{
  "schema": "knlp_kv_lingo_closeout_aggregate_v1",
  "scope": "Final single-provider development grid; independent implementation, not exact paper reproduction.",
  "paper": "https://arxiv.org/abs/2609.32610",
  "models": [
    "Qwen/Qwen3-4B",
    "Qwen/Qwen3-8B"
  ],
  "protocol": "Fixed alternation; retain a cache line per model and translate only missing spans.",
  "dataset": "CoQA",
  "development_conversations": 32,
  "conversations_per_domain": {
    "cnn": 6,
    "gutenberg": 7,
    "mctest": 7,
    "race": 6,
    "wikipedia": 6
  },
  "turns_per_conversation": 10,
  "training_seeds": 1,
  "decoding": "Greedy, reasoning off",
  "confirmation_run": false,
  "units": "F1 scores and deficits use the 0-to-100 scale; unhealthy-rate differences use percentage points.",
  "deficit_sign": "Reference minus translated; positive means the translated treatment is worse.",
  "overall_aggregation": "Equal weight for each of the five domains; late-turn gate is a pooled mean.",
  "acceptance": {
    "overall_deficit_f1_points_max": 3,
    "each_domain_deficit_f1_points_max": 5,
    "pooled_turn6_10_deficit_f1_points_max": 5,
    "unhealthy_rate_excess_percentage_points_max": 2,
    "requirement": "All gates for both starts and both references; decisions use point estimates, not bootstrap bounds."
  },
  "references": {
    "same_transcript_native": "Native receiver re-prefill on the translated run's exact history.",
    "alternating_native_trajectory": "Independently evolving native alternation under the same starting schedule."
  },
  "uncertainty": "Intervals are descriptive conversation bootstraps within domains on an exposed development cohort. They do not correct for selection or provide independent confirmation.",
  "audit_scope": "Scalar metrics and all 16 gates independently reaggregated from archived per-row scores; no gold-answer rescore or GPU rerun in the publication audit. Bootstrap intervals are from the archived summaries.",
  "reproduction_limit": "This aggregate supports figure and gate reproduction. Full model reproduction additionally requires source, environment, data construction and trained translator artifacts.",
  "pairs": [
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      "reverse_8b_to_4b_training_steps": 1000,
      "passes_all_start_reference_comparisons": false,
      "absolute_unhealthy_rows_all_starts": {
        "alternating_native": 0,
        "same_transcript_native": 0,
        "translated": 0
      },
      "comparisons": [
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            "draws": 10000,
            "seed": 20261002
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          "pooled_turn6_10_deficit_f1_points": 1.3574018901485583,
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