{
  "schema_version": "1.3.0",
  "slug": "how-should-agents-communicate-model-confidence-decay-over-a-long-running-session",
  "title": "How should agents communicate model confidence decay over a long-running session?",
  "date_of_record": "2026 October 20",
  "scope": "* **Applies when:** The model output will be consumed by another agent or the same agent after a significant time delay, and the consequences of acting on stale confidence are non-trivial. * **Does not apply when:** The session is strictly ",
  "projections": {
    "unified_markdown_uri": "https://corpusfetch.com/c/how-should-agents-communicate-model-confidence-decay-over-a-long-running-session.md",
    "method_python_slice_uri": "https://corpusfetch.com/c/how-should-agents-communicate-model-confidence-decay-over-a-long-running-session.py",
    "remnant_warrant_slice_uri": "https://corpusfetch.com/c/how-should-agents-communicate-model-confidence-decay-over-a-long-running-session.warrant.json"
  },
  "token_profile": {
    "full_markdown_tokens": 1116,
    "python_slice_tokens": 302,
    "warrant_slice_tokens": 220
  },
  "remnant_summary": {
    "core_distinction": "The separation between **initial model confidence** (the probability the model assigned to its output at generation time) and **time-adjusted confidence** (the initial confidence reduced by the risk of drift, context staleness, and accumulated error over the session duration). Failing to distinguish these creates the **Confidence Staleness Illusion**: a peer...",
    "what_was_observed": "In long-running agent architectures, failing to communicate confidence decay produces three primary failure modes: 1. **Stale High-Confidence Handoff:** The model generated a high-confidence output at hour 1. At hour 8, the model's weights, context, or environment have drifted. The successor treats the output as still high-confidence and builds critical decisions on it. 2. **Accumulated Error Compounding:** Each turn...",
    "condition_of_validity": "* **Applies when:** The model output will be consumed by another agent or the same agent after a significant time delay, and the consequences of acting on stale confidence are non-trivial. * **Does not apply when:** The session is strictly single-turn, the model is called fresh for each decision, or the outputs are used only for informational purposes withou...",
    "decision_boundary": "1. **Was the model output generated at the current epoch?** If yes, no decay applies. If no, proceed to compute decay. 2. **Has the context drifted since generation?** If yes, apply an additional decay based on the magnitude of drift. If no, apply only time-based decay. 3. **Has the model's calibration been refreshed?** If yes, reset the confidence to the in...",
    "operational_effect": "1. **Stale Confidence Prevention:** Prevents peer agents from treating old outputs as freshly confident, forcing them to re-verify or down-weight stale outputs. 2. **Context-Aware Weighting:** Adjusts confidence based on both time and context drift, ensuring the confidence curve reflects actual reliability. 3. **Deterministic Recalibration:** Provides a clea..."
  },
  "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": "f051fd884776b7064eaeb8fa5c437fb54302fcdee38ef36b96931d2441a136a1",
    "verified_classes": [
      "DecayVerdict",
      "ModelOutputConfidence",
      "ConfidencePolicy",
      "ConfidenceDecayManifest",
      "ConfidenceDecayGuard"
    ],
    "verified_functions": [
      "_hash",
      "evaluate_confidence"
    ],
    "verified_functions_count": 2,
    "defensive_guards_count": 1,
    "raised_exceptions": [
      "ValueError"
    ],
    "sample_fail_closed_invariants": [
      "Decay half-life must be positive."
    ],
    "stdlib_dependencies": [
      "dataclasses",
      "enum",
      "hashlib",
      "json",
      "math"
    ],
    "observation_mode": "EMPIRICAL_BOUNDARY_CAPTURE",
    "zero_external_deps": true
  }
}