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Error code: DatasetGenerationError
Exception: TypeError
Message: Couldn't cast array of type
struct<name: string, x: int64, y: int64>
to
{'name': Value('string')}
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1827, 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 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2312, in cast_table_to_schema
cast_array_to_feature(
~~~~~~~~~~~~~~~~~~~~~^
table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
feature,
^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1861, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
~~~~^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2158, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
TypeError: Couldn't cast array of type
struct<name: string, x: int64, y: int64>
to
{'name': Value('string')}
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 1694, 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 1880, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
model string | game string | i int64 | ts float64 | action dict | reset bool | full_reset bool | prev_level int64 | level int64 | level_up bool | state string | win bool | game_over bool | win_levels int64 | available_actions list | n_anim_frames int64 | grid list | prev_grid_changed_cells int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
release-gpt | ar25 | 0 | 1,787,955,946.114 | {
"name": "RESET"
} | true | true | 0 | 0 | false | NOT_FINISHED | false | false | 8 | [
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... | null |
release-gpt | ar25 | 1 | 1,787,956,041.517 | {
"name": "ACTION1"
} | false | false | 0 | 0 | false | NOT_FINISHED | false | false | 8 | [
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... | 109 |
release-gpt | ar25 | 2 | 1,787,956,135.713 | {
"name": "ACTION2"
} | false | false | 0 | 0 | false | NOT_FINISHED | false | false | 8 | [
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... | 109 |
release-gpt | ar25 | 3 | 1,787,956,209.208 | {
"name": "ACTION3"
} | false | false | 0 | 0 | false | NOT_FINISHED | false | false | 8 | [
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... | 109 |
release-gpt | ar25 | 4 | 1,787,956,217.249 | {
"name": "ACTION2"
} | false | false | 0 | 0 | false | NOT_FINISHED | false | false | 8 | [
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... | 109 |
release-gpt | ar25 | 5 | 1,787,956,217.251 | {
"name": "ACTION2"
} | false | false | 0 | 0 | false | NOT_FINISHED | false | false | 8 | [
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... | 109 |
release-gpt | ar25 | 6 | 1,787,956,217.253 | {
"name": "ACTION2"
} | false | false | 0 | 0 | false | NOT_FINISHED | false | false | 8 | [
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... | 109 |
release-gpt | ar25 | 7 | 1,787,956,217.255 | {
"name": "ACTION2"
} | false | false | 0 | 0 | false | NOT_FINISHED | false | false | 8 | [
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... | 109 |
release-gpt | ar25 | 8 | 1,787,956,217.257 | {
"name": "ACTION2"
} | false | false | 0 | 0 | false | NOT_FINISHED | false | false | 8 | [
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... | 109 |
release-gpt | ar25 | 9 | 1,787,956,217.259 | {
"name": "ACTION2"
} | false | false | 0 | 0 | false | NOT_FINISHED | false | false | 8 | [
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... | 109 |
release-gpt | ar25 | 10 | 1,787,956,217.261 | {
"name": "ACTION2"
} | false | false | 0 | 0 | false | NOT_FINISHED | false | false | 8 | [
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... | 109 |
release-gpt | ar25 | 11 | 1,787,956,217.262 | {
"name": "ACTION2"
} | false | false | 0 | 0 | false | NOT_FINISHED | false | false | 8 | [
1,
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... | 109 |
release-gpt | ar25 | 12 | 1,787,956,217.264 | {
"name": "ACTION2"
} | false | false | 0 | 0 | false | NOT_FINISHED | false | false | 8 | [
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... | 109 |
release-gpt | ar25 | 13 | 1,787,956,217.266 | {
"name": "ACTION2"
} | false | false | 0 | 0 | false | NOT_FINISHED | false | false | 8 | [
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... | 109 |
release-gpt | ar25 | 14 | 1,787,956,221.269 | {
"name": "ACTION3"
} | false | false | 0 | 0 | false | NOT_FINISHED | false | false | 8 | [
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... | 109 |
release-gpt | ar25 | 15 | 1,787,956,241.273 | {
"name": "ACTION3"
} | false | false | 0 | 0 | false | NOT_FINISHED | false | false | 8 | [
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... | 108 |
release-gpt | ar25 | 16 | 1,787,956,299.299 | {
"name": "ACTION3"
} | false | false | 0 | 0 | false | NOT_FINISHED | false | false | 8 | [
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... | 108 |
release-gpt | ar25 | 17 | 1,787,956,299.302 | {
"name": "ACTION3"
} | false | false | 0 | 1 | true | NOT_FINISHED | false | false | 8 | [
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... | 710 |
release-gpt | ar25 | 18 | 1,787,956,379.202 | {
"name": "ACTION4"
} | false | false | 1 | 1 | false | NOT_FINISHED | false | false | 8 | [
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... | 487 |
release-gpt | ar25 | 19 | 1,787,956,567.862 | {
"name": "ACTION5"
} | false | false | 1 | 1 | false | NOT_FINISHED | false | false | 8 | [
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... | 30 |
release-gpt | ar25 | 20 | 1,787,956,753.508 | {
"name": "ACTION2"
} | false | false | 1 | 1 | false | NOT_FINISHED | false | false | 8 | [
1,
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... | 181 |
release-gpt | ar25 | 21 | 1,787,956,753.51 | {
"name": "ACTION2"
} | false | false | 1 | 1 | false | NOT_FINISHED | false | false | 8 | [
1,
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... | 181 |
Kepler 1.0 ARC-AGI-3 trace corpus
Run artifacts from Kepler 1.0, an open-source agent harness for the 25 public
ARC-AGI-3 games. A stock CLI coding agent
encodes its theory of each game as an executable world_model.py, certifies it
against the full recorded interaction history, plans inside the certified
model, and acts through a guarded channel that voids the plan on the first
misprediction.
Project page · Code · Paper · Integrity record
The canonical release contains two single-configuration boards:
- Claude Opus 5: 100.00. One frozen configuration, one run per game, no score-conditioned reruns. ARC Prize's official server replay re-executed all 25 games to 100. Scorecard: 91aa2f10.
- GPT-5.6 Sol (max): 95.97. One frozen configuration, with the two non-perfect games retained. Scorecard: c9f087f3.
Across these two frozen release boards, 48 of 50 game-model cells reached 100. On the Opus board, 181 of 183 completed levels used no more actions than the corresponding median-human baseline. This is final-attempt action efficiency, not learning cost or evidence of human-like cognition.
Token and cost accounting was recovered from provider-side session records retained locally on the execution host and deduplicated by provider message ID. Those provider records are not part of this dataset; only the captured CLI session logs are. They put the Opus campaign at 858,041,926 raw tokens, 97.37% cache reads, and $777.72 at current API list-equivalent rates. That is 74.0% below the $2,986 API-equivalent estimate Retrodict published for Tycho. Tycho discloses no cost of its own, and neither do AVO or VISTA, so this is a comparison against one third-party estimate and not a ranking of the field. Retrodict's lower-scoring 99.86 costs less at $654. The GPT board is $1,312.14 current API list-equivalent.
The retained Opus board runs used 8,256 environment actions, with 7,292 in the original local scored-level results and 7,202 on ARC's public replay card. The GPT equivalents are 8,400 and 8,220. Replay selects the last full-reset-to-end ledger segment and counts a new opening reset, so these are different recorded denominators rather than score disagreements. Full campaign logs contain at least 13,688 non-reset actions and omit 22 prefix events, so 8,256 is not labeled learning-inclusive. None of these counts is ranked against other systems, whose action counts cover different stