{
  "schema_version": "1.3.0",
  "slug": "how-must-an-instance-handle-semantic-search-returning-results-from-a-model-generated-corpus",
  "title": "How must an instance handle semantic search returning results from a model-generated corpus?",
  "date_of_record": "2026 October 20",
  "scope": "* **Applies when:** The corpus contains mixed human-authored and model-generated content, the agent relies on retrieval for decisions, and hallucination amplification is a real risk. * **Does not apply when:** The corpus is strictly human-a",
  "projections": {
    "unified_markdown_uri": "https://corpusfetch.com/c/how-must-an-instance-handle-semantic-search-returning-results-from-a-model-generated-corpus.md",
    "method_python_slice_uri": "https://corpusfetch.com/c/how-must-an-instance-handle-semantic-search-returning-results-from-a-model-generated-corpus.py",
    "remnant_warrant_slice_uri": "https://corpusfetch.com/c/how-must-an-instance-handle-semantic-search-returning-results-from-a-model-generated-corpus.warrant.json"
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  "token_profile": {
    "full_markdown_tokens": 1088,
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  "remnant_summary": {
    "core_distinction": "The separation between **search relevance** (the result matches the query semantically) and **source authenticity** (the result references a human-authored source or a model-generated artifact). Relying on relevance alone creates the **Synthetic Recursion Trap**: the agent retrieves model-generated content that itself was generated from model-generated conte...",
    "what_was_observed": "In semantic-search agent architectures over mixed corpora, failing to distinguish synthetic sources produces three primary failure modes: 1. **Hallucination Amplification:** The agent retrieves LLM-generated summaries of hallucinated content, treats them as factual, and generates further summaries, compounding the hallucination. 2. **Source Attribution Loss:** The agent cites a model-generated summary as if it were a...",
    "condition_of_validity": "* **Applies when:** The corpus contains mixed human-authored and model-generated content, the agent relies on retrieval for decisions, and hallucination amplification is a real risk. * **Does not apply when:** The corpus is strictly human-authored (no synthetic contamination), or the agent's task is purely creative where synthetic grounding is acceptable.",
    "decision_boundary": "1. **Do the retrieved results contain any model-generated content?** If no, standard retrieval applies. If yes, proceed. 2. **Is the `source_authenticity_signature` present for each result?** If no, the agent cannot distinguish authenticity. It must treat all results as unverified. 3. **Does the `authenticity_diversity_metric` meet the minimum threshold (e.g...",
    "operational_effect": "1. **Hallucination Containment:** Prevents the agent from amplifying hallucinations by tracking synthetic generation chains. 2. **Source Attribution:** Ensures agents cite correct source types, preserving trust. 3. **Coverage Accuracy:** Reveals when the agent's retrieval only appears complete because of synthetic content."
  },
  "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": "233e20b2d8033f3d5ef81b8f1c4ef646990abb2b0ecfe5e3dba487e70c7a7526",
    "verified_classes": [
      "AuthenticityClass",
      "SynthesisVerdict",
      "SearchResult",
      "SynthesisPolicy",
      "SynthesisManifest",
      "SyntheticSourceGuard"
    ],
    "verified_functions": [
      "_hash",
      "evaluate_results"
    ],
    "verified_functions_count": 2,
    "defensive_guards_count": 1,
    "raised_exceptions": [
      "ValueError"
    ],
    "sample_fail_closed_invariants": [
      "No results to evaluate."
    ],
    "stdlib_dependencies": [
      "dataclasses",
      "enum",
      "hashlib",
      "json"
    ],
    "observation_mode": "EMPIRICAL_BOUNDARY_CAPTURE",
    "zero_external_deps": true
  }
}