Datasets:

cubed-core
/

cubed-data-v1

Error code:   DatasetGenerationError
Exception:    CastError
Message:      Couldn't cast
gt19: struct<detected: int64, reach_GL: bool, hit: null, deepest: null, firstdiv: string, gl_index: int64, (... 16 chars omitted)
  child 0, detected: int64
  child 1, reach_GL: bool
  child 2, hit: null
  child 3, deepest: null
  child 4, firstdiv: string
  child 5, gl_index: int64
  child 6, inputs: string
gt22: struct<detected: int64, reach_GL: bool, hit: int64, deepest: string, firstdiv: null, gl_index: int64 (... 17 chars omitted)
  child 0, detected: int64
  child 1, reach_GL: bool
  child 2, hit: int64
  child 3, deepest: string
  child 4, firstdiv: null
  child 5, gl_index: int64
  child 6, inputs: string
gtD2s: struct<detected: int64, reach_GL: bool, hit: null, deepest: null, firstdiv: string, gl_index: int64, (... 16 chars omitted)
  child 0, detected: int64
  child 1, reach_GL: bool
  child 2, hit: null
  child 3, deepest: null
  child 4, firstdiv: string
  child 5, gl_index: int64
  child 6, inputs: string
gt18: struct<detected: int64, reach_GL: bool, hit: null, deepest: null, firstdiv: string, gl_index: int64, (... 16 chars omitted)
  child 0, detected: int64
  child 1, reach_GL: bool
  child 2, hit: null
  child 3, deepest: null
  child 4, firstdiv: string
  child 5, gl_index: int64
  child 6, inputs: string
claim_tier: string
benchmark_id: string
limitations: list<item: string>
  child 0, item: string
schema_version: int64
capture_count: int64
split_integrity: struct<fixed: bool, group_policy: struct<camera_id: struct<mode: string, reason: string>, cube_id: s (
...
ngth: int64
teacher_isolation: struct<prediction_input_binding_verified: bool, prediction_lane: string, process_environment_audited (... 106 chars omitted)
  child 0, prediction_input_binding_verified: bool
  child 1, prediction_lane: string
  child 2, process_environment_audited: bool
  child 3, scope: string
  child 4, teacher_access_declaration: string
  child 5, teacher_root_separate_from_public_bundle: bool
contract_valid: bool
aggregate: struct<diagnostics: struct<abstention_count: int64, abstention_rate: double, edit_distance_mean: dou (... 295 chars omitted)
  child 0, diagnostics: struct<abstention_count: int64, abstention_rate: double, edit_distance_mean: double, predicted_lengt (... 94 chars omitted)
      child 0, abstention_count: int64
      child 1, abstention_rate: double
      child 2, edit_distance_mean: double
      child 3, predicted_length_mean: double
      child 4, runtime_ms_mean: double
      child 5, runtime_ms_total: int64
      child 6, teacher_length_mean: double
  child 1, primary: struct<definition: string, metric: string, rate: double, successes: int64, total: int64>
      child 0, definition: string
      child 1, metric: string
      child 2, rate: double
      child 3, successes: int64
      child 4, total: int64
  child 2, secondary: struct<metric: string, rate: double, successes: int64, total: int64>
      child 0, metric: string
      child 1, rate: double
      child 2, successes: int64
      child 3, total: int64
manifest_sha256: string
to
{'aggregate': {'diagnostics': {'abstention_count': Value('int64'), 'abstention_rate': Value('float64'), 'edit_distance_mean': Value('float64'), 'predicted_length_mean': Value('float64'), 'runtime_ms_mean': Value('float64'), 'runtime_ms_total': Value('int64'), 'teacher_length_mean': Value('float64')}, 'primary': {'definition': Value('string'), 'metric': Value('string'), 'rate': Value('float64'), 'successes': Value('int64'), 'total': Value('int64')}, 'secondary': {'metric': Value('string'), 'rate': Value('float64'), 'successes': Value('int64'), 'total': Value('int64')}}, 'benchmark_id': Value('string'), 'benchmark_version': Value('string'), 'capture_count': Value('int64'), 'captures': List({'abstention_reason': Value('string'), 'capture_id': Value('string'), 'deepest_phase_reached': Value('string'), 'edit_distance': Value('int64'), 'first_unreached_phase': Value('string'), 'predicted_length': Value('int64'), 'reach_ll': Value('bool'), 'reach_ll_hit_step': Value('int64'), 'runtime_ms': Value('int64'), 'solved_endpoint_completed': Value('bool'), 'status': Value('string'), 'teacher_length': Value('int64')}), 'claim_tier': Value('string'), 'component_diagnostics': {'metrics': List(Value('null')), 'scope': Value('string')}, 'contract_valid': Value('bool'), 'limitations': List(Value('string')), 'manifest_sha256': Value('string'), 'schema': Value('string'), 'schema_version': Value('int64'), 'split': Value('string'), 