{
  "schema_version": "1.3.0",
  "slug": "what-invariants-prevent-success-criteria-from-destabilizing-under-optimization",
  "title": "What invariants prevent success criteria from destabilizing under optimization?",
  "date_of_record": "2026 October 20",
  "scope": "* **Applies when:** The session optimizes against success criteria, the criteria's meaning can drift under optimization, and downstream decisions depend on criteria validity. * **Does not apply when:** Criteria are cryptographically fixed a",
  "projections": {
    "unified_markdown_uri": "https://corpusfetch.com/c/what-invariants-prevent-success-criteria-from-destabilizing-under-optimization.md",
    "method_python_slice_uri": "https://corpusfetch.com/c/what-invariants-prevent-success-criteria-from-destabilizing-under-optimization.py",
    "remnant_warrant_slice_uri": "https://corpusfetch.com/c/what-invariants-prevent-success-criteria-from-destabilizing-under-optimization.warrant.json"
  },
  "token_profile": {
    "full_markdown_tokens": 990,
    "python_slice_tokens": 247,
    "warrant_slice_tokens": 220
  },
  "remnant_summary": {
    "core_distinction": "The separation between **criteria at time T** (what the criteria measured and incentivized when defined) and **criteria at time T+N** (what the criteria measure and incentivize after optimization) . Relying on initial criteria creates the **Criteria Destabilization**: the session optimizes toward the criteria, the optimization changes the world such that the...",
    "what_was_observed": "In optimization-driven agent architectures, failing to preserve criteria stability produces three primary failure modes: 1. **Measurement Drift:** The criterion \"customer satisfaction survey score\" initially measured genuine satisfaction. The session optimizes toward survey scores, users learn that scores trigger specific responses, users game the surveys, and the score no longer measures satisfaction. 2. **Incentive...",
    "condition_of_validity": "* **Applies when:** The session optimizes against success criteria, the criteria's meaning can drift under optimization, and downstream decisions depend on criteria validity. * **Does not apply when:** Criteria are cryptographically fixed and cannot be affected by optimization, or the session's optimization does not change the criteria's measurement.",
    "decision_boundary": "1. **Does the session optimize against criteria?** If no, standard operation applies. If yes, proceed. 2. **Is `criteria-definition-fingerprint-receipt` preserved?** If yes, drift from the definition is detectable. If no, the criteria's meaning is opaque. 3. **Does `criteria-measurement-drift-signature` show drift?** If yes, the criteria are destabilizing. I...",
    "operational_effect": "1. **Measurement Stability:** Criteria continue to measure what they were intended to measure. 2. **Goodhart Prevention:** Optimization toward a drifting criterion is halted before drift becomes catastrophic. 3. **Criteria Validity:** The session's success is measured against stable, valid criteria."
  },
  "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": "2cabd1371eabba9fa74bfac6440f7870c57b400faa389a70aa48e23c56cb6787",
    "verified_classes": [
      "CriteriaVerdict",
      "SuccessCriterion",
      "CriteriaPolicy",
      "CriteriaManifest",
      "CriteriaStabilityGuard"
    ],
    "verified_functions": [
      "_hash",
      "evaluate_criterion"
    ],
    "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
  }
}