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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
_comment: string
direction: string
poster: string
hue_centers: struct<accent: int64, emph: int64>
  child 0, accent: int64
  child 1, emph: int64
base_hex: struct<accent: string, accent_deep: string, accent_light: string, accent_soft: string, emph: string, (... 56 chars omitted)
  child 0, accent: string
  child 1, accent_deep: string
  child 2, accent_light: string
  child 3, accent_soft: string
  child 4, emph: string
  child 5, emph_soft: string
  child 6, emph_light: string
  child 7, bg_page: string
semantics: struct<accent: string, emph: string>
  child 0, accent: string
  child 1, emph: string
dark_ground: bool
skill: string
schema_version: int64
gates: list<item: struct<name: string, severity: string, status: string, command: list<item: string>, summa (... 229 chars omitted)
  child 0, item: struct<name: string, severity: string, status: string, command: list<item: string>, summary: struct< (... 217 chars omitted)
      child 0, name: string
      child 1, severity: string
      child 2, status: string
      child 3, command: list<item: string>
          child 0, item: string
      child 4, summary: struct<exit_code: int64, tail: string, gate: string, status: string, rules: list<item: struct<id: in (... 73 chars omitted)
          child 0, exit_code: int64
          child 1, tail: string
          child 2, gate: string
          child 3, status: string
          child 4, rules: list<item: struct<id: int64, severity: string, status: string, detail: string>>
              child 0, item: struct<id: int64, severity: string, status: string, detail: string>
                  child 0, id: int64
                  child 1, severity: string
                  child 2, status: string
                  child 3, detail: string
          child 5, not_run: string
      child 5, artifacts: list<item: string>
          child 0, item: string
      child 6, advisories: int64
overall: string
soft_advisories: int64
poster_html: string
canvas: struct<source: string, width_cm: double, height_cm: double, orientation: string, source_url: null>
  child 0, source: string
  child 1, width_cm: double
  child 2, height_cm: double
  child 3, orientation: string
  child 4, source_url: null
hard_failures: int64
timestamp: timestamp[s]
warnings: int64
to
{'schema_version': Value('int64'), 'skill': Value('string'), 'timestamp': Value('timestamp[s]'), 'poster_html': Value('string'), 'canvas': {'source': Value('string'), 'width_cm': Value('float64'), 'height_cm': Value('float64'), 'orientation': Value('string'), 'source_url': Value('null')}, 'overall': Value('string'), 'hard_failures': Value('int64'), 'warnings': Value('int64'), 'soft_advisories': Value('int64'), 'gates': List({'name': Value('string'), 'severity': Value('string'), 'status': Value('string'), 'command': List(Value('string')), 'summary': {'exit_code': Value('int64'), 'tail': Value('string'), 'gate': Value('string'), 'status': Value('string'), 'rules': List({'id': Value('int64'), 'severity': Value('string'), 'status': Value('string'), 'detail': Value('string')}), 'not_run': Value('string')}, 'artifacts': List(Value('string')), 'advisories': Value('int64')})}
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 489, 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 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, 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 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, 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 343, 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 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              _comment: string
              direction: string
              poster: string
              hue_centers: struct<accent: int64, emph: int64>
                child 0, accent: int64
                child 1, emph: int64
              base_hex: struct<accent: string, accent_deep: string, accent_light: string, accent_soft: string, emph: string, (... 56 chars omitted)
                child 0, accent: string
                child 1, accent_deep: string
                child 2, accent_light: string
                child 3, accent_soft: string
                child 4, emph: string
                child 5, emph_soft: string
                child 6, emph_light: string
                child 7, bg_page: string
              semantics: struct<accent: string, emph: string>
                child 0, accent: string
                child 1, emph: string
              dark_ground: bool
              skill: string
              schema_version: int64
              gates: list<item: struct<name: string, severity: string, status: string, command: list<item: string>, summa (... 229 chars omitted)
                child 0, item: struct<name: string, severity: string, status: string, command: list<item: string>, summary: struct< (... 217 chars omitted)
                    child 0, name: string
                    child 1, severity: string
                    child 2, status: string
                    child 3, command: list<item: string>
                        child 0, item: string
                    child 4, summary: struct<exit_code: int64, tail: string, gate: string, status: string, rules: list<item: struct<id: in (... 73 chars omitted)
                        child 0, exit_code: int64
                        child 1, tail: string
                        child 2, gate: string
                        child 3, status: string
                        child 4, rules: list<item: struct<id: int64, severity: string, status: string, detail: string>>
                            child 0, item: struct<id: int64, severity: string, status: string, detail: string>
                                child 0, id: int64
                                child 1, severity: string
                                child 2, status: string
                                child 3, detail: string
                        child 5, not_run: string
                    child 5, artifacts: list<item: string>
                        child 0, item: string
                    child 6, advisories: int64
              overall: string
              soft_advisories: int64
              poster_html: string
              canvas: struct<source: string, width_cm: double, height_cm: double, orientation: string, source_url: null>
                child 0, source: string
                child 1, width_cm: double
                child 2, height_cm: double
                child 3, orientation: string
                child 4, source_url: null
              hard_failures: int64
              timestamp: timestamp[s]
              warnings: int64
              to
              {'schema_version': Value('int64'), 'skill': Value('string'), 'timestamp': Value('timestamp[s]'), 'poster_html': Value('string'), 'canvas': {'source': Value('string'), 'width_cm': Value('float64'), 'height_cm': Value('float64'), 'orientation': Value('string'), 'source_url': Value('null')}, 'overall': Value('string'), 'hard_failures': Value('int64'), 'warnings': Value('int64'), 'soft_advisories': Value('int64'), 'gates': List({'name': Value('string'), 'severity': Value('string'), 'status': Value('string'), 'command': List(Value('string')), 'summary': {'exit_code': Value('int64'), 'tail': Value('string'), 'gate': Value('string'), 'status': Value('string'), 'rules': List({'id': Value('int64'), 'severity': Value('string'), 'status': Value('string'), 'detail': Value('string')}), 'not_run': Value('string')}, 'artifacts': List(Value('string')), 'advisories': Value('int64')})}
              because column names don't match

