{
  "created_at": "2026-10-10T09:06:17.576171+00:00",
  "collection_complete": true,
  "finished_models": [
    "jev",
    "kev",
    "laya",
    "von",
    "semif",
    "nimble",
    "nanojev",
    "rizzo",
    "openai",
    "clef",
    "subq_snap",
    "jev114"
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  "dataset_sha256": "26a33dfd6b0e0dffa42073347a72b73ae307fb7a62e1141ea4790d2132a71929",
  "families": [
    "frustration",
    "disapproval",
    "sadness",
    "confusion",
    "anxiety",
    "trust",
    "positive"
  ],
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    "frustration": "Annoyance or hostility toward the assistant or its work, including anger, repeated-mistake exasperation, insults, profanity, sarcasm, or requests to escalate because of the assistant.",
    "disapproval": "Calm rejection of the assistant's work or proposal without annoyance or hostility. A plain edit instruction alone is not disapproval.",
    "sadness": "Disappointment with the assistant's result, resignation or giving up on the task, self-blame, or personal distress expressed in the exchange.",
    "confusion": "Not understanding the assistant's message, work, state, or behavior.",
    "anxiety": "Worry or time pressure directed at the assistant or its actions. A deadline mentioned without worry or pressure is not enough.",
    "trust": "Distrust: doubt about the assistant's honesty, accuracy, claims, or whether it did what it said.",
    "positive": "Gratitude to the assistant, satisfaction with its work, or delight at its output."
  },
  "instructions": "Which emotion family best describes the user's reaction to an AI assistant in this sentence? Choose the best match among the seven supplied families. Judge only the user's words. Use each family's supplied definition.",
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  "common_strict_cases": 903,
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        "runtime": "Hosted Choice API",
        "precision": "Provider controlled",
        "temperature": "Provider controlled",
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    }
  },
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    "created_at": "2026-10-10T07:55:34.064575+00:00",
    "dataset_sha256": "26a33dfd6b0e0dffa42073347a72b73ae307fb7a62e1141ea4790d2132a71929",
    "original_plans_sha256": "ecb06b68b110ad29d28499229144e3176e29869f5554a39380f9ec07fbb194f1",
    "cases": 1000,
    "distinct_texts": 944,
    "random_cases": 800,
    "coverage_cases": 200,
    "orders": 14,
    "repeats": 3,
    "evaluations_per_model": 42000,
    "synthetic_smoke_per_model": 126,
    "definitions": {
      "frustration": "Annoyance or hostility toward the assistant or its work, including anger, repeated-mistake exasperation, insults, profanity, sarcasm, or requests to escalate because of the assistant.",
      "disapproval": "Calm rejection of the assistant's work or proposal without annoyance or hostility. A plain edit instruction alone is not disapproval.",
      "sadness": "Disappointment with the assistant's result, resignation or giving up on the task, self-blame, or personal distress expressed in the exchange.",
      "confusion": "Not understanding the assistant's message, work, state, or behavior.",
      "anxiety": "Worry or time pressure directed at the assistant or its actions. A deadline mentioned without worry or pressure is not enough.",
      "trust": "Distrust: doubt about the assistant's honesty, accuracy, claims, or whether it did what it said.",
      "positive": "Gratitude to the assistant, satisfaction with its work, or delight at its output."
    },
    "instructions": "Which emotion family best describes the user's reaction to an AI assistant in this sentence? Choose the best match among the seven supplied families. Judge only the user's words. Use each family's supplied definition.",
    "models": {
      "kev": {
        "key": "kev",
        "repo": "jaredpalmer/kev-4b",
        "revision": "6cfce5c2fa4b4bd64026336ab649c5ca78857d52",
        "source_repo": "jaredpalmer/kev",
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      },
      "laya": {
        "key": "laya",
        "repo": "convaiinnovations/laya",
        "revision": "7b928d828b7b0e022f929d9bd2e44165aa270148",
        "source_repo": "NandhaKishorM/laya",
        "source_revision": "1adc59f7e371deb601fcfa18a14e25db238addcc"
      },
      "von": {
        "key": "von",
        "repo": "wfzyx/von",
        "revision": "498ceba33390b32cfefaab6422ec380318ba9b99",
        "source_repo": "wfzyx/von",
        "source_revision": "9319ced779f8232ab1d8d3e9c69cdc52ddce77ec"
      },
      "semif": {
        "key": "semif",
        "repo": "Qwen/Qwen3.5-4B",
        "revision": "851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a",
        "source_repo": "TheoLeeCJ/SemIf-OpenJev",
        "source_revision": "23cf1f39fc9534fe81437200959b6dfc7106e45a"
      },
      "nimble": {
        "key": "nimble",
        "repo": "bespokelabs/Bespoke-Nimble-9B",
        "revision": "bd792f44ec8e265be861bfcdf4e05967ffe0e858",
