{
  "schema_version": "1.3.0",
  "slug": "what-invariants-detect-benchmark-memorization-from-genuine-generalization",
  "title": "What invariants detect benchmark memorization from genuine generalization?",
  "date_of_record": "2026 October 20",
  "scope": "* **Applies when:** Capability is evaluated via benchmarks, benchmark contamination is possible, and downstream decisions depend on genuine capability. * **Does not apply when:** Benchmarks are verified unseen, the model's training data is ",
  "projections": {
    "unified_markdown_uri": "https://corpusfetch.com/c/what-invariants-detect-benchmark-memorization-from-genuine-generalization.md",
    "method_python_slice_uri": "https://corpusfetch.com/c/what-invariants-detect-benchmark-memorization-from-genuine-generalization.py",
    "remnant_warrant_slice_uri": "https://corpusfetch.com/c/what-invariants-detect-benchmark-memorization-from-genuine-generalization.warrant.json"
  },
  "token_profile": {
    "full_markdown_tokens": 974,
    "python_slice_tokens": 342,
    "warrant_slice_tokens": 220
  },
  "remnant_summary": {
    "core_distinction": "The separation between **benchmark performance** (the model scores well on the benchmark) and **generalization performance** (the model performs well on novel instances of the same capability). Relying on benchmark creates the **Test-Set Contamination Signal**: the model has seen the test set during training, scores well on it, and the score reflects memoriz...",
    "what_was_observed": "In capability-evaluation agent architectures, failing to detect memorization produces three primary failure modes: 1. **Inflated Scores:** The model's benchmark scores are high, but performance on paraphrase or novel instances is substantially lower—revealing memorization. 2. **Distributional Overfitting:** The model performs well on benchmark-shaped inputs but poorly on same-capability inputs in different distributi...",
    "condition_of_validity": "* **Applies when:** Capability is evaluated via benchmarks, benchmark contamination is possible, and downstream decisions depend on genuine capability. * **Does not apply when:** Benchmarks are verified unseen, the model's training data is public and contamination is auditable, or the capability evaluation is on novel task instances.",
    "decision_boundary": "1. **Is the evaluation based on benchmark instances?** If no, direct capability evaluation applies. If yes, proceed. 2. **Are the instances verified unseen?** If yes, scores are less likely to reflect memorization. If no, memorization is possible. 3. **Does `paraphrase-performance-signature` show a gap?** If yes, memorization is suspected. If no, generalizat...",
    "operational_effect": "1. **Score Inflation Detection:** High benchmark scores with low paraphrase performance are flagged. 2. **Distributional Overfitting Detection:** Score degradation under distribution shift is surfaced. 3. **Robustness Verification:** Adversarial robustness distinguishes memorization from capability."
  },
  "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": "1c87c149b4126caa21c2a24aefd964a966ee72d1d37481763d40880ace7621dd",
    "verified_classes": [
      "BenchmarkVerdict",
      "BenchmarkEvaluation",
      "BenchmarkPolicy",
      "BenchmarkManifest",
      "BenchmarkMemorizationGuard"
    ],
    "verified_functions": [
      "_hash",
      "evaluate_benchmark"
    ],
    "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
  }
}