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    "reference": "G",
    "seed_pairs": 9,
    "models": {
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      "softmask": "Soft-Masked + ReMDM",
      "mdlm": "MDLM"
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    "tables": {
      "dream": "temperature-source-dream8k.tex",
      "gpt2": "temperature-source-gpt1k.tex"
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    "aggregation": "Mean and population SD of nine rooted scores: three direction seeds crossed with three GP seeds on fixed corpora. The spread is not a confidence interval. Table entries are rounded to four decimal places; downloads preserve the original seed scores.",
    "protocol": "SOL against reference G (GPT-2-packed OpenWebText), 5,000 samples per side, 1,024-token horizon, M=64. Dream 7B uses Lg=Lxi=8192; GPT-2 Large uses Lg=Lxi=1024. Soft-Masked uses ReMDM-loop; MDLM uses exact categorical sampling. Sampling budgets differ across models. Compare temperatures within each model and encoder.",
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    ],
    "protocol": "AG News articles from disjoint source pools. Reference P and starting candidate Q2 each contain 50% World and 50% Sports. On the Q2-to-Q1 path, replace an increasing share with World-only articles. On Q2-to-Q3, replace it with a mixture balanced across World, Sports, Business and Sci/Tech. All comparisons use 5,000 articles per corpus; the reference stays fixed within each dataset seed. Both encoders score the same articles with a 1,024-token cap, M=64 and raw RBF GP at lengthscale 0.1. GPT-2 Large: L=K=1024, scale 1280. Dream 7B: L=K=8192, scale 3584. Raw SOL scales differ between encoders. Nearby mixture points reuse articles. Topic percentages shown are averaged over the five dataset seeds.",
    "computed": "2026-09-23 (GPT-2 Large) and 2026-10-06 (Dream 7B)"
  }
}