'split_integrity': {'fixed': Value('bool'), 'group_policy': {'camera_id': {'mode': Value('string'), 'reason': Value('string')}, 'cube_id': {'mode': Value('string'), 'reason': Value('string')}, 'session_id': {'mode': Value('string'), 'reason': Value('string')}, 'setup_id': {'mode': Value('string'), 'reason': Value('string')}, 'solver_id': {'mode': Value('string'), 'reason': Value('string')}}, 'observed_cross_split_groups': {'camera_id': List({'capture_count': Value('int64'), 'splits': List(Value('string')), 'value': Value('string')}), 'cube_id': List({'capture_count': Value('int64'), 'splits': List(Value('string')), 'value': Value('string')}), 'session_id': List({'capture_count': Value('int64'), 'splits': List(Value('string')), 'value': Value('string')}), 'setup_id': List({'capture_count': Value('int64'), 'splits': List(Value('string')), 'value': Value('string')}), 'solver_id': List({'capture_count': Value('int64'), 'splits': List(Value('string')), 'value': Value('string')})}, 'unit': Value('string')}, 'system': {'config_sha256': Value('string'), 'implementation_sha256': Value('string'), 'model_artifacts': List({'name': Value('string'), 'sha256': Value('string')}), 'name': Value('string'), 'run_receipt_sha256': Value('string'), 'version': Value('string')}, 'teacher_isolation': {'prediction_input_binding_verified': Value('bool'), 'prediction_lane': Value('string'), 'process_environment_audited': Value('bool'), 'scope': Value('string'), 'teacher_access_declaration': Value('string'), 'teacher_root_separate_from_public_bundle': Value('bool')}}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1816, in _prepare_split_single
                  for key, table in generator:
                                    ^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
                  for item in generator(*args, **kwargs):
                              ~~~~~~~~~^^^^^^^^^^^^^^^^^
                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
              gt19: struct<detected: int64, reach_GL: bool, hit: null, deepest: null, firstdiv: string, gl_index: int64, (... 16 chars omitted)
                child 0, detected: int64
                child 1, reach_GL: bool
                child 2, hit: null
                child 3, deepest: null
                child 4, firstdiv: string
                child 5, gl_index: int64
                child 6, inputs: string
              gt22: struct<detected: int64, reach_GL: bool, hit: int64, deepest: string, firstdiv: null, gl_index: int64 (... 17 chars omitted)
                child 0, detected: int64
                child 1, reach_GL: bool
                child 2, hit: int64
                child 3, deepest: string
                child 4, firstdiv: null
                child 5, gl_index: int64
                child 6, inputs: string
              gtD2s: struct<detected: int64, reach_GL: bool, hit: null, deepest: null, firstdiv: string, gl_index: int64, (... 16 chars omitted)
                child 0, detected: int64
                child 1, reach_GL: bool
                child 2, hit: null
                child 3, deepest: null
                child 4, firstdiv: string
                child 5, gl_index: int64
                child 6, inputs: string
              gt18: struct<detected: int64, reach_GL: bool, hit: null, deepest: null, firstdiv: string, gl_index: int64, (... 16 chars omitted)
                child 0, detected: int64
                child 1, reach_GL: bool
                child 2, hit: null
                child 3, deepest: null
                child 4, firstdiv: string
                child 5, gl_index: int64
                child 6, inputs: string
              claim_tier: string
              benchmark_id: string
              limitations: list<item: string>
                child 0, item: string
              schema_version: int64
              capture_count: int64
              split_integrity: struct<fixed: bool, group_policy: struct<camera_id: struct<mode: string, reason: string>, cube_id: s (
              ...
              ngth: int64
              teacher_isolation: struct<prediction_input_binding_verified: bool, prediction_lane: string, process_environment_audited (... 106 chars omitted)
                child 0, prediction_input_binding_verified: bool
                child 1, prediction_lane: string
                child 2, process_environment_audited: bool
                child 3, scope: string
                child 4, teacher_access_declaration: string
                child 5, teacher_root_separate_from_public_bundle: bool
