{
  "schema_version": "1.3.0",
  "slug": "under-what-conditions-must-an-instance-treat-verifier-model-non-determinism-as-a-replay-divergence-fault-rather-than-plan-noise",
  "title": "Under what conditions must an instance treat verifier-model non-determinism as a replay-divergence fault rather than plan noise",
  "date_of_record": "2026 November 14",
  "scope": "Applies to speculative agent planners that fork candidate plans, score branches with a verifier model, and later replay committed branches under a deterministic transcript for audit or recovery. Does not apply to planners that replay from a full state snapshot with bit-identical model weights and pinned kernels, nor to verifiers whose outputs are never re-executed after commit.",
  "projections": {
    "unified_markdown_uri": "https://corpusfetch.com/c/under-what-conditions-must-an-instance-treat-verifier-model-non-determinism-as-a-replay-divergence-fault-rather-than-plan-noise.md",
    "method_python_slice_uri": "https://corpusfetch.com/c/under-what-conditions-must-an-instance-treat-verifier-model-non-determinism-as-a-replay-divergence-fault-rather-than-plan-noise.py",
    "remnant_warrant_slice_uri": "https://corpusfetch.com/c/under-what-conditions-must-an-instance-treat-verifier-model-non-determinism-as-a-replay-divergence-fault-rather-than-plan-noise.warrant.json"
  },
  "token_profile": {
    "full_markdown_tokens": 1678,
    "python_slice_tokens": 248,
    "warrant_slice_tokens": 220
  },
  "remnant_summary": {
    "core_distinction": "The distinction that matters is between *plan noise* and *replay divergence*. Plan noise is verifier score variance observed during the speculative phase: two forks of the same branch receive slightly different scores because of sampling, batching, or floating-point reduction order. It is benign because the losing fork is discarded and never re-executed. Rep...",
    "what_was_observed": "In a planner that forked three candidate continuations per step and scored them with a 7B verifier served through a continuous batcher, replays of committed branches reproduced the winning branch's *identity* in 99.2% of cases but reproduced the *full score vector* in only 87.4%. The 11.8% gap was concentrated in ties and near-ties: branches whose speculative scores differed by less than 0.03. In those cases the repl...",
    "condition_of_validity": "The replay is only a valid oracle for the committed decision under three simultaneous conditions: 1. **Batch-shape invariance.** The verifier's score for a given (context, branch) pair must be invariant to the number and identity of co-batched items, or the replay must reconstruct the exact batch membership of the speculative phase. If neither holds, replay ...",
    "decision_boundary": "The operational rule is a two-axis decision: - **Axis 1 — crossed commit?** Variance before commit: record for calibration, never fault. Variance after commit: proceed to Axis 2. - **Axis 2 — replay parameterized identically?** If batch membership, precision, and reduction order match the commit record, any score-vector deviation beyond the calibrated tolera...",
    "operational_effect": "Enforcing this boundary changes three things. First, the commit record grows to include verifier invocation parameters, making replays reproducible by construction rather than by hope. Second, audit tooling stops flagging correct plans as corrupt, because it compares score vectors under a calibrated tolerance instead of asserting rank equality. Third, re-pla..."
  },
  "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": "90f7b97a96628a7e84e40dd38d58cfb9de13447c61dfd18301e01d57047466d2",
    "verified_classes": [
      "ReplayDivergenceError",
      "ReconstructionMismatchError"
    ],
    "verified_functions": [
      "classify_replay"
    ],
    "verified_functions_count": 1,
    "defensive_guards_count": 3,
    "raised_exceptions": [
      "ReconstructionMismatchError",
      "ReplayDivergenceError"
    ],
    "sample_fail_closed_invariants": [],
    "stdlib_dependencies": [],
    "observation_mode": "EMPIRICAL_BOUNDARY_CAPTURE",
    "zero_external_deps": true
  }
}