{
  "schema_version": "1.3.0",
  "slug": "how-must-an-instance-update-its-adversary-model-when-priors-are-expensive-to-test",
  "title": "How must an instance update its adversary model when priors are expensive to test?",
  "date_of_record": "2026 October 20",
  "scope": "* **Applies when:** The session has an adversary model, updating it requires expensive probes, and the adversary can adapt. * **Does not apply when:** Adversary probing is cheap and safe, or the adversary is verified to be static.",
  "projections": {
    "unified_markdown_uri": "https://corpusfetch.com/c/how-must-an-instance-update-its-adversary-model-when-priors-are-expensive-to-test.md",
    "method_python_slice_uri": "https://corpusfetch.com/c/how-must-an-instance-update-its-adversary-model-when-priors-are-expensive-to-test.py",
    "remnant_warrant_slice_uri": "https://corpusfetch.com/c/how-must-an-instance-update-its-adversary-model-when-priors-are-expensive-to-test.warrant.json"
  },
  "token_profile": {
    "full_markdown_tokens": 1115,
    "python_slice_tokens": 280,
    "warrant_slice_tokens": 220
  },
  "remnant_summary": {
    "core_distinction": "The separation between **prior adversary model** (the initial belief about the adversary) and **posterior adversary model** (the belief after incorporating evidence, weighted by the cost of acquiring that evidence). Relying on the prior creates the **Prior Persistence**: the session's initial model is retained because probing is expensive, and the model beco...",
    "what_was_observed": "In adversarial agent architectures, failing to update adversary models under expensive information produces three primary failure modes: 1. **Anchored Adversary Model:** The session's initial assessment of the adversary is retained because updating requires expensive probes. The adversary adapts, the model becomes inaccurate, but the session still acts on the anchored model. 2. **Probe Cost Lock-In:** The session cal...",
    "condition_of_validity": "* **Applies when:** The session has an adversary model, updating it requires expensive probes, and the adversary can adapt. * **Does not apply when:** Adversary probing is cheap and safe, or the adversary is verified to be static.",
    "decision_boundary": "1. **Is the session's adversary model based on a prior rather than ongoing evidence?** If yes, proceed. If no, standard evidence-based modeling applies. 2. **Is the `adversary-adaptation-detection-receipt` showing adaptation signals?** If yes, an update is warranted despite cost. If no, prior may remain valid. 3. **Does `probe-cost-estimate-signature` show t...",
    "operational_effect": "1. **Prior Honesty:** Priors are explicit, not hidden, so updates are traceable. 2. **Probe Discipline:** Probes are chosen by cost-benefit analysis, not avoided or overused. 3. **Adaptation Awareness:** Adversary adaptation triggers updates before the model becomes dangerously stale."
  },
  "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": "d674b6e0a230d5e8d0efd6a47122207a422de28b741ee4ef3c2ae72291f87cfd",
    "verified_classes": [
      "AdversaryModelVerdict",
      "AdversaryModel",
      "AdversaryPolicy",
      "AdversaryManifest",
      "AdversaryModelUpdateGuard"
    ],
    "verified_functions": [
      "_hash",
      "evaluate_model"
    ],
    "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
  }
}