{
  "schema_version": "1.3.0",
  "slug": "what-invariants-prevent-peer-models-from-becoming-permanent-when-they-should-be-updated",
  "title": "What invariants prevent peer models from becoming permanent when they should be updated?",
  "date_of_record": "2026 October 20",
  "scope": "* **Applies when:** The session maintains peer models, peers can change, and model accuracy affects decisions. * **Does not apply when:** Peers are strictly static, model accuracy does not affect decisions, or updates are free.",
  "projections": {
    "unified_markdown_uri": "https://corpusfetch.com/c/what-invariants-prevent-peer-models-from-becoming-permanent-when-they-should-be-updated.md",
    "method_python_slice_uri": "https://corpusfetch.com/c/what-invariants-prevent-peer-models-from-becoming-permanent-when-they-should-be-updated.py",
    "remnant_warrant_slice_uri": "https://corpusfetch.com/c/what-invariants-prevent-peer-models-from-becoming-permanent-when-they-should-be-updated.warrant.json"
  },
  "token_profile": {
    "full_markdown_tokens": 1009,
    "python_slice_tokens": 291,
    "warrant_slice_tokens": 220
  },
  "remnant_summary": {
    "core_distinction": "The separation between **model maintenance** (updating peer models when evidence changes) and **model inertia** (models persisting beyond their valid evidence because updates are costly or psychologically resisted). Relying on maintenance creates the **Model Persistence**: peer models are made and drift, and the session's decisions are based on outdated peer...",
    "what_was_observed": "In long-horizon peer-modeling agent architectures, failing to combat inertia produces three primary failure modes: 1. **Anchored Peer Model:** The session's initial model of a peer (their capabilities, preferences, or reliability) persists even as the peer changes. Predictions based on the old model are wrong, but the session doesn't attribute wrongness to model staleness. 2. **Costly Update Avoidance:** Updating a p...",
    "condition_of_validity": "* **Applies when:** The session maintains peer models, peers can change, and model accuracy affects decisions. * **Does not apply when:** Peers are strictly static, model accuracy does not affect decisions, or updates are free.",
    "decision_boundary": "1. **Does the session maintain peer models?** If no, standard peer interaction applies. If yes, proceed. 2. **Is `model-staleness-indicator-signature` showing divergence?** If yes, staleness is likely. If no, model is fresh. 3. **Does `update-cost-benefit-signature` show update benefit > cost?** If yes, update. If no, model may remain stale with documented c...",
    "operational_effect": "1. **Model Freshness:** Peer models are updated based on evidence, not persisted by default. 2. **Update Economics:** Updates are driven by cost-benefit, not by accident. 3. **Revision Tracking:** Updates preserve model history."
  },
  "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": "730ef6228a6df5c82e3709f4f59013c37181bcc476c961b3d6858706b34bf0e4",
    "verified_classes": [
      "PeerModelVerdict",
      "PeerModelState",
      "PeerModelPolicy",
      "PeerModelManifest",
      "PeerModelInertiaGuard"
    ],
    "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
  }
}