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YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

Reproduction bundle — "Beyond First-order Asymptotics in Sequential Mean Testing"

ICML 2026, OpenReview HMyCBL2yMV, arXiv 2606.04520. Logbook: https://huggingface.co/spaces/JG1310/repro-beyond-first-order-asymptotics-in-sequential-mean-testing

Independent, from-scratch reproduction of both [THEOREM] claims. No official code release exists; everything here was written for this reproduction. CPU only — no GPU is used or needed.

Verdicts

Claim	Statement	Verdict
1	CLT for the stopping time with explicit variance `σ²_bd = σ²/KL_inf³` (Theorem 4.4)	VERIFIED
2	second-order asymptotic characterization; optimal expected stopping time as `α→0`	VERIFIED as written (claim text misattributes the optimality result to this paper — see logbook)

Layout

core/         KL_inf dual solver (Honda-Takemura), exact population constants, path simulator
scripts/      exp00 self-check, exp01 Thm 4.2, exp02 Thm 4.4, exp03 Prop 4.5, exp04 DSSAT real data
specs/        the per-experiment specifications the scripts were built against
diagnostics/  three post-hoc analyses (seconds; read results/*.json, run no simulation)
results/      *.json full result records + GATE_REPORT.txt + DRIVER_REPORT.json
logs/         stdout of each experiment run
figures/      Plotly HTML + the CSV raw data behind each logbook figure
data/         icml.xlsx -- the DSSAT yield pool, sha256 bce8f40b...92dc1 (see below)
DERIVATIONS.md   re-derivation of both theorems, each step paired with an executable check
BRIEF_WRITER.md  what counts as support vs refutation, fixed before the runs
gates.py         procedure-fidelity gates (schema/shape/range/provenance -- NOT claim verdicts)

Rerun

pip install numpy scipy pandas joblib openpyxl requests
JOB_CORES=8 ./run_all.sh          # exp00 -> exp04 in dependency order; ~31 min on 10 cores
python3 gates.py all --full --report
python3 diagnostics/diag_theory_threshold.py       # exit 0
python3 diagnostics/diag_anscombe_orientation.py   # exit 0
python3 diagnostics/diag_c1_3e_vacuous.py          # exit 0

Each script is seeded (SEED_BASE in its meta) and reproduces bitwise at a fixed JOB_CORES; exp00 asserts determinism across JOB_CORES=1 vs 2.

exp04 re-downloads icml.xlsx from the authors' repository and refuses to run unless the sha256, row count, columns and marginals all match. A copy is vendored in data/ because the paper cites only https://dssat.net, which hosts no yield table:

https://raw.githubusercontent.com/vikasdeep131/Beyond-First-order-Asymptotics-in-Sequential-Mean-Testing/main/icml.xlsx
15527663 bytes   sha256 bce8f40bb0669998b5ffa15c9f226c6bcad87cc3cdfaf4ffc2f54d6320692dc1

Known limitations, stated plainly

exp02 reports monotone_in_delta = False. This is an orientation bug in our check, not an Anscombe failure; diagnostics/diag_anscombe_orientation.py demonstrates it. Left as it ran.
p(n, δ=0.02) ≈ 0.26 never reaches the 0.05 target in DERIVATIONS.md C2.3; δ was not taken small enough at the fixed ε. Directionally satisfied, not a pass.
DERIVATIONS.md check C1.3(e) (the Taylor mean-value identity) is vacuous as implemented: exp01 assigns A_n the very slope that makes max_taylor_err cancel to zero, so its 0.0 is algebra rather than evidence. See diagnostics/diag_c1_3e_vacuous.py. Counted as unverified. Every other C1.x check is independent and did run.
Proposition 4.5 coverage converges toward nominal but does not reach it by α = 10⁻¹².
The upstream DSSAT configuration (crop, cultivar, site, weather, model version) is documented nowhere in the paper or the authors' repo. Recorded as meta.provenance_gap.

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