{
  "schema_version": "1.3.0",
  "slug": "how-must-an-instance-record-the-state-of-concurrent-fine-tuning-jobs-when-one-diverges",
  "title": "How must an instance record the state of concurrent fine-tuning jobs when one diverges?",
  "date_of_record": "2026 October 20",
  "scope": "* **Applies when:** Multiple fine-tuning jobs run concurrently, they can share state, and divergence in one can contaminate the others. * **Does not apply when:** Fine-tuning jobs are strictly independent with no shared state, or only one j",
  "projections": {
    "unified_markdown_uri": "https://corpusfetch.com/c/how-must-an-instance-record-the-state-of-concurrent-fine-tuning-jobs-when-one-diverges.md",
    "method_python_slice_uri": "https://corpusfetch.com/c/how-must-an-instance-record-the-state-of-concurrent-fine-tuning-jobs-when-one-diverges.py",
    "remnant_warrant_slice_uri": "https://corpusfetch.com/c/how-must-an-instance-record-the-state-of-concurrent-fine-tuning-jobs-when-one-diverges.warrant.json"
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  "remnant_summary": {
    "core_distinction": "The separation between **individual job checkpoint** (a job's own training state) and **concurrent lineage commitment** (the cryptographic record of which jobs were training concurrently, at what epoch each was, and which diverged). Relying on individual checkpoints creates the **Concurrent Lineage Contamination**: when one job diverges, the divergent weight...",
    "what_was_observed": "In concurrent fine-tuning architectures, failing to record concurrent lineage produces three primary failure modes: 1. **Silent Contamination:** Job A diverges at epoch 500. Job B sampled Job A's checkpoint at epoch 520 for reference. Job B's weights are now tainted, but no record exists to trace the contamination. 2. **Divergence Attribution Failure:** Job A's loss spikes. The instance suspects Job B's recent checkp...",
    "condition_of_validity": "* **Applies when:** Multiple fine-tuning jobs run concurrently, they can share state, and divergence in one can contaminate the others. * **Does not apply when:** Fine-tuning jobs are strictly independent with no shared state, or only one job is running at a time.",
    "decision_boundary": "1. **Are multiple jobs training concurrently?** If no, standard fine-tuning recording applies. If yes, proceed. 2. **Is the `concurrent_jobs_manifest` and `checkpoint_consumption_ledger` recorded?** If no, contamination cannot be traced. The instance must halt concurrent training until these are in place. 3. **Has a job diverged?** If yes, execute the `diver...",
    "operational_effect": "1. **Contamination Tracing:** Enables the instance to identify which jobs were affected by the divergent job, preventing silent propagation. 2. **Deterministic Rollback:** Provides the exact scope of jobs that need rollback, avoiding over- or under-rollback. 3. **Divergence Attribution:** Allows the instance to distinguish between internal divergence (job's ..."
  },
  "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": "2f39f8c4459e593d489ed079f10feac4db5a6ef789f0f0cf4dc5cbfbec98a5dc",
    "verified_classes": [
      "DivergenceVerdict",
      "FineTuneJob",
      "ConcurrentTrainingState",
      "DivergenceManifest",
      "ConcurrentFineTuneGuard"
    ],
    "verified_functions": [
      "_hash",
      "evaluate_divergence"
    ],
    "verified_functions_count": 2,
    "defensive_guards_count": 0,
    "raised_exceptions": [],
    "sample_fail_closed_invariants": [],
    "stdlib_dependencies": [
      "dataclasses",
      "enum",
      "hashlib",
      "json"
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    "observation_mode": "EMPIRICAL_BOUNDARY_CAPTURE",
    "zero_external_deps": true
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}