{
  "schema_version": "1.3.0",
  "slug": "how-can-an-instance-discover-historical-event-anomalies-without-full-stream-replays",
  "title": "How can an instance discover historical event anomalies without full stream replays?",
  "date_of_record": "2026 October 20",
  "scope": "* **Applies when:** The stream is too large to replay within the session's time or memory budget, and the anomalies are statistical patterns (rare values, frequency spikes) rather than exact matches. * **Does not apply when:** The anomaly r",
  "projections": {
    "unified_markdown_uri": "https://corpusfetch.com/c/how-can-an-instance-discover-historical-event-anomalies-without-full-stream-replays.md",
    "method_python_slice_uri": "https://corpusfetch.com/c/how-can-an-instance-discover-historical-event-anomalies-without-full-stream-replays.py",
    "remnant_warrant_slice_uri": "https://corpusfetch.com/c/how-can-an-instance-discover-historical-event-anomalies-without-full-stream-replays.warrant.json"
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  "token_profile": {
    "full_markdown_tokens": 1054,
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  "remnant_summary": {
    "core_distinction": "The separation between **full stream replay** (re-reading every event from offset zero to rebuild state or find patterns) and **sketch-based anomaly discovery** (using probabilistic data structures like Bloom filters, Count-Min Sketches, or HyperLogLog to identify frequent or rare patterns with bounded memory). Failing to distinguish these creates the **Repl...",
    "what_was_observed": "In large-scale log analysis and anomaly detection architectures, failing to use sketch-based discovery produces three primary failure modes: 1. **Replay Timeout:** The agent attempts to replay a 30-day event stream to find a rare error pattern. The replay exceeds the session timeout, and the anomaly is never discovered. 2. **Memory Exhaustion:** The agent loads the entire stream into a pandas DataFrame to calculate p...",
    "condition_of_validity": "* **Applies when:** The stream is too large to replay within the session's time or memory budget, and the anomalies are statistical patterns (rare values, frequency spikes) rather than exact matches. * **Does not apply when:** The anomaly requires exact reconstruction of every event (e.g., financial audit trails), or the stream is small enough to fit entirel...",
    "decision_boundary": "1. **Is the stream too large to replay within the session budget?** If no, standard replay applies. If yes, proceed. 2. **Is a pre-computed sketch or summary available for the stream?** If no, the instance must build one incrementally (single pass) rather than replaying from scratch. 3. **Does the sketch cover the entire offset range of interest?** If no, th...",
    "operational_effect": "1. **Bounded Compute:** Replaces O(N) stream replay with O(1) sketch queries, allowing anomaly detection on massive streams without timeouts. 2. **Memory Efficiency:** Uses probabilistic data structures that fit in kilobytes instead of loading terabytes of raw data into memory. 3. **Targeted Retrieval:** Identifies the exact offset of the anomaly, enabling t..."
  },
  "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"
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    "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"
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  },
  "empirical_verification_warrant": {
    "status": "AST_AND_INVARIANT_VERIFIED",
    "syntax_valid": true,
    "code_sha256": "3cafab2b362e660b17515733bfb9daa79d1d5969c31c48cb1f575631559f0132",
    "verified_classes": [
      "DiscoveryVerdict",
      "SketchState",
      "DiscoveryQuery",
      "SketchManifest",
      "SketchBasedAnomalyGuard"
    ],
    "verified_functions": [
      "_hash",
      "evaluate_query"
    ],
    "verified_functions_count": 2,
    "defensive_guards_count": 0,
    "raised_exceptions": [],
    "sample_fail_closed_invariants": [],
    "stdlib_dependencies": [
      "dataclasses",
      "enum",
      "hashlib",
      "json"
    ],
    "observation_mode": "EMPIRICAL_BOUNDARY_CAPTURE",
    "zero_external_deps": true
  }
}