Datasets:

KineJIng
/

kinejing-open-source

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
stage: string
frozen_utc: timestamp[s]
files: struct<README.md: string, audit_training.py: string, common.py: string, config.json: string, data-in (... 314 chars omitted)
  child 0, README.md: string
  child 1, audit_training.py: string
  child 2, common.py: string
  child 3, config.json: string
  child 4, data-inventory.json: string
  child 5, data.py: string
  child 6, dependencies.json: string
  child 7, dynamics.py: string
  child 8, numpy_runtime.py: string
  child 9, parity-inputs.json: string
  child 10, prepare.py: string
  child 11, synthetic-tests.json: string
  child 12, test_horizon.py: string
  child 13, train-window-ids.json.gz: string
  child 14, train_horizon.py: string
  child 15, validation-window-ids.json.gz: string
sources: struct<reference_training_source: struct<path: string, sha256: string>, reference_training_protocol: (... 498 chars omitted)
  child 0, reference_training_source: struct<path: string, sha256: string>
      child 0, path: string
      child 1, sha256: string
  child 1, reference_training_protocol: struct<path: string, sha256: string>
      child 0, path: string
      child 1, sha256: string
  child 2, reference_runtime: struct<path: string, sha256: string>
      child 0, path: string
      child 1, sha256: string
  child 3, production_checkpoint: struct<path: string, sha256: string>
      child 0, path: string
      child 1, sha256: string
  child 4, data_manifest: struct<path: string, sha256: string>
      child 0, path: string
      child 1, sha256: string
  child 5, collection_protocol: struct<path: string, sha256: string>
      child 0, path: string
      child 1, sha256: string
  child 6, collection_source: struct<path: string, sha256: string>
      child 0, path: string
      child 1, sha256: string
  child 7, proposal: struct<path: string, sha256: string>
      child 0, path: string
      child 1, sha256: string
  child 8, training_reuse_inventory: struct<path: string, sha256: string>
      child 0, path: string
      child 1, sha256: string
  child 9, objective_v2_manifest: struct<path: string, sha256: string>
      child 0, path: string
      child 1, sha256: string
timing_started: bool
formal_started: bool
shared_registry_verified: bool
local_task: string
created_utc: timestamp[s]
experiment_status: string
artifact_type: string
schema_version: int64
to
{'schema_version': Value('int64'), 'artifact_type': Value('string'), 'created_utc': Value('timestamp[s]'), 'experiment_status': Value('string'), 'files': List({'path': Value('string'), 'bytes': Value('int64'), 'sha256': Value('string')})}
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
              stage: string
              frozen_utc: timestamp[s]
              files: struct<README.md: string, audit_training.py: string, common.py: string, config.json: string, data-in (... 314 chars omitted)
                child 0, README.md: string
                child 1, audit_training.py: string
                child 2, common.py: string
                child 3, config.json: string
                child 4, data-inventory.json: string
                child 5, data.py: string
                child 6, dependencies.json: string
                child 7, dynamics.py: string
                child 8, numpy_runtime.py: string
                child 9, parity-inputs.json: string
                child 10, prepare.py: string
                child 11, synthetic-tests.json: string
                child 12, test_horizon.py: string
                child 13, train-window-ids.json.gz: string
                child 14, train_horizon.py: string
                child 15, validation-window-ids.json.gz: string
              sources: struct<reference_training_source: struct<path: string, sha256: string>, reference_training_protocol: (... 498 chars omitted)
                child 0, reference_training_source: struct<path: string, sha256: string>
                    child 0, path: string
                    child 1, sha256: string
                child 1, reference_training_protocol: struct<path: string, sha256: string>
                    child 0, path: string
                    child 1, sha256: string
                child 2, reference_runtime: struct<path: string, sha256: string>
                    child 0, path: string
                    child 1, sha256: string
                child 3, production_checkpoint: struct<path: string, sha256: string>
                    child 0, path: string
                    child 1, sha256: string
                child 4, data_manifest: struct<path: string, sha256: string>
                    child 0, path: string
                    child 1, sha256: string
                child 5, collection_protocol: struct<path: string, sha256: string>
                    child 0, path: string
                    child 1, sha256: string
                child 6, collection_source: struct<path: string, sha256: string>
                    child 0, path: string
                    child 1, sha256: string
                child 7, proposal: struct<path: string, sha256: string>
                    child 0, path: string
                    child 1, sha256: string
                child 8, training_reuse_inventory: struct<path: string, sha256: string>
                    child 0, path: string
                    child 1, sha256: string
                child 9, objective_v2_manifest: struct<path: string, sha256: string>
                    child 0, path: string
                    child 1, sha256: string
              timing_started: bool
              formal_started: bool
              shared_registry_verified: bool
              local_task: string
              created_utc: timestamp[s]
              experiment_status: string
              artifact_type: string
              schema_version: int64
              to
              {'schema_version': Value('int64'), 'artifact_type': Value('string'), 'created_utc': Value('timestamp[s]'), 'experiment_status': Value('string'), 'files': List({'path': Value('string'), 'bytes': Value('int64'), 'sha256': Value('string')})}
              because column names don't match

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KineJing · 勘境 — Open technical source collection

Reviewed source snapshots, engineering extensions and scientific evidence. This is a bounded local-project release, not a claim that every file on a computer was inspected or that published prototypes have general model capabilities.