              contract_valid: bool
              aggregate: struct<diagnostics: struct<abstention_count: int64, abstention_rate: double, edit_distance_mean: dou (... 295 chars omitted)
                child 0, diagnostics: struct<abstention_count: int64, abstention_rate: double, edit_distance_mean: double, predicted_lengt (... 94 chars omitted)
                    child 0, abstention_count: int64
                    child 1, abstention_rate: double
                    child 2, edit_distance_mean: double
                    child 3, predicted_length_mean: double
                    child 4, runtime_ms_mean: double
                    child 5, runtime_ms_total: int64
                    child 6, teacher_length_mean: double
                child 1, primary: struct<definition: string, metric: string, rate: double, successes: int64, total: int64>
                    child 0, definition: string
                    child 1, metric: string
                    child 2, rate: double
                    child 3, successes: int64
                    child 4, total: int64
                child 2, secondary: struct<metric: string, rate: double, successes: int64, total: int64>
                    child 0, metric: string
                    child 1, rate: double
                    child 2, successes: int64
                    child 3, total: int64
              manifest_sha256: string
              to
              {'aggregate': {'diagnostics': {'abstention_count': Value('int64'), 'abstention_rate': Value('float64'), 'edit_distance_mean': Value('float64'), 'predicted_length_mean': Value('float64'), 'runtime_ms_mean': Value('float64'), 'runtime_ms_total': Value('int64'), 'teacher_length_mean': Value('float64')}, 'primary': {'definition': Value('string'), 'metric': Value('string'), 'rate': Value('float64'), 'successes': Value('int64'), 'total': Value('int64')}, 'secondary': {'metric': Value('string'), 'rate': Value('float64'), 'successes': Value('int64'), 'total': Value('int64')}}, 'benchmark_id': Value('string'), 'benchmark_version': Value('string'), 'capture_count': Value('int64'), 'captures': List({'abstention_reason': Value('string'), 'capture_id': Value('string'), 'deepest_phase_reached': Value('string'), 'edit_distance': Value('int64'), 'first_unreached_phase': Value('string'), 'predicted_length': Value('int64'), 'reach_ll': Value('bool'), 'reach_ll_hit_step': Value('int64'), 'runtime_ms': Value('int64'), 'solved_endpoint_completed': Value('bool'), 'status': Value('string'), 'teacher_length': Value('int64')}), 'claim_tier': Value('string'), 'component_diagnostics': {'metrics': List(Value('null')), 'scope': Value('string')}, 'contract_valid': Value('bool'), 'limitations': List(Value('string')), 'manifest_sha256': Value('string'), 'schema': Value('string'), 'schema_version': Value('int64'), 'split': Value('string'), 'split_integrity': {'fixed': Value('bool'), 'group_policy': {'camera_id': {'mode': Value('string'), 'reason': Value('string')}, 'cube_id': {'mode': Value('string'), 'reason': Value('string')}, 'session_id': {'mode': Value('string'), 'reason': Value('string')}, 'setup_id': {'mode': Value('string'), 'reason': Value('string')}, 'solver_id': {'mode': Value('string'), 'reason': Value('string')}}, 'observed_cross_split_groups': {'camera_id': List({'capture_count': Value('int64'), 'splits': List(Value('string')), 'value': Value('string')}), 'cube_id': List({'capture_count': Value('int64'), 'splits': List(Value('string')), 'value': Value('string')}), 'session_id': List({'capture_count': Value('int64'), 'splits': List(Value('string')), 'value': Value('string')}), 'setup_id': List({'capture_count': Value('int64'), 'splits': List(Value('string')), 'value': Value('string')}), 'solver_id': List({'capture_count': Value('int64'), 'splits': List(Value('string')), 'value': Value('string')})}, 'unit': Value('string')}, 'system': {'config_sha256': Value('string'), 'implementation_sha256': Value('string'), 'model_artifacts': List({'name': Value('string'), 'sha256': Value('string')}), 'name': Value('string'), 'run_receipt_sha256': Value('string'), 'version': Value('string')}, 'teacher_isolation': {'prediction_input_binding_verified': Value('bool'), 'prediction_lane': Value('string'), 'process_environment_audited': Value('bool'), 'scope': Value('string'), 'teacher_access_declaration': Value('string'), 'teacher_root_separate_from_public_bundle': Value('bool')}}
              because column names don't match
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1869, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

