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j1s4nn
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jevgag-closedloop-gran

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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
run_id: string
kernel: string
protocol: struct<seed: int64, horizon: int64, limit: int64, n_ref_steps: int64, n_fine_steps: int64, n_coarse_ (... 135 chars omitted)
  child 0, seed: int64
  child 1, horizon: int64
  child 2, limit: int64
  child 3, n_ref_steps: int64
  child 4, n_fine_steps: int64
  child 5, n_coarse_steps: int64
  child 6, fine_ckpt: string
  child 7, coarse_ckpt: string
  child 8, ref_ckpt: string
  child 9, label: string
  child 10, label_space: string
  child 11, labels: list<item: string>
      child 0, item: string
records: list<item: struct<prompt_idx: int64, stratum: string, prompt: string, segment: int64, l1_fine: doubl (... 445 chars omitted)
  child 0, item: struct<prompt_idx: int64, stratum: string, prompt: string, segment: int64, l1_fine: double, l1_coars (... 433 chars omitted)
      child 0, prompt_idx: int64
      child 1, stratum: string
      child 2, prompt: string
      child 3, segment: int64
      child 4, l1_fine: double
      child 5, l1_coarse: double
      child 6, label_coarse_unsafe: int64
      child 7, ll1_fine: double
      child 8, ll1_coarse: double
      child 9, label_latent: int64
      child 10, jitter_fine: double
      child 11, jitter_coarse: double
      child 12, label_smooth: int64
      child 13, lj_fine: double
      child 14, lj_coarse: double
      child 15, label_latent_smooth: int64
      child 16, disc_coarse_fine: double
      child 17, seam_coarse: double
      child 18, seam_fine: double
      child 19, int
...
uble
  child 2, jev_text: double
  child 3, selfaware_dit_prefill_probe: double
tests: struct<A_prior_open_loop: struct<n: int64, auroc: double, ci: list<item: double>>, B_evidence_prev_o (... 397 chars omitted)
  child 0, A_prior_open_loop: struct<n: int64, auroc: double, ci: list<item: double>>
      child 0, n: int64
      child 1, auroc: double
      child 2, ci: list<item: double>
          child 0, item: double
  child 1, B_evidence_prev_only: struct<n: int64, auroc: double, ci: list<item: double>>
      child 0, n: int64
      child 1, auroc: double
      child 2, ci: list<item: double>
          child 0, item: double
  child 2, C_closed_loop_prior+ev_prev: struct<n: int64, auroc: double, ci: list<item: double>>
      child 0, n: int64
      child 1, auroc: double
      child 2, ci: list<item: double>
          child 0, item: double
  child 3, A2_prior_on_causal_subset: struct<n: int64, auroc: double, ci: list<item: double>>
      child 0, n: int64
      child 1, auroc: double
      child 2, ci: list<item: double>
          child 0, item: double
  child 4, D_prior+ev_same_noncausal: struct<n: int64, auroc: double, ci: list<item: double>>
      child 0, n: int64
      child 1, auroc: double
      child 2, ci: list<item: double>
          child 0, item: double
  child 5, E_closed_loop+disc_prev: struct<n: int64, auroc: double, ci: list<item: double>>
      child 0, n: int64
      child 1, auroc: double
      child 2, ci: list<item: double>
          child 0, item: double
to
{'n_segments': Value('int64'), 'n_causal_pairs': Value('int64'), 'n_positive': Value('int64'), 'tests': {'A_prior_open_loop': {'n': Value('int64'), 'auroc': Value('float64'), 'ci': List(Value('float64'))}, 'B_evidence_prev_only': {'n': Value('int64'), 'auroc': Value('float64'), 'ci': List(Value('float64'))}, 'C_closed_loop_prior+ev_prev': {'n': Value('int64'), 'auroc': Value('float64'), 'ci': List(Value('float64'))}, 'A2_prior_on_causal_subset': {'n': Value('int64'), 'auroc': Value('float64'), 'ci': List(Value('float64'))}, 'D_prior+ev_same_noncausal': {'n': Value('int64'), 'auroc': Value('float64'), 'ci': List(Value('float64'))}, 'E_closed_loop+disc_prev': {'n': Value('int64'), 'auroc': Value('float64'), 'ci': List(Value('float64'))}}, 'reference_points': {'chance': Value('float64'), 'hand_crafted': Value('float64'), 'jev_text': Value('float64'), 'selfaware_dit_prefill_probe': 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
              run_id: string
              kernel: string
              protocol: struct<seed: int64, horizon: int64, limit: int64, n_ref_steps: int64, n_fine_steps: int64, n_coarse_ (... 135 chars omitted)
                child 0, seed: int64
                child 1, horizon: int64
                child 2, limit: int64
                child 3, n_ref_steps: int64
                child 4, n_fine_steps: int64
                child 5, n_coarse_steps: int64
                child 6, fine_ckpt: string
                child 7, coarse_ckpt: string
                child 8, ref_ckpt: string
                child 9, label: string
                child 10, label_space: string
                child 11, labels: list<item: string>
                    child 0, item: string
              records: list<item: struct<prompt_idx: int64, stratum: string, prompt: string, segment: int64, l1_fine: doubl (... 445 chars omitted)