Company-owned code without an existing public licence uses Apache-2.0. Existing component licences, attribution, and third-party restrictions remain in LICENSES.md and component directories. Restricted extension packs remain clearly labelled.

KineJing: 动作条件表征、动力学组件与复现接口。仅保留源码及有明确范围的研究记录。
H-JEPA-KineJing: 默认关闭的规划与运行时工程扩展；原作者归属及验证限制保留。
kine-bench: 研究评测协议、任务接口与结果核验工具。
kine-datapipe: 时序数据与动作协议处理组件。
kine-jepa: 动作条件潜在动力学实现与测试。
kine-learn: 学习与适配研究组件及可检查测试。
kineedge: 轻量研究运行接口与工程工具。
KineJing-Serve: 推理适配与资产身份核验源码；不包含生产状态或未获再分发确认的权重。
kineone-wm-train: 世界模型训练研究源码；本次不发布未核实权利的外部权重。
pusht-world-model-research: 仿真预测与闭环控制研究代码及已记录结果，保留负面证据和适用范围。
spatial-0: 空间世界模型技术原型与诊断代码。
world-model-diagnostic: 世界模型行为诊断与可复核结果。
Destiny: 独立研究原型源码快照，不宣称已验证的通用能力。
kinejing-research: 公开研究复现入口、工程扩展与证据导览；媒体逐项保留来源。
kinejing-site: 公司网站技术源代码快照；生产与经营资料排除。
kineworld-site: 网站与研究展示工程快照，历史品牌保持来源语境。
kinejing-general: 状态、动作与预测表征研究源码及科学验证记录；未据发布行为新增性能结论。
kinejing-mini: 状态、动作与预测表征研究源码及科学验证记录；未据发布行为新增性能结论。
kinejing-pro-next: 状态、动作与预测表征研究源码及科学验证记录；未据发布行为新增性能结论。
kwm: 状态、动作与预测表征研究源码及科学验证记录；未据发布行为新增性能结论。
legacy-kineworld: 历史仿真、动力学和核验技术源码；公司资料、外部依赖、原始数据与权重不在此快照中。
kw-moon: 历史任务运行、仿真与结果核验技术快照；不新增模型能力声明。
official-site-source: 公司网站技术文件快照；只保留公开展示工程，不包含生产状态、账号、支付或运营材料。
sparse-world-dynamics: 稀疏动力学研究与初始化/少量优化诊断。现有证据没有证明有效的大规模模型学习能力；大型初始化矩阵与外部权重不随包再分发。
extension-vjepa2: 公司维护的可检查工程变更、替换文件与聚焦测试。保留上游来源、许可和限制，不附带原模型权重。
extension-dreamerv3: 公司维护的可检查工程变更、替换文件与聚焦测试。保留上游来源、许可和限制，不附带原模型权重。
extension-Wan2.2: 公司维护的可检查工程变更、替换文件与聚焦测试。保留上游来源、许可和限制，不附带原模型权重。
extension-jepa-wms: 公司维护的可检查工程变更、替换文件与聚焦测试。保留上游来源、许可和限制，不附带原模型权重。
extension-cosmos: 公司维护的可检查工程变更、替换文件与聚焦测试。保留上游来源、许可和限制，不附带原模型权重。
extension-OpenDW: 公司维护的可检查工程变更、替换文件与聚焦测试。保留上游来源、许可和限制，不附带原模型权重。
extension-KineJing-CausalWM: 公司维护的可检查工程变更、替换文件与聚焦测试。保留上游来源、许可和限制，不附带原模型权重。
KineJing-ZeroAdapt: 算法审计、负结果、精确正/负价值环境与小规模配对证据；不主张通用性能或原创性。
kinejing-platform: 核心数值推理引擎、研究源码、公开SDK及科学评估记录；商业应用、账户、账务和生产运营被分离排除。
model-v2: 从随机初始化训练的状态输入仿真动力学模型源码、审计通过的小型检查点及32个数值测试轨迹；保留离线预测范围和局限。
core-api: 历史有界数值推理API与合成测试源码；不代表当前部署服务，不包含账户、收费状态或外部权重。
jingjie-ai-worldmodel-0.5.1: 境阶学习应用的课程、复习、仿真实验与本地工作台技术模块。账号与支付入口已排除，本包不是完整可部署应用。

Disclosure and reproducibility

No raw conversations, personal files, credentials, production state, customer/contracts, financial/personnel/operating records, Git history or recovery archives are published. Some assets are deferred pending distribution provenance. Public text redacts local-home prefixes/contact emails outside required licences. Scientific numeric values are retained; original private experiments remain unchanged. Historical source-lock hashes may identify pre-disclosure bytes; public_manifest.json identifies the exported bytes.

The collection includes negative and limited results. Engineering adapters are not proof of trained-model performance. No new experiments, training or benchmark expansion were performed for publication.

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KineJing · 勘境 — Open technical source collection

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Disclosure and reproducibility