58 lines
1.9 KiB
JSON
58 lines
1.9 KiB
JSON
{
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"created_utc": "2026-09-23T10:26:41.743208+00:00",
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"sample_count": 100,
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"diagnostic_sample": "-tPCytz4rww/12",
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"seed": 42,
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"device": "cuda",
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"gpu_name": "NVIDIA GeForce RTX 5070 Ti",
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"python": "3.14.7",
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"torch": "2.14.0+cu130",
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"experiments": [
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{
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"name": "D0",
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"scope": "single sample; span + barycenter band only",
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"steps_per_model": 1000,
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"loss": "5 * L_span + 10 * L_band"
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},
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{
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"name": "D1",
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"scope": "single sample; Gaussian target KL only",
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"steps_per_model": 1000,
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"sigma_normalized_time": 0.1,
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"M4_control": "no PE versus fixed sinusoidal PE"
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},
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{
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"name": "D2",
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"scope": "all 100 clips; Gaussian target KL only; one seed; in-sample diagnostic",
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"steps_per_model": 500,
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"sigma_normalized_time": 0.1,
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"M4_position_encoding": "fixed sinusoidal"
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},
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{
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"name": "D3",
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"scope": "all 100 clips; Gaussian KL + masked reconstruction + contrastive; one seed; in-sample diagnostic",
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"steps_per_model": 500,
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"sigma_normalized_time": 0.1,
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"M4_position_encoding": "fixed sinusoidal"
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}
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],
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"optimizer": "AdamW",
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"learning_rate": 0.001,
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"dropout": 0.0,
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"batch_size": 8,
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"gradient_logging_interval_steps": 20,
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"gradient_metrics": [
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"W_Q",
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"W_K",
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"M4 latent slots Z"
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],
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"features_changed": false,
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"M1_M2_changed": false,
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"elapsed_seconds": 103.73381090164185,
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"interpretation_limits": [
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"D0 and D1 overfit one selected sample and diagnose optimization/representability only.",
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"D2 and D3 train and evaluate on the same 100 clips; they diagnose whether the target can be optimized, not generalization.",
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"Gaussian targets are weak temporal priors constructed from timestamps; they are not human alignment ground truth.",
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"M4 absolute sinusoidal encoding is enabled only for D1's PE control and D2/D3; prior M1-M4 and v2 results are unchanged."
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]
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} |