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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
</tool_call>: int64
<tool_call>: int64
<|box_end|>: int64
<|box_start|>: int64
<|endoftext|>: int64
<|file_sep|>: int64
<|fim_middle|>: int64
<|fim_pad|>: int64
<|fim_prefix|>: int64
<|fim_suffix|>: int64
<|im_end|>: int64
<|im_start|>: int64
<|image_pad|>: int64
<|object_ref_end|>: int64
<|object_ref_start|>: int64
<|quad_end|>: int64
<|quad_start|>: int64
<|repo_name|>: int64
<|video_pad|>: int64
<|vision_end|>: int64
<|vision_pad|>: int64
<|vision_start|>: int64
scope: string
manifest_hash: string
checkpoint_commit_hash: string
version: int64
format: string
files: list<item: struct<path: string, bytes: int64, sha256: string>>
  child 0, item: struct<path: string, bytes: int64, sha256: string>
      child 0, path: string
      child 1, bytes: int64
      child 2, sha256: string
total_bytes: int64
to
{'format': Value('string'), 'version': Value('int64'), 'checkpoint_commit_hash': Value('string'), 'files': List({'path': Value('string'), 'bytes': Value('int64'), 'sha256': Value('string')}), 'total_bytes': Value('int64'), 'scope': Value('string'), 'manifest_hash': Value('string')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, 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 127, 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 483, 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 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, 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 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              </tool_call>: int64
              <tool_call>: int64
              <|box_end|>: int64
              <|box_start|>: int64
              <|endoftext|>: int64
              <|file_sep|>: int64
              <|fim_middle|>: int64
              <|fim_pad|>: int64
              <|fim_prefix|>: int64
              <|fim_suffix|>: int64
              <|im_end|>: int64
              <|im_start|>: int64
              <|image_pad|>: int64
              <|object_ref_end|>: int64
              <|object_ref_start|>: int64
              <|quad_end|>: int64
              <|quad_start|>: int64
              <|repo_name|>: int64
              <|video_pad|>: int64
              <|vision_end|>: int64
              <|vision_pad|>: int64
              <|vision_start|>: int64
              scope: string
              manifest_hash: string
              checkpoint_commit_hash: string
              version: int64
              format: string
              files: list<item: struct<path: string, bytes: int64, sha256: string>>
                child 0, item: struct<path: string, bytes: int64, sha256: string>
                    child 0, path: string
                    child 1, bytes: int64
                    child 2, sha256: string
              total_bytes: int64
              to
              {'format': Value('string'), 'version': Value('int64'), 'checkpoint_commit_hash': Value('string'), 'files': List({'path': Value('string'), 'bytes': Value('int64'), 'sha256': Value('string')}), 'total_bytes': Value('int64'), 'scope': Value('string'), 'manifest_hash': Value('string')}
              because column names don't match

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SearchForge research artifacts

Versioned synthetic-world data, typed task programs, evidence audits, protected splits, and recovery/training states for the SearchForge symbolic task-model project. These artifacts support source-to-program-to-grounding reproduction.

The first snapshot is recovery and implementation evidence, not a successful solver-learning result. Historical findings remain: E3 PASS; E5a PASS; confirmatory E5 FAIL under its preregistered criterion; E6 INCONCLUSIVE. No symbolic SFT/static comparison is claimed until a corresponding run exists.

Snapshots and restoration

Each snapshots/<id>/ has a MANIFEST, gzip/tar parts and a COMPLETE marker published together in one Hub commit. Pin the commit SHA, validate each part's SHA-256, concatenate parts in manifest order, extract into a fresh directory, and validate all restored file sizes and SHA-256 values. COMPLETE means complete publication; a separate restoration receipt records actual state-loading checks.

Content includes a 28-type/84-relation synthetic world, 137-token IR, complete replay/grounding outcomes, protected splits, and bounded recovery-smoke states. Failures and costs are retained. Protected solver tests must not be used to train or tune generators. Original source and environment locks remain in the original GitHub repository; the manifest records its exact commit.

Upstream data: https://huggingface.co/datasets/hkuzxc/SearchGym-test-data, revision 5bf73cafe184610b40933ab2f42fb21c7926bd81.

Licensing

Third-party materials retain their upstream terms and provenance; this repository does not grant a blanket license for unrelated source repositories.

Built with Qwen. R4 derives from Qwen/Qwen2.5-3B revision 3aab1f1954e9cc14eb9509a215f9e5ca08227a9b. Its Qwen RESEARCH LICENSE AGREEMENT and required notice are included in licenses/. Those materials are for non-commercial research/evaluation under that agreement. The checkpoint was reserialized by AReaL after one recovery-only AdamW step, creating optimizer state. Model tensor hashes did not change because the group had zero utility. It is not an old M4 or a formal training anchor.

Only explicitly selected research files are included. Account credentials, other user projects and whole private source archives are excluded. Public Hub storage is best-effort, not a guarantee of unlimited capacity or availability.

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