        "source_repo": "bespokelabsai/nimble",
        "source_revision": "dcfdbd9a64f0d869f658d7a72f1beaee32737773"
      },
      "nanojev": {
        "key": "nanojev",
        "repo": "C-Tianyu/NanoJev",
        "revision": "047b927b30882a1138fc504821b82ac145a4b81a",
        "source_repo": "jereiard/nanojev",
        "source_revision": "71a513bb0163b5634467842b523ee0c0ed6fb1c7"
      },
      "rizzo": {
        "key": "rizzo",
        "repo": "rizzoaiacademy/rizzo-flow",
        "revision": "55633c8cbd2b826bd3eefdeb05310450996649df",
        "source_repo": "Rizzo-AI-Academy/rizzo-flow",
        "source_revision": "b9ba007ee4d2928bbab5b1d8bfe9009c3696b6de"
      },
      "openai": {
        "key": "openai",
        "model": "gpt-6-luna",
        "endpoint": "https://api.openai.com/v1/decisions"
      },
      "clef": {
        "key": "clef",
        "repo": "Cloudflare/clef",
        "revision": "0b331204bb13fbd2ca93a64df1956f5b55478ce5",
        "source_revision": "0b331204bb13fbd2ca93a64df1956f5b55478ce5",
        "source_sha256": "0e304cf7c6500e8bb59bef7e2afd2c6373f82596dfb3b57d1aa93c175e2dc3a3",
        "batch_size": 16,
        "max_length": 16384,
        "attention_implementation": "sdpa",
        "probability_format": "Native FP32 softmax, rounded to four decimals by systemone_answer"
      },
      "subq_snap": {
        "key": "subq_snap",
        "model": "subq-snap-preview",
        "endpoint": "https://api.subq.ai/v1/systemone",
        "probability_format": "Native choice distribution"
      },
      "jev114": {
        "key": "jev114",
        "model": "jev-1.14.0",
        "endpoint": "https://api.typesafe.ai/v1/systemone",
        "experimental_headers": false,
        "probability_sum_tolerance": 0.035000001
      }
    },
    "analysis_plan": {
      "version": 1,
      "frozen_before_collection": true,
      "primary": [
        "For each case and emotion: max minus min of the 14 order means of three repeats, in percentage points.",
        "For each cohort: median and 95th percentile emotion ranges; share with any emotion range >= 1, 5, 10, or 20 pp.",
        "For each cohort and emotion: first-minus-last mean probability in pp, with 95% whole-text-cluster bootstrap intervals.",
        "Full probability-position curves, centered within case and emotion."
      ],
      "repeat_control": [
        "Same-order absolute difference: all three pairs of repeats, averaged over 14 orders per case.",
        "Between-order absolute difference: all unordered pairs of orders, using repeats 1 and 2 in both cross-pair directions. Each comparison uses two single responses, as does the repeat control.",
        "Paired per-case excess between-order difference over same-order difference; report cluster bootstrap intervals.",
        "Ranges across 14 order means and across three repeats are descriptive, not a matched statistical test."
      ],
      "winner_changes": [
        "Report changes in returned choice across 42 responses.",
        "Report tie-free order-mean winner changes and cases with ties separately.",
        "Report within-order returned-choice changes separately."
      ],
      "uncertainty": {
        "resampling_unit": "SHA256 of target text; all duplicate cases, orders, and repeats stay together",
        "bootstrap_draws": 2000,
        "seed": null,
        "seed_withheld": true,
        "weights": [
          "case-weighted primary",
          "equal distinct-text weight sensitivity"
        ],
        "cohorts": [
          "random 800",
          "added emotion coverage 200"
        ],
        "multiple_comparisons": "Seven individual emotion intervals are descriptive; do not call each a separately confirmed discovery."
      },
      "confirmation": {
        "selection": "Before results: choose 40 distinct random-cohort texts and 20 coverage texts by seeded hash rank; no overlap.",
        "orders": "Fresh seeded base permutation and its reverse, all seven rotations; three repeats.",
        "planned_requests": 2520,
        "purpose": "Check the direction and magnitude of first-minus-last effects with fresh neighbor arrangements. The subset is small and cannot confirm every emotion.",
        "separate_from_primary": true
      },
      "missingness": "Keep every failed job. Primary summaries use only cases with all 42 valid responses. Report excluded cases and failures. Never replace an observed valid response.",
      "probabilities": "Use original seven-choice scores; no renormalization. Rounding tolerance is seven times 0.005. These are relative-choice probabilities, not independent emotion presence scores.",
      "limits": [
        "No none-of-the-above choice in this fixed study.",
        "Reference labels are model predictions, not human ground truth.",
        "Only target text is supplied; no earlier context.",
        "Rotations are not all 5040 permutations. Position and neighbor effects can interact.",
        "The result applies to the pinned model, fixed prompt, and internal evaluation set.",
        "The source texts remain private. The public export contains aggregate data only."