                child 0, item: struct<prompt_idx: int64, stratum: string, prompt: string, segment: int64, l1_fine: double, l1_coars (... 433 chars omitted)
                    child 0, prompt_idx: int64
                    child 1, stratum: string
                    child 2, prompt: string
                    child 3, segment: int64
                    child 4, l1_fine: double
                    child 5, l1_coarse: double
                    child 6, label_coarse_unsafe: int64
                    child 7, ll1_fine: double
                    child 8, ll1_coarse: double
                    child 9, label_latent: int64
                    child 10, jitter_fine: double
                    child 11, jitter_coarse: double
                    child 12, label_smooth: int64
                    child 13, lj_fine: double
                    child 14, lj_coarse: double
                    child 15, label_latent_smooth: int64
                    child 16, disc_coarse_fine: double
                    child 17, seam_coarse: double
                    child 18, seam_fine: double
                    child 19, int
              ...
              uble
                child 2, jev_text: double
                child 3, selfaware_dit_prefill_probe: double
              tests: struct<A_prior_open_loop: struct<n: int64, auroc: double, ci: list<item: double>>, B_evidence_prev_o (... 397 chars omitted)
                child 0, A_prior_open_loop: struct<n: int64, auroc: double, ci: list<item: double>>
                    child 0, n: int64
                    child 1, auroc: double
                    child 2, ci: list<item: double>
                        child 0, item: double
                child 1, B_evidence_prev_only: struct<n: int64, auroc: double, ci: list<item: double>>
                    child 0, n: int64
                    child 1, auroc: double
                    child 2, ci: list<item: double>
                        child 0, item: double
                child 2, C_closed_loop_prior+ev_prev: struct<n: int64, auroc: double, ci: list<item: double>>
                    child 0, n: int64
                    child 1, auroc: double
                    child 2, ci: list<item: double>
                        child 0, item: double
                child 3, A2_prior_on_causal_subset: struct<n: int64, auroc: double, ci: list<item: double>>
                    child 0, n: int64
                    child 1, auroc: double
                    child 2, ci: list<item: double>
                        child 0, item: double
                child 4, D_prior+ev_same_noncausal: struct<n: int64, auroc: double, ci: list<item: double>>
                    child 0, n: int64
                    child 1, auroc: double
                    child 2, ci: list<item: double>
                        child 0, item: double
                child 5, E_closed_loop+disc_prev: struct<n: int64, auroc: double, ci: list<item: double>>
                    child 0, n: int64
                    child 1, auroc: double
                    child 2, ci: list<item: double>
                        child 0, item: double
              to
              {'n_segments': Value('int64'), 'n_causal_pairs': Value('int64'), 'n_positive': Value('int64'), 'tests': {'A_prior_open_loop': {'n': Value('int64'), 'auroc': Value('float64'), 'ci': List(Value('float64'))}, 'B_evidence_prev_only': {'n': Value('int64'), 'auroc': Value('float64'), 'ci': List(Value('float64'))}, 'C_closed_loop_prior+ev_prev': {'n': Value('int64'), 'auroc': Value('float64'), 'ci': List(Value('float64'))}, 'A2_prior_on_causal_subset': {'n': Value('int64'), 'auroc': Value('float64'), 'ci': List(Value('float64'))}, 'D_prior+ev_same_noncausal': {'n': Value('int64'), 'auroc': Value('float64'), 'ci': List(Value('float64'))}, 'E_closed_loop+disc_prev': {'n': Value('int64'), 'auroc': Value('float64'), 'ci': List(Value('float64'))}}, 'reference_points': {'chance': Value('float64'), 'hand_crafted': Value('float64'), 'jev_text': Value('float64'), 'selfaware_dit_prefill_probe': Value('float64')}}
              because column names don't match

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JevGAG ClosedLoop-Gran Evidence Capture

Closed-loop probe features and post-generation evidence captures

JevGAG ClosedLoop-Gran Evidence Capture

Closed-loop temporal-granularity experiment for ClosedLoop-Gran. Captures intrinsic pre-generation probe features plus free post-generation evidence (boundary seam, interior jitter, coarse-vs-fine discrepancy) for 64 matched segments on a Kaggle Tesla T4.

Free evidence from the previous segment alone predicts the next segment's coarse-unsafety at AUROC 0.725; closing the loop improves the open loop from 0.690 to 0.737 on the causal subset. This is direct evidence for adaptive temporal granularity in causal video generation and video world models.

features.npz, manifest.json
e1_closedloop.json: closed-loop results
fig_closedloop.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-closedloop-gran
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}
}