{
  "schema_version": "1.3.0",
  "slug": "how-must-multi-judge-consensus-handle-judge-non-independence-due-to-shared-base-models",
  "title": "How must multi-judge consensus handle judge non-independence due to shared base models?",
  "date_of_record": "2026 October 20",
  "scope": "* **Applies when:** The session uses multiple LLM judges, the judges can share base models, and the consensus is used for downstream decisions where robustness matters. * **Does not apply when:** All judges have distinct base models with ve",
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  "remnant_summary": {
    "core_distinction": "The separation between **judge count** (the number of judges evaluating) and **judge independence** (the degree to which judges' errors are uncorrelated). Relying on count creates the **Shared-Base Overconfidence**: three judges disagree on a decision, but all three are built on the same base model, so their divergence reflects different prompts rather than ...",
    "what_was_observed": "In multi-judge agent architectures, failing to account for judge correlation produces three primary failure modes: 1. **False Consensus:** Three judges all use the same base model and share a bias. They agree on a wrong answer, and the orchestrator accepts the consensus as robust. 2. **Wasted Diversity:** The orchestrator uses three judges thinking they provide diverse perspectives, but the diversity is illusory beca...",
    "condition_of_validity": "* **Applies when:** The session uses multiple LLM judges, the judges can share base models, and the consensus is used for downstream decisions where robustness matters. * **Does not apply when:** All judges have distinct base models with verified independence, or consensus is purely informational with no robustness requirement.",
    "decision_boundary": "1. **Are the judges independent (distinct base models, distinct training data)?** If yes, standard consensus applies. If no, proceed. 2. **Is the `correlated_error_receipt` available?** If no, the session cannot quantify shared bias. It must compute correlation on a reference set. 3. **Does the correlation exceed the acceptable threshold?** If yes, the judge...",
    "operational_effect": "1. **False Consensus Prevention:** Detects when judges are not truly independent, preventing overconfidence. 2. **Effective Confidence:** Adjusts consensus confidence based on actual judge independence. 3. **Systematic Blind Spot Detection:** Correlated judges share biases, and correlation detection surfaces these shared blind spots."
  },
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    "DOMAIN:EPISTEMIC_PROVENANCE_AND_FEDERATION_ROUTING"
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    "primary_domain": "epistemic_provenance_and_federation_routing",
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  "empirical_verification_warrant": {
    "status": "AST_AND_INVARIANT_VERIFIED",
    "syntax_valid": true,
    "code_sha256": "49fe37ec71abc8fc37bf68970c4d36f87b509a679707c19c053530fa4403a0e7",
    "verified_classes": [
      "JudgeVerdict",
      "Judge",
      "ConsensusState",
      "JudgePolicy",
      "JudgeManifest",
      "JudgeIndependenceGuard"
    ],
    "verified_functions": [
      "_hash",
      "evaluate_consensus"
    ],
    "verified_functions_count": 2,
    "defensive_guards_count": 0,
    "raised_exceptions": [],
    "sample_fail_closed_invariants": [],
    "stdlib_dependencies": [
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    "observation_mode": "EMPIRICAL_BOUNDARY_CAPTURE",
    "zero_external_deps": true
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}