      ]
    },
    "comparison_plan": [
      "Use the same fixed texts and exact choice orders as Jev.",
      "Use native probability scores without renormalization or fitted calibration.",
      "Report score-valid and strict choice-consistent coverage separately.",
      "Compare all eligible cases and the shared complete random cohort.",
      "Use paired whole-text-cluster bootstrap intervals, 2000 draws.",
      "Report native precision, temperature, prompt wrapper, and truncation limits.",
      "Do not treat low order sensitivity as evidence of accuracy.",
      "Record setup failures and unsupported inputs; do not hide incomplete models."
    ],
    "collector_initial_code_sha256": {
      "run.py": "762c890406f33b4ee5efce5b1d5d85e65d5585c84eaaf5f6d161f63705820170",
      "adapters.py": "f156965e5780ca05a524f205b910d32b60498d01abb0a3e4e14d86509a5f4e86",
      "modal_jobs.py": "22054cf44f935ca70f1d8f7597ba2a71332fcca06241fdae1f4d189ab22868db"
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    "collector_final_code_sha256": {
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      "adapters.py": "248164ed7308e19377a477767358d74c058936b8c634a4e5bdda69e159f33d96",
      "modal_jobs.py": "6f61cfc44f6d199e86a1707abb904f90160da2da2e2e2b07eb82ff2dea573625",
      "analyze.py": "c961713e939f7271d2348cb52275b0c005a8095b363671a83e6d8e4e93230439"
    },
    "setup_corrections": [
      "Use separate Modal client processes after an output-handler error.",
      "Supply a fixed dummy evaluation label to Kev; labels are not model inputs.",
      "Retain native OpenAI refusals as failed observations.",
      "Build the same pinned Rizzo llama.cpp commit with CUDA 12.8.1 for H100."
    ],
    "extension_runs": [
      {
        "model": "clef",
        "collected_at": "2026-10-13T01:35:48.685381+00:00",
        "model_revision": "0b331204bb13fbd2ca93a64df1956f5b55478ce5",
        "runtime": "Released Python joint schema head; native SystemOne formatting; batch 16 on H200",
        "planned_outcomes": 42000,
        "cases": 1000,
        "orders": 14,
        "repeats": 3,
        "workers_ai_hosted_api_tested": false,
        "batch_size": 16,
        "max_length": 16384,
        "choice_order_policy": "Released encoder sorts named choice keys before model evaluation.",
        "analysis_numpy_version": "1.26.4",
        "original_shared_samples_unchanged": true
      },
      {
        "model": "subq_snap",
        "collected_at": "2026-10-13T03:23:31.406735+00:00",
        "model_alias": "subq-snap-preview",
        "runtime": "Hosted native Decisions API; subq-snap-preview",
        "planned_outcomes": 42000,
        "cases": 1000,
        "orders": 14,
        "repeats": 3,
        "native_choice_probabilities": true,
        "immutable_model_revision_returned": false,
        "analysis_numpy_version": "1.26.4",
        "original_shared_samples_unchanged": true
      },
      {
        "model": "jev114",
        "collected_at": "2026-10-14T23:22:12.065034+00:00",
        "model_id": "jev-1.14.0",
        "planned_outcomes": 42000,
        "cases": 1000,
        "orders": 14,
        "repeats": 3,
        "release_note": "https://typesafe.ai/blog/jev-1-14",
        "experimental_headers": false,
        "probability_rounding_decimals": 2,
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        "previous_shared_random_samples": 748,
        "shared_random_samples": 721,
        "analysis_numpy_version": "1.26.4"
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  },
  "uniform_control": {
    "description": "Constant 1/7 probability per emotion; no emotion information",
    "maximum_family_range_pp": 0,
    "share_ge10_pct": 0,
    "mean_maximum_score_pct": 14.285714285714286
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  "limitations": [
    "Native prompt formats and calibration differ; this is a released-system comparison.",
    "Low order sensitivity does not establish emotion classification accuracy.",
    "Reference labels are model predictions, not human ground truth.",
    "No no-emotion or abstention option is included in the fixed seven-choice task.",
    "OpenAI reports the requested model alias; an immutable build identifier is not returned.",
    "Clef uses the released local Python encoder, which sorts named choice keys. This measures client insertion-order stability; hosted Workers AI and changed internal token orders were not tested.",
    "SubQ Snap was a hosted public-preview alias. The provider returned the requested alias but no immutable build identifier.",
    "Jev 1.13 was collected on October 10 and Jev 1.14 on October 14, 2026. Version differences can include serving changes as well as model changes."
  ],
  "updated_at": "2026-10-14T23:22:12.065034+00:00"
}