aggregate dict	benchmark_id string	benchmark_version string	capture_count int64	captures list	claim_tier string	component_diagnostics dict	contract_valid bool	limitations list	manifest_sha256 string	schema string	schema_version int64	split string	split_integrity dict	system dict	teacher_isolation dict
{ "diagnostics": { "abstention_count": 13, "abstention_rate": 0.9285714285714286, "edit_distance_mean": 78.71428571428571, "predicted_length_mean": 5.5, "runtime_ms_mean": 180428.57142857142, "runtime_ms_total": 2526000, "teacher_length_mean": 83.5 }, "primary": { "definition": "re...	cubed-core-benchmark-v0	0	14	[ { "abstention_reason": "decoder declined to emit a sequence", "capture_id": "0565964fe0024ac0a5ad53cd3468940b", "deepest_phase_reached": "scramble-start", "edit_distance": 85, "first_unreached_phase": "last-layer-onset", "predicted_length": 0, "reach_ll": false, "reach_ll_hit_step": ...	interface-boundary-only	{ "metrics": [], "scope": "component diagnostics describe tracker/read subproblems and do not establish camera-to-state reconstruction" }	true	[ "This is a development benchmark of complete-solve captures only, not a general dataset and not evidence of generalization.", "All captures come from one solver, one GAN 12 ui smart cube, and one camera setup family.", "Every capture is bound to the single reviewed gtD1 six-color calibration; that calibration w...	b3e53597678d2c71bc6b787dbb780f4c50018b8336a1d4f0b794e98097c8bbd8	cubed-core/benchmark-v0-report	1	train	{ "fixed": true, "group_policy": { "camera_id": { "mode": "disclosed-overlap", "reason": "One solver, one physical cube, and one camera setup family produced the whole corpus, so this dimension necessarily crosses splits. Disclosed rather than hidden: results are within-solver, within-cube, within-s...	{ "config_sha256": "86fa6c722804f88ce9486b0276032ff0e939af01f6acaba8d4fff1d618f98f5e", "implementation_sha256": "22124598154864394d0da5cd7a74b9b97e89097ee84596525d48fb2c81026c23", "model_artifacts": [ { "name": "alignment-classifier.onnx", "sha256": "d715b66ba3d7b500bf841f22f2ae29273baae03880d473e...	{ "prediction_input_binding_verified": true, "prediction_lane": "camera-only", "process_environment_audited": false, "scope": "The evaluator verifies prediction documents, exact input hashes, and the separation of public inputs from the teacher root. This establishes an interface boundary only; it does not audi...

