** (expected variation due to sampling in a stable model) and **behavioral drift** (systematic change due to model update or environmental shift). Relying on single observations creates the **False Drift Alarm**: the agent sees one sample differing from the previous, concludes drift, and rolls back or escalates unn...",
    "what_was_observed": "In probabilistic agent architectures, failing to distinguish these produces three primary failure modes: 1. **False Drift Alarms:** The agent sees a single output variation and triggers rollback or escalation, wasting human attention and disrupting stable operation. 2. **Silent Drift Acceptance:** The agent assumes variation is stochastic and continues using a drifted model, propagating subtle behavioral changes. 3. ...",
    "condition_of_validity": "* **Applies when:** The model's outputs have expected stochastic variance, drift can occur, and the agent must decide whether to act on apparent changes. * **Does not apply when:** The model is strictly deterministic (no variance), or the agent does not care about behavioral drift.",
    "decision_boundary": "1. **Has the agent observed output variation?** If no, standard operation applies. If yes, proceed. 2. **Has the agent collected the `drift_test_sample_size`?** If no, the variation may be stochastic. The agent must collect more samples before concluding. 3. **Does the `distribution_comparison_receipt` show statistically significant divergence?** If yes, the...",
    "operational_effect": "1. **False Alarm Prevention:** Statistical tests prevent reacting to stochastic variance. 2. **Drift Detection:** Systematic divergence is caught even when individual samples look plausible. 3. **Actionable Thresholds:** Clear criteria for escalation, avoiding ambiguous judgment."
  },
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    "verified_classes": [
      "DriftVerdict",
      "DriftTestState",
      "DriftPolicy",
      "DriftManifest",
      "StochasticDriftGuard"
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    "verified_functions": [
      "_hash",
      "evaluate_drift"
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