加入实验输出(批次 2/14)

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2026-09-23 23:32:30 +08:00
parent d3bf93d410
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{
"created_utc": "2026-09-23T10:26:41.743208+00:00",
"sample_count": 100,
"diagnostic_sample": "-tPCytz4rww/12",
"seed": 42,
"device": "cuda",
"gpu_name": "NVIDIA GeForce RTX 5070 Ti",
"python": "3.14.7",
"torch": "2.14.0+cu130",
"experiments": [
{
"name": "D0",
"scope": "single sample; span + barycenter band only",
"steps_per_model": 1000,
"loss": "5 * L_span + 10 * L_band"
},
{
"name": "D1",
"scope": "single sample; Gaussian target KL only",
"steps_per_model": 1000,
"sigma_normalized_time": 0.1,
"M4_control": "no PE versus fixed sinusoidal PE"
},
{
"name": "D2",
"scope": "all 100 clips; Gaussian target KL only; one seed; in-sample diagnostic",
"steps_per_model": 500,
"sigma_normalized_time": 0.1,
"M4_position_encoding": "fixed sinusoidal"
},
{
"name": "D3",
"scope": "all 100 clips; Gaussian KL + masked reconstruction + contrastive; one seed; in-sample diagnostic",
"steps_per_model": 500,
"sigma_normalized_time": 0.1,
"M4_position_encoding": "fixed sinusoidal"
}
],
"optimizer": "AdamW",
"learning_rate": 0.001,
"dropout": 0.0,
"batch_size": 8,
"gradient_logging_interval_steps": 20,
"gradient_metrics": [
"W_Q",
"W_K",
"M4 latent slots Z"
],
"features_changed": false,
"M1_M2_changed": false,
"elapsed_seconds": 103.73381090164185,
"interpretation_limits": [
"D0 and D1 overfit one selected sample and diagnose optimization/representability only.",
"D2 and D3 train and evaluate on the same 100 clips; they diagnose whether the target can be optimized, not generalization.",
"Gaussian targets are weak temporal priors constructed from timestamps; they are not human alignment ground truth.",
"M4 absolute sinusoidal encoding is enabled only for D1's PE control and D2/D3; prior M1-M4 and v2 results are unchanged."
]
}