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ddag-experiments

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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_positive: int64
variants: struct<prefill_norm: struct<auroc: double, ci: list<item: double>, perm_p: double>, step_first: stru (... 512 chars omitted)
  child 0, prefill_norm: struct<auroc: double, ci: list<item: double>, perm_p: double>
      child 0, auroc: double
      child 1, ci: list<item: double>
          child 0, item: double
      child 2, perm_p: double
  child 1, step_first: struct<auroc: double, ci: list<item: double>, perm_p: double>
      child 0, auroc: double
      child 1, ci: list<item: double>
          child 0, item: double
      child 2, perm_p: double
  child 2, step_last: struct<auroc: double, ci: list<item: double>, perm_p: double>
      child 0, auroc: double
      child 1, ci: list<item: double>
          child 0, item: double
      child 2, perm_p: double
  child 3, step_mean: struct<auroc: double, ci: list<item: double>, perm_p: double>
      child 0, auroc: double
      child 1, ci: list<item: double>
          child 0, item: double
      child 2, perm_p: double
  child 4, step_std: struct<auroc: double, ci: list<item: double>, perm_p: double>
      child 0, auroc: double
      child 1, ci: list<item: double>
          child 0, item: double
      child 2, perm_p: double
  child 5, step_max: struct<auroc: double, ci: list<item: double>, perm_p: double>
      child 0, auroc: double
      child 1, ci: list<item: double>
          child 0, item: double
      child 2, perm_p: double
  child 6, step_range: struct<auroc: double, ci: 
...
      child 2, note: string
  child 1, r_conv_alone: struct<auroc: double, ci: list<item: double>>
      child 0, auroc: double
      child 1, ci: list<item: double>
          child 0, item: double
  child 2, probe+r_conv: struct<auroc: double, ci: list<item: double>>
      child 0, auroc: double
      child 1, ci: list<item: double>
          child 0, item: double
  child 3, r_conv+free_evidence: struct<auroc: double, ci: list<item: double>>
      child 0, auroc: double
      child 1, ci: list<item: double>
          child 0, item: double
  child 4, probe+r_conv+evidence: struct<auroc: double, ci: list<item: double>>
      child 0, auroc: double
      child 1, ci: list<item: double>
          child 0, item: double
n_prompts: int64
best_feasible: struct<quantile: double, tau: double, rho: double, error_among_accepted: double>
  child 0, quantile: double
  child 1, tau: double
  child 2, rho: double
  child 3, error_among_accepted: double
r_conv_stats: struct<mean: double, std: double, corr_with_label: double>
  child 0, mean: double
  child 1, std: double
  child 2, corr_with_label: double
probe_block11_direct_auroc: double
feasible_at_target: bool
calibration: list<item: struct<quantile: double, tau: double, rho: double, error_among_accepted: double>>
  child 0, item: struct<quantile: double, tau: double, rho: double, error_among_accepted: double>
      child 0, quantile: double
      child 1, tau: double
      child 2, rho: double
      child 3, error_among_accepted: double
to
{'n_segments': Value('int64'), 'n_positive': Value('int64'), 'n_prompts': Value('int64'), 'probe_block11_direct_auroc': Value('float64'), 'r_conv_stats': {'mean': Value('float64'), 'std': Value('float64'), 'corr_with_label': Value('float64')}, 'auroc': {'probe_alone': {'auroc': Value('float64'), 'ci': List(Value('float64')), 'note': Value('string')}, 'r_conv_alone': {'auroc': Value('float64'), 'ci': List(Value('float64'))}, 'probe+r_conv': {'auroc': Value('float64'), 'ci': List(Value('float64'))}, 'r_conv+free_evidence': {'auroc': Value('float64'), 'ci': List(Value('float64'))}, 'probe+r_conv+evidence': {'auroc': Value('float64'), 'ci': List(Value('float64'))}}, 'calibration': List({'quantile': Value('float64'), 'tau': Value('float64'), 'rho': Value('float64'), 'error_among_accepted': Value('float64')}), 'feasible_at_target': Value('bool'), 'best_feasible': {'quantile': Value('float64'), 'tau': Value('float64'), 'rho': Value('float64'), 'error_among_accepted': Value('float64')}, 'reference': {'chance': Value('float64'), 'hand_crafted': Value('float64'), 'jev_text': Value('float64'), 'selfaware_probe': Value('float64')}, 'verdict': {'H1_supported': Value('bool'), 'beats_probe': Value('bool'), 'cost_win_feasible': Value('bool')}}
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_positive: int64
              variants: struct<prefill_norm: struct<auroc: double, ci: list<item: double>, perm_p: double>, step_first: stru (... 512 chars omitted)
                child 0, prefill_norm: struct<auroc: double, ci: list<item: double>, perm_p: double>
                    child 0, auroc: double
                    child 1, ci: list<item: double>
                        child 0, item: double
                    child 2, perm_p: double
                child 1, step_first: struct<auroc: double, ci: list<item: double>, perm_p: double>
                    child 0, auroc: double
                    child 1, ci: list<item: double>
                        child 0, item: double
                    child 2, perm_p: double
                child 2, step_last: struct<auroc: double, ci: list<item: double>, perm_p: double>
                    child 0, auroc: double
                    child 1, ci: list<item: double>
                        child 0, item: double
                    child 2, perm_p: double
