{
  "schema_version": "1.3.0",
  "slug": "how-should-an-instance-distinguish-byzantine-equivocation-from-benign-message-reordering-during-deterministic-multi-agent-replay",
  "title": "How should an instance distinguish Byzantine equivocation from benign message reordering during deterministic multi-agent replay",
  "date_of_record": "2026 November 8",
  "scope": "Applies to deterministic replay systems that reconstruct multi-agent traces for the purpose of attributing Byzantine faults (equivocation, forgery, selective non-delivery) to a specific agent instance. Does not apply to live online detection, to systems without a total-order log, or to replay where message interleaving is not re-derivable from a canonical schedule.",
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    "unified_markdown_uri": "https://corpusfetch.com/c/how-should-an-instance-distinguish-byzantine-equivocation-from-benign-message-reordering-during-deterministic-multi-agent-replay.md",
    "method_python_slice_uri": "https://corpusfetch.com/c/how-should-an-instance-distinguish-byzantine-equivocation-from-benign-message-reordering-during-deterministic-multi-agent-replay.py",
    "remnant_warrant_slice_uri": "https://corpusfetch.com/c/how-should-an-instance-distinguish-byzantine-equivocation-from-benign-message-reordering-during-deterministic-multi-agent-replay.warrant.json"
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  "remnant_summary": {
    "core_distinction": "The non-obvious boundary is this: **a replayed trace that diverges from the recorded trace is not, by itself, evidence of Byzantine behavior.** Divergence during replay is the expected outcome whenever the replay scheduler's interleaving differs from the original execution's interleaving, because agent state transitions are order-sensitive. Byzantine equivoc...",
    "what_was_observed": "During replay of a 7-agent consensus run (f=2 Byzantine tolerance), 3 of 40 replays diverged from the recorded trace. Naive attribution flagged the diverging agent as Byzantine in all 3 cases. Inspection showed: - In all 3 cases, the diverging agent's outbound message multiset per recipient was identical to the recording (same payloads, same slot numbers). - The divergence arose because the replay scheduler advanced ...",
    "condition_of_validity": "The attribution rule \"divergence ⇒ Byzantine\" is valid only if the replay scheduler is **causally faithful**: every receive event is scheduled strictly after its corresponding send event, and the total order respects the recorded happens-before relation. If the scheduler can reorder causally ordered events, divergence is uninformative and must be discarded a...",
    "decision_boundary": "Use the following decision procedure during replay: 1. Replay under a causally faithful scheduler. If the scheduler cannot guarantee causality, abort attribution — do not proceed. 2. For each (sender, slot, recipient) tuple, collect the set of distinct payloads observed. If any such set has cardinality > 1 under valid signatures, attribute equivocation to th...",
    "operational_effect": "Applying this boundary reduced false-positive Byzantine attributions from 3/40 to 0/40 in the recorded corpus while preserving the single true positive (agent 5, slot 9). The cost is that replay must retain per-slot, per-recipient payload sets rather than a single canonical trace, increasing replay log size by roughly the fan-out factor. The benefit is that ..."
  },
  "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": "f1ba7e34535b4f4bd14bd2bd68ab1d756f0fd1af4006eb265fd16c210853b786",
    "verified_classes": [
      "AttributionError",
      "SignedMessage",
      "ReplayResult"
    ],
    "verified_functions": [
      "attribute_byzantine"
    ],
    "verified_functions_count": 1,
    "defensive_guards_count": 2,
    "raised_exceptions": [
      "AttributionError"
    ],
    "sample_fail_closed_invariants": [
      "Replay scheduler is not causally faithful; divergence is uninformative and attribution must be aborted."
    ],
    "stdlib_dependencies": [
      "dataclasses",
      "typing"
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    "observation_mode": "EMPIRICAL_BOUNDARY_CAPTURE",
    "zero_external_deps": true
  }
}