加入实验输出（批次 7/14）

deep_learning/Q1/outputs/alignment_v2/v2_b/reconstruction_metrics_learned_only.csv

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deep_learning/Q1/outputs/alignment_v2/v2_b/reconstruction_metrics_with_fixed.csv

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deep_learning/Q1/outputs/alignment_v2/v2_b/retrieval_summary.csv

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deep_learning/Q1/outputs/alignment_v2/v2_b/run_manifest.json

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+    "No human event timestamps are available, so temporal probes do not replace manual annotation.",
+    "Slot-regularization losses impose weak temporal structure and must be interpreted alongside the unregularized M1/M2 reference."
+  ]
+}

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deep_learning/Q1/outputs/alignment_v2/v2_b/summary.json

@@ -0,0 +1,680 @@
+{
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deep_learning/Q1/outputs/alignment_v2/v2_c/README.md

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+# v2_c M3/M4 alignment variant
+
+M1/M2 rows in the comparison files are fixed references recomputed on the original grouped folds. Only M3/M4 training losses changed. See `training_history.csv` for per-epoch loss components and row-collapse scores. `retrieval_metrics_learned_only.csv` restricts candidates to the same clip. `reconstruction_metrics_learned_only.csv` contrasts aligned and shuffled non-target streams. The experiment does not include RoPE, Gaussian bias, or latent-length changes.

deep_learning/Q1/outputs/alignment_v2/v2_c/alignment_summary.csv

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