                child 3, step_mean: struct<auroc: double, ci: list<item: double>, perm_p: double>
                    child 0, auroc: double
                    child 1, ci: list<item: double>
                        child 0, item: double
                    child 2, perm_p: double
                child 4, step_std: struct<auroc: double, ci: list<item: double>, perm_p: double>
                    child 0, auroc: double
                    child 1, ci: list<item: double>
                        child 0, item: double
                    child 2, perm_p: double
                child 5, step_max: struct<auroc: double, ci: list<item: double>, perm_p: double>
                    child 0, auroc: double
                    child 1, ci: list<item: double>
                        child 0, item: double
                    child 2, perm_p: double
                child 6, step_range: struct<auroc: double, ci: 
              ...
                    child 2, note: string
                child 1, r_conv_alone: struct<auroc: double, ci: list<item: double>>
                    child 0, auroc: double
                    child 1, ci: list<item: double>
                        child 0, item: double
                child 2, probe+r_conv: struct<auroc: double, ci: list<item: double>>
                    child 0, auroc: double
                    child 1, ci: list<item: double>
                        child 0, item: double
                child 3, r_conv+free_evidence: struct<auroc: double, ci: list<item: double>>
                    child 0, auroc: double
                    child 1, ci: list<item: double>
                        child 0, item: double
                child 4, probe+r_conv+evidence: struct<auroc: double, ci: list<item: double>>
                    child 0, auroc: double
                    child 1, ci: list<item: double>
                        child 0, item: double
              n_prompts: int64
              best_feasible: struct<quantile: double, tau: double, rho: double, error_among_accepted: double>
                child 0, quantile: double
                child 1, tau: double
                child 2, rho: double
                child 3, error_among_accepted: double
              r_conv_stats: struct<mean: double, std: double, corr_with_label: double>
                child 0, mean: double
                child 1, std: double
                child 2, corr_with_label: double
              probe_block11_direct_auroc: double
              feasible_at_target: bool
              calibration: list<item: struct<quantile: double, tau: double, rho: double, error_among_accepted: double>>
                child 0, item: struct<quantile: double, tau: double, rho: double, error_among_accepted: double>
                    child 0, quantile: double
                    child 1, tau: double
                    child 2, rho: double
                    child 3, error_among_accepted: double
              to
              {'n_segments': Value('int64'), 'n_positive': Value('int64'), 'n_prompts': Value('int64'), 'probe_block11_direct_auroc': Value('float64'), 'r_conv_stats': {'mean': Value('float64'), 'std': Value('float64'), 'corr_with_label': Value('float64')}, 'auroc': {'probe_alone': {'auroc': Value('float64'), 'ci': List(Value('float64')), 'note': Value('string')}, 'r_conv_alone': {'auroc': Value('float64'), 'ci': List(Value('float64'))}, 'probe+r_conv': {'auroc': Value('float64'), 'ci': List(Value('float64'))}, 'r_conv+free_evidence': {'auroc': Value('float64'), 'ci': List(Value('float64'))}, 'probe+r_conv+evidence': {'auroc': Value('float64'), 'ci': List(Value('float64'))}}, 'calibration': List({'quantile': Value('float64'), 'tau': Value('float64'), 'rho': Value('float64'), 'error_among_accepted': Value('float64')}), 'feasible_at_target': Value('bool'), 'best_feasible': {'quantile': Value('float64'), 'tau': Value('float64'), 'rho': Value('float64'), 'error_among_accepted': Value('float64')}, 'reference': {'chance': Value('float64'), 'hand_crafted': Value('float64'), 'jev_text': Value('float64'), 'selfaware_probe': Value('float64')}, 'verdict': {'H1_supported': Value('bool'), 'beats_probe': Value('bool'), 'cost_win_feasible': Value('bool')}}
              because column names don't match

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DDAG Adaptive Temporal Granularity Experiments

Audited experiments for Deadline- and Drift-Aware Adaptive Granularity

DDAG Adaptive Temporal Granularity Experiments

Consolidated, audited results for DDAG (Deadline- and Drift-Aware Adaptive Temporal Granularity), the method part of the JevGAG + DDAG programme.

Eleven experiments on causal video world models across a Tesla T4 and an A100-80GB: intrinsic pre-generation probe, seam repair, closed-loop evidence, escalation gate, convergence residual, drift propagation, online policy, intermediate band, decoded-pixel endpoint, typed Jev critic, and the flicker endpoint. Includes the publication figures (architecture, regime map, signal, drift, seam) in PDF and PNG. Every headline number re-derives from a stored result file.

README_E2_*.md, README_E3_drift.md, README_E5_*.md, README_E7_jcd.md, README_E8_flicker.md: per-experiment notes
a100/, band_t4/: per-hardware result JSON and figures
drift_*.npz, drift_manifest_t4.json: drift measurements
DATASET_NOTES.md: original dataset 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/ddag-experiments
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}
}