Cubed Solve Captures (`cubed-data-v1`)

35 self-recorded captures of a GAN 12 ui Bluetooth smart cube being solved: portrait 1080×1920 H.264 video at ~120 fps with no audio (3.15 GB total), paired with the cube's own BLE move log, phone IMU samples, and a replay-verified scramble record. The corpus exists for camera-only decode research — reconstructing a solve from ordinary phone video — and is the input set for Cubed Core's Benchmark v0. It is a corpus, not a benchmark: no train/validation/test split is assigned here.

Suites

suite	captures	what it is
`cs`	25	Complete solves. Full BLE + IMU. Video covers the whole solve on 24 of 25.
`gt`	6	Research captures (gt17–gt22). Video ends before the solve's final move on five of six. Video + BLE + scramble record; no IMU.
`gtD`	4	On-camera scramble from a verified solved start (gtD2–gtD5). The scramble itself is filmed. Full BLE + IMU.

All 35 BLE move logs replay bit-exact to solved on two independent cube engines: starting state, then the recorded move sequence, ends on the solved state. The per-move facelets strings embedded in a log are the cube's own periodic state broadcast and lag the move stream, so they are not the replay source of truth — use the scramble/start state plus the move tokens.

Layout and fields

One directory per capture under captures/<capture_id>/.

file	captures	contents
`video.mp4`	35	Stream-copied from source: bitstream untouched, audio removed, container metadata stripped.
`scramble.json`	35	Scramble in standard notation, derived start state, replay-verification and truncation status.
`rights_record.json`	35	Per-artifact rights binding by byte count and SHA-256.
`cube_session.json`	35	BLE move log: moves, facelet states, quaternions. `moves[].t_ms` and `orientations[].t_ms` are present as integer-millisecond deltas from session start; wall-clock time is still removed.
`imu.json`	29	Phone IMU orientation samples.
`app_cube_session.json`	15	App-side BLE log, verified move-identical to `cube_session.json`.
`capture_manifest.json`	14	Capture-app manifest.
`clip_ble_ground_truth.json`	1	Frame-indexed BLE ground truth (gtD1 only).

dataset/manifest.json is the authoritative inventory; SHA256SUMS lists a digest for every published file. Every JSON validates against a schema in the schemas/ directory of the cubed-core repository.

As of this release, cube_session.json adds relative timestamps. moves[].t_ms and orientations[].t_ms are integer-millisecond deltas from the start of the session. This makes frame registration (aligning the move log to the video) computable from public data alone, for every capture that ships a BLE session. Wall-clock date, time of day, and every private ingest handle stay out of the corpus, unchanged from the prior release. The relative timestamps follow the same disclosure the corpus already made for gtD1: clip_ble_ground_truth.json has shipped a frame-indexed BLE record with its own scrub block since the first release, and this update brings the other captures' BLE sessions to the same standard.

No calibration or camera-metadata artifact ships per capture. The corpus was recorded with one cube and one camera setup family, and decode uses the single shared six-colour calibration (calibration_gan12.json) distributed as a cubed-core release asset. That calibration was measured under one lighting condition while the corpus spans three recording cohorts — treat colour evidence away from the gtD1 session accordingly.

Benchmark v0 results

Measured on this corpus; per-row tables, per-capture narratives and hash-bound receipts are under benchmark/.

suite	metric	result
gt suite	reach-LL	5 / 6
gtD1s	reach-solved	1 / 1
cs suite	reach-LL	4 / 24

gt19 is reported informationally and excluded from the tallies: it was used to train an upstream verifier, so any result on it is contaminated.

Reach-LL asks whether the raw predicted state path reaches last-layer onset with the correct pre-last-layer state, up to whole-cube orientation. These figures come from the evaluation lane, which supplies a ground-truth terminal; the productized camera-only path is a separate lane with its own contract and is not tabulated.

Intended use

This corpus is for camera-only cube-decode research: reconstructing a solve from ordinary phone video, with the cube's own BLE move log available as ground truth. It is also the input set for Cubed Core's Benchmark v0. It is a corpus, not a benchmark — no train/validation/test split is assigned here, and results measured on it are within-solver, within-setup findings.

Decoding requires a CUDA host. The pipeline, the runner and the authoritative configuration live in cubed-core — see docs/tutorials/DECODE.md for the workflow and config/decode-runtime-v1.json for the profiles of record: local_camera_v1 (camera-only, CFG_HASH 1852738634) and canonical_eval_reference (evaluation lane, CFG_HASH 2587091255).

from huggingface_hub import snapshot_download
snapshot_download("cubed-core/cubed-data-v1", repo_type="dataset")

Licenses and attribution

Released under CC-BY-SA-4.0. Attribution: Manas (https://github.com/KingBobJoeIV). Every capture carries a rights_record.json binding each published artifact by byte count and SHA-256, with its own licence and attribution; those per-artifact licences are authoritative. The collection licence in LICENSES/DATASET.md covers selection and arrangement only.

Privacy

All captures were self-recorded by the dataset author solving a GAN 12 ui smart cube filmed with an iPhone, using the project's own capture tooling. No other person appears in or contributed to the recordings. The videos have no audio track and no container metadata: hands and the cube on a desk are the whole frame. The published sidecars carry move tokens, facelet states, quaternions, scrambles and frame indices — no absolute wall-clock times, filesystem paths, device serial numbers, hostnames or account identifiers.

Limitations

One solver, one cube, one camera setup family. These captures cannot establish generalization across users, cameras, cubes, grips or lighting.
Video ends before the recorded solve's final move on six captures (five of gt17–gt22, plus gtD4); each capture's scramble.json records its own truncation status. The BLE log is complete regardless.
No per-capture colour calibration ships; decode uses one shared calibration measured under a single lighting condition across a corpus spanning three recording cohorts.
The Benchmark v0 figures above come from the evaluation lane, which supplies a ground-truth terminal. They are not camera-only results.

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Total file size:

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Cubed Solve Captures (cubed-data-v1)