[start] samples=100 groups=37 folds=5 seeds=[42, 3407, 2026] device=cuda [fold 1/5] train=80 validation=20 train_video_ids=31 validation_video_ids=6 [fold 1] evaluate M1 and train identical probes [fold 1] evaluate M2 and train identical probes [fold 1] train M3, seed=42 In file included from /usr/include/python3.14/pyconfig.h:6, from /usr/include/python3.14/Python.h:14, from /home/gloamxun/modeling_zhaocui/Q1/.venv/lib/python3.14/site-packages/triton/backends/nvidia/driver.c:9: /usr/include/python3.14/pyconfig-64.h:2031:9: warning: ‘_POSIX_C_SOURCE’ redefined 2031 | #define _POSIX_C_SOURCE 200809L | ^~~~~~~~~~~~~~~ In file included from /usr/include/bits/libc-header-start.h:33, from /usr/include/stdlib.h:26, from /home/gloamxun/modeling_zhaocui/Q1/.venv/lib/python3.14/site-packages/triton/backends/nvidia/include/cuda.h:56, from /home/gloamxun/modeling_zhaocui/Q1/.venv/lib/python3.14/site-packages/triton/backends/nvidia/driver.c:1: /usr/include/features.h:319:10: note: this is the location of the previous definition 319 | # define _POSIX_C_SOURCE 202405L | ^~~~~~~~~~~~~~~ [fold 1] M3, seed=42 best_epoch=50 val_objective=4.93766; running frozen probes [fold 1] train M4, seed=42 [fold 1] M4, seed=42 best_epoch=4 val_objective=6.07687; running frozen probes [fold 1] train M3, seed=3407 [fold 1] M3, seed=3407 best_epoch=50 val_objective=4.98259; running frozen probes [fold 1] train M4, seed=3407 [fold 1] M4, seed=3407 best_epoch=4 val_objective=5.87185; running frozen probes [fold 1] train M3, seed=2026 [fold 1] M3, seed=2026 best_epoch=50 val_objective=5.10248; running frozen probes [fold 1] train M4, seed=2026 [fold 1] M4, seed=2026 best_epoch=3 val_objective=6.09901; running frozen probes [fold 2/5] train=80 validation=20 train_video_ids=30 validation_video_ids=7 [fold 2] evaluate M1 and train identical probes [fold 2] evaluate M2 and train identical probes [fold 2] train M3, seed=42 [fold 2] M3, seed=42 best_epoch=1 val_objective=6.12588; running frozen probes [fold 2] train M4, seed=42 [fold 2] M4, seed=42 best_epoch=1 val_objective=5.94268; running frozen probes [fold 2] train M3, seed=3407 [fold 2] M3, seed=3407 best_epoch=50 val_objective=5.14454; running frozen probes [fold 2] train M4, seed=3407 [fold 2] M4, seed=3407 best_epoch=3 val_objective=6.06774; running frozen probes [fold 2] train M3, seed=2026 [fold 2] M3, seed=2026 best_epoch=50 val_objective=5.17416; running frozen probes [fold 2] train M4, seed=2026 [fold 2] M4, seed=2026 best_epoch=1 val_objective=5.96431; running frozen probes [fold 3/5] train=80 validation=20 train_video_ids=29 validation_video_ids=8 [fold 3] evaluate M1 and train identical probes [fold 3] evaluate M2 and train identical probes [fold 3] train M3, seed=42 [fold 3] M3, seed=42 best_epoch=50 val_objective=4.97643; running frozen probes [fold 3] train M4, seed=42 [fold 3] M4, seed=42 best_epoch=3 val_objective=6.19582; running frozen probes [fold 3] train M3, seed=3407 [fold 3] M3, seed=3407 best_epoch=50 val_objective=4.65949; running frozen probes [fold 3] train M4, seed=3407 [fold 3] M4, seed=3407 best_epoch=9 val_objective=5.91066; running frozen probes [fold 3] train M3, seed=2026 [fold 3] M3, seed=2026 best_epoch=50 val_objective=4.99993; running frozen probes [fold 3] train M4, seed=2026 [fold 3] M4, seed=2026 best_epoch=3 val_objective=6.02409; running frozen probes [fold 4/5] train=80 validation=20 train_video_ids=29 validation_video_ids=8 [fold 4] evaluate M1 and train identical probes [fold 4] evaluate M2 and train identical probes [fold 4] train M3, seed=42 [fold 4] M3, seed=42 best_epoch=50 val_objective=5.15230; running frozen probes [fold 4] train M4, seed=42 [fold 4] M4, seed=42 best_epoch=1 val_objective=5.99857; running frozen probes [fold 4] train M3, seed=3407 [fold 4] M3, seed=3407 best_epoch=50 val_objective=5.09800; running frozen probes [fold 4] train M4, seed=3407 [fold 4] M4, seed=3407 best_epoch=2 val_objective=6.03456; running frozen probes [fold 4] train M3, seed=2026 [fold 4] M3, seed=2026 best_epoch=50 val_objective=5.26026; running frozen probes [fold 4] train M4, seed=2026 [fold 4] M4, seed=2026 best_epoch=2 val_objective=6.08121; running frozen probes [fold 5/5] train=80 validation=20 train_video_ids=29 validation_video_ids=8 [fold 5] evaluate M1 and train identical probes [fold 5] evaluate M2 and train identical probes [fold 5] train M3, seed=42 [fold 5] M3, seed=42 best_epoch=50 val_objective=5.29874; running frozen probes [fold 5] train M4, seed=42 [fold 5] M4, seed=42 best_epoch=4 val_objective=6.02189; running frozen probes [fold 5] train M3, seed=3407 [fold 5] M3, seed=3407 best_epoch=50 val_objective=4.95355; running frozen probes [fold 5] train M4, seed=3407 [fold 5] M4, seed=3407 best_epoch=8 val_objective=6.05872; running frozen probes [fold 5] train M3, seed=2026 [fold 5] M3, seed=2026 best_epoch=50 val_objective=5.13369; running frozen probes [fold 5] train M4, seed=2026 [fold 5] M4, seed=2026 best_epoch=1 val_objective=5.88269; running frozen probes [done] elapsed_seconds=516.3 output=/home/gloamxun/modeling_zhaocui/Q1/outputs/method_comparison { "created_utc": "2026-09-23T09:07:49.626404+00:00", "sample_count": 100, "group_count": 37, "folds": 5, "seeds": [ 42, 3407, 2026 ], "device": "cuda", "gpu_name": "NVIDIA GeForce RTX 5070 Ti", "python": "3.14.7", "torch": "2.14.0+cu130", "parameters": { "grid_size": 50, "hidden_size": 128, "heads": 4, "dropout": 0.1, "batch_size": 8, "epochs_max": 50, "early_stopping_patience": 8, "learning_rate": 0.0001, "mask_ratio": 0.2, "retrieval_probe_epochs": 20, "reconstruction_probe_epochs": 25, "mvr_epsilon": 0.02 }, "objective": "masked reconstruction + cross-modal contrastive + temporal monotonicity; emotion labels unused", "split_rule": "GroupKFold by group_id/video_id", "example_sample_id": "-tPCytz4rww/12", "elapsed_seconds": 516.2944533824921, "interpretation_limits": [ "Grid-index retrieval is a representation-consistency probe, not independent temporal ground truth.", "Masked reconstruction uses a decoder trained on the training fold and reports standardized-feature errors.", "The emotion probe is a small-sample downstream utility check, not a claim of generalization to MOSEI.", "No human event timestamps are available, so human IoU/MATE is not reported." ] }