{
  "model": "Qwen3-1.7B base",
  "hidden_dimensions": 2048,
  "scope": {
    "inputs": "Saved per-token final RMSNorm rows, CPU BF16 captures converted to FP32 and unit-normalized exactly like viewer; saved means average normalized rows without normalizing the mean again.",
    "display_fit": "Actual app global basis; thin QR only removes floating-point non-orthogonality. Fit includes all151936 unit input embeddings and3690 means, not token paths.",
    "covariance": "CPU float64 sufficient statistics, centered separately for each reported dataset. A best affine plane may have an offset from the global fit center.",
    "display_plane": "Best2D plane inside the displayed3D subspace, not arbitrarily the first2 global axes.",
    "holdout": "80/20 topic split, seed43. Natural-language versions of a topic stay together. Code has8/2 separate topics. Held out for this plane fit, not guaranteed unseen during Qwen pretraining.",
    "scope": "These synthetic question+answer samples, selected layer and normalization only. No new model inference, training, device actions or downloads."
  },
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    "features": "Length-normalized final token states and saved conversation means; exact Global PCA basis from prior audit.",
    "language": "Requested dataset language, not independently verified output language. Code is a separate task set, excluded from language-only tests.",
    "frequency": "Observed token frequency in these saved examples, NOT pretraining-corpus frequency. Held-out regressions use training-topic counts only.",
    "heldout": "Same80/20 topic split as planarity audit; translations kept together; fixed axes were already fitted on the whole embedding+mean dataset.",
    "roles": "Fast-tokenizer character offsets verified against saved token IDs; Q/A boundary-crossing tokens are labeled boundary.",
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  "notes": [
    "Global fitting points are151936 normalized input embeddings plus3690 conversation means; token paths are projected afterward.",
    "80/20 topics are held out for geometry or association fits, not necessarily unseen in Qwen pretraining.",
    "Axis-association predictors are tested on held-out topics, while PCA axes were already fixed from the complete experiment.",
    "Code is a separate90-example set with English prompts; natural languages each have900 examples."
  ]
}