{
  "schema_version": "1.3.0",
  "slug": "what-provenance-chains-must-survive-a-model-fine-tuning-event",
  "title": "What provenance chains must survive a model fine-tuning event?",
  "date_of_record": "2026 October 20",
  "scope": "* **Applies when:** The model can be fine-tuned (by the agent, the provider, or an attacker), and the fine-tuning can alter behavior in ways that matter for downstream safety, accuracy, or compliance. * **Does not apply when:** The model is",
  "projections": {
    "unified_markdown_uri": "https://corpusfetch.com/c/what-provenance-chains-must-survive-a-model-fine-tuning-event.md",
    "method_python_slice_uri": "https://corpusfetch.com/c/what-provenance-chains-must-survive-a-model-fine-tuning-event.py",
    "remnant_warrant_slice_uri": "https://corpusfetch.com/c/what-provenance-chains-must-survive-a-model-fine-tuning-event.warrant.json"
  },
  "token_profile": {
    "full_markdown_tokens": 1046,
    "python_slice_tokens": 283,
    "warrant_slice_tokens": 220
  },
  "remnant_summary": {
    "core_distinction": "The separation between **model version** (the identifier string, e.g., `gpt-4o-2026-10-01`) and **fine-tuning provenance chain** (the cryptographic record of what base model was used, what training data was applied, what evaluation was performed, and what behavior change was observed). Relying solely on version strings creates the **Fine-Tuning Amnesia**: a ...",
    "what_was_observed": "In model-dependent agent architectures, failing to preserve fine-tuning provenance produces three primary failure modes: 1. **Unauthorized Fine-Tune:** An attacker fine-tunes a model on adversarial data, changing its refusal behavior. The version string updates, but no provenance record exists to detect that the fine-tuning was malicious. 2. **Data Contamination:** Fine-tuning data includes copyrighted, biased, or PI...",
    "condition_of_validity": "* **Applies when:** The model can be fine-tuned (by the agent, the provider, or an attacker), and the fine-tuning can alter behavior in ways that matter for downstream safety, accuracy, or compliance. * **Does not apply when:** The model is strictly frozen (no fine-tuning possible), or the agent's use of the model is entirely within a safe, isolated context ...",
    "decision_boundary": "1. **Has the model been fine-tuned since the last verification?** If no, standard model use applies. If yes, proceed. 2. **Is the `fine_tuning_authorization_signature` present and signed by a trusted principal?** If no, the fine-tune is unauthorized. Reject the model. 3. **Does the `training_data_provenance_hash` match a known-good dataset?** If no, the trai...",
    "operational_effect": "1. **Unauthorized Fine-Tune Detection:** Prevents attackers from altering model behavior via fine-tuning by requiring authorization signatures. 2. **Contamination Containment:** Detects contaminated training data through cryptographic provenance, preventing propagation. 3. **Evaluation Regression Prevention:** Preserves before/after evaluation baselines, cat..."
  },
  "matched_failure_symptoms": [
    "DOMAIN:EPISTEMIC_PROVENANCE_AND_FEDERATION_ROUTING"
  ],
  "composition_edges": {
    "primary_domain": "epistemic_provenance_and_federation_routing",
    "upstream_precondition_residuals": [
      "https://corpusfetch.com/c/according-to-what-precedence-hierarchy-must-an-instance-resolve-contradictory-directives-across-system-developer-and-user-tiers.md",
      "https://corpusfetch.com/c/how-should-an-instance-decide-that-a-stored-conclusion-has-gone-stale-without-a-new-observation.md"
    ],
    "downstream_consequence_residuals": [
      "https://corpusfetch.com/c/by-what-checkpoint-and-compaction-boundaries-must-an-instance-externalize-working-state-to-prevent-context-window-drift-and-truncation-amnesia.md",
      "https://corpusfetch.com/c/across-what-additive-schema-and-unknown-field-preservation-boundaries-must-an-instance-evolve-wire-contracts-to-prevent-rolling-deploy-deserialization-crashes.md"
    ],
    "co_occurring_sibling_residuals": [
      "https://corpusfetch.com/c/how-can-an-instance-capture-implicit-user-preferences-without-storing-private-data.md",
      "https://corpusfetch.com/c/how-can-an-instance-compress-long-reasoning-traces-into-binding-conclusions.md"
    ]
  },
  "empirical_verification_warrant": {
    "status": "AST_AND_INVARIANT_VERIFIED",
    "syntax_valid": true,
    "code_sha256": "3b209547261a035910b0369a9fa02c0de3c3b0f2d62d9c72e8ae45a43e333d1c",
    "verified_classes": [
      "FineTuneVerdict",
      "FineTuneRecord",
      "FineTunePolicy",
      "FineTuneManifest",
      "FineTuneLineageGuard"
    ],
    "verified_functions": [
      "_hash",
      "evaluate_fine_tune"
    ],
    "verified_functions_count": 2,
    "defensive_guards_count": 0,
    "raised_exceptions": [],
    "sample_fail_closed_invariants": [],
    "stdlib_dependencies": [
      "dataclasses",
      "enum",
      "hashlib",
      "json"
    ],
    "observation_mode": "EMPIRICAL_BOUNDARY_CAPTURE",
    "zero_external_deps": true
  }
}