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j1s4nn
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jevgag-seamcontrol

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Cannot load the dataset split (in streaming mode) to extract the first rows.

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
n_segments: int64
n_prompts: int64
variants: struct<none: struct<seam_mean: double, seam_ci: list<item: double>, fidelity_mean: double, fidelity_ (... 1182 chars omitted)
  child 0, none: struct<seam_mean: double, seam_ci: list<item: double>, fidelity_mean: double, fidelity_ci: list<item (... 144 chars omitted)
      child 0, seam_mean: double
      child 1, seam_ci: list<item: double>
          child 0, item: double
      child 2, fidelity_mean: double
      child 3, fidelity_ci: list<item: double>
          child 0, item: double
      child 4, seam_delta_vs_none: double
      child 5, seam_delta_ci: list<item: double>
          child 0, item: double
      child 6, fidelity_delta_vs_none: double
      child 7, fidelity_delta_ci: list<item: double>
          child 0, item: double
  child 1, blend0.25: struct<seam_mean: double, seam_ci: list<item: double>, fidelity_mean: double, fidelity_ci: list<item (... 144 chars omitted)
      child 0, seam_mean: double
      child 1, seam_ci: list<item: double>
          child 0, item: double
      child 2, fidelity_mean: double
      child 3, fidelity_ci: list<item: double>
          child 0, item: double
      child 4, seam_delta_vs_none: double
      child 5, seam_delta_ci: list<item: double>
          child 0, item: double
      child 6, fidelity_delta_vs_none: double
      child 7, fidelity_delta_ci: list<item: double>
          child 0, item: double
  child 2, blend0.5: struct<seam_mean: double, seam_ci: list<item: double>, fidelity
...
        child 0, item: double
reference: struct<seam_fine_mean: double, interior_fine_mean: double, seam_coarse_mean: double, interior_coarse (... 62 chars omitted)
  child 0, seam_fine_mean: double
  child 1, interior_fine_mean: double
  child 2, seam_coarse_mean: double
  child 3, interior_coarse_mean: double
  child 4, fid_fine_mean: double
  child 5, fid_coarse_mean: double
n_rows: int64
by_hw: struct<T4: struct<n: int64, n_switched_segments: int64, mean_adj_l1_switched: double, mean_adj_l1_in (... 336 chars omitted)
  child 0, T4: struct<n: int64, n_switched_segments: int64, mean_adj_l1_switched: double, mean_adj_l1_interior: dou (... 108 chars omitted)
      child 0, n: int64
      child 1, n_switched_segments: int64
      child 2, mean_adj_l1_switched: double
      child 3, mean_adj_l1_interior: double
      child 4, seam_ratio: double
      child 5, seam_diff_ci: list<item: double>
          child 0, item: double
      child 6, spearman_psi_vs_adj: double
      child 7, mean_ref_l1: double
  child 1, A100: struct<n: int64, n_switched_segments: int64, mean_adj_l1_switched: double, mean_adj_l1_interior: dou (... 108 chars omitted)
      child 0, n: int64
      child 1, n_switched_segments: int64
      child 2, mean_adj_l1_switched: double
      child 3, mean_adj_l1_interior: double
      child 4, seam_ratio: double
      child 5, seam_diff_ci: list<item: double>
          child 0, item: double
      child 6, spearman_psi_vs_adj: double
      child 7, mean_ref_l1: double
to
{'n_rows': Value('int64'), 'by_hw': {'T4': {'n': Value('int64'), 'n_switched_segments': Value('int64'), 'mean_adj_l1_switched': Value('float64'), 'mean_adj_l1_interior': Value('float64'), 'seam_ratio': Value('float64'), 'seam_diff_ci': List(Value('float64')), 'spearman_psi_vs_adj': Value('float64'), 'mean_ref_l1': Value('float64')}, 'A100': {'n': Value('int64'), 'n_switched_segments': Value('int64'), 'mean_adj_l1_switched': Value('float64'), 'mean_adj_l1_interior': Value('float64'), 'seam_ratio': Value('float64'), 'seam_diff_ci': List(Value('float64')), 'spearman_psi_vs_adj': Value('float64'), 'mean_ref_l1': Value('float64')}}}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 544, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 3052, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 558, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 361, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2381, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              n_segments: int64
              n_prompts: int64
              variants: struct<none: struct<seam_mean: double, seam_ci: list<item: double>, fidelity_mean: double, fidelity_ (... 1182 chars omitted)
                child 0, none: struct<seam_mean: double, seam_ci: list<item: double>, fidelity_mean: double, fidelity_ci: list<item (... 144 chars omitted)
                    child 0, seam_mean: double
                    child 1, seam_ci: list<item: double>
                        child 0, item: double
                    child 2, fidelity_mean: double
                    child 3, fidelity_ci: list<item: double>
                        child 0, item: double
                    child 4, seam_delta_vs_none: double
                    child 5, seam_delta_ci: list<item: double>
                        child 0, item: double
                    child 6, fidelity_delta_vs_none: double
                    child 7, fidelity_delta_ci: list<item: double>
                        child 0, item: double
                child 1, blend0.25: struct<seam_mean: double, seam_ci: list<item: double>, fidelity_mean: double, fidelity_ci: list<item (... 144 chars omitted)
                    child 0, seam_mean: double
                    child 1, seam_ci: list<item: double>
                        child 0, item: double
                    child 2, fidelity_mean: double
                    child 3, fidelity_ci: list<item: double>
                        child 0, item: double
                    child 4, seam_delta_vs_none: double
                    child 5, seam_delta_ci: list<item: double>
                        child 0, item: double
                    child 6, fidelity_delta_vs_none: double
                    child 7, fidelity_delta_ci: list<item: double>
                        child 0, item: double
                child 2, blend0.5: struct<seam_mean: double, seam_ci: list<item: double>, fidelity
              ...
                      child 0, item: double
              reference: struct<seam_fine_mean: double, interior_fine_mean: double, seam_coarse_mean: double, interior_coarse (... 62 chars omitted)
                child 0, seam_fine_mean: double
                child 1, interior_fine_mean: double
                child 2, seam_coarse_mean: double
                child 3, interior_coarse_mean: double
                child 4, fid_fine_mean: double
                child 5, fid_coarse_mean: double
              n_rows: int64
              by_hw: struct<T4: struct<n: int64, n_switched_segments: int64, mean_adj_l1_switched: double, mean_adj_l1_in (... 336 chars omitted)
                child 0, T4: struct<n: int64, n_switched_segments: int64, mean_adj_l1_switched: double, mean_adj_l1_interior: dou (... 108 chars omitted)
                    child 0, n: int64
                    child 1, n_switched_segments: int64
                    child 2, mean_adj_l1_switched: double
                    child 3, mean_adj_l1_interior: double
                    child 4, seam_ratio: double
                    child 5, seam_diff_ci: list<item: double>
                        child 0, item: double
                    child 6, spearman_psi_vs_adj: double
                    child 7, mean_ref_l1: double
                child 1, A100: struct<n: int64, n_switched_segments: int64, mean_adj_l1_switched: double, mean_adj_l1_interior: dou (... 108 chars omitted)
                    child 0, n: int64
                    child 1, n_switched_segments: int64
                    child 2, mean_adj_l1_switched: double
                    child 3, mean_adj_l1_interior: double
                    child 4, seam_ratio: double
                    child 5, seam_diff_ci: list<item: double>
                        child 0, item: double
                    child 6, spearman_psi_vs_adj: double
                    child 7, mean_ref_l1: double
              to
              {'n_rows': Value('int64'), 'by_hw': {'T4': {'n': Value('int64'), 'n_switched_segments': Value('int64'), 'mean_adj_l1_switched': Value('float64'), 'mean_adj_l1_interior': Value('float64'), 'seam_ratio': Value('float64'), 'seam_diff_ci': List(Value('float64')), 'spearman_psi_vs_adj': Value('float64'), 'mean_ref_l1': Value('float64')}, 'A100': {'n': Value('int64'), 'n_switched_segments': Value('int64'), 'mean_adj_l1_switched': Value('float64'), 'mean_adj_l1_interior': Value('float64'), 'seam_ratio': Value('float64'), 'seam_diff_ci': List(Value('float64')), 'spearman_psi_vs_adj': Value('float64'), 'mean_ref_l1': Value('float64')}}}
              because column names don't match

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JevGAG SeamControl Seam Repair Measurements

Boundary-seam measurements and training-free seam-repair ablation

JevGAG SeamControl Seam Repair Measurements

Boundary-seam measurements and a training-free seam-repair ablation for SeamControl. In a matched design (same boundary, fine c1-1_step2 versus coarse c3-3_step2), switching to coarse produces a boundary discontinuity 2.9x larger than fine's.

A 25% latent blend of the coarse segment's first frame toward the boundary cuts the seam by 25% and slightly improves fidelity (both confidence intervals exclude 0). Relevant to adaptive temporal granularity in causal video generation and video world models.

measurements.npz, manifest.json
e1_seam.json, e2_repair.json
fig_seam_repair.png
DATASET_NOTES.md: original experiment notes

Related resources

Code (JevGAG programme): https://github.com/j1s4nn/JevGAG
Code (Jev-Routed Temporal Granularity): https://github.com/j1s4nn/jev-adaptive-granularity-WM
Project page (JevGAG): https://j1s4nn.github.io/JevGAG/
Project page (Paper 1): https://j1s4nn.github.io/jev-adaptive-granularity-WM/
Portfolio: https://j1s4nn.github.io
Source (Kaggle): https://www.kaggle.com/datasets/ajjisan/jevgag-seamcontrol
Preprint (JevGAG): https://doi.org/10.5281/zenodo.23693517
Preprint (Jev-Routed Temporal Granularity): https://doi.org/10.5281/zenodo.23395414

Citation

@misc{hossen_jevgag_ddag,
  author       = {Hossen, MD Jisan},
  title        = {Regime-Aware Temporal Granularity for Causal Video World Models: When Does Adaptivity Pay?},
  year         = {2026},
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.23693517},
  url          = {https://doi.org/10.5281/zenodo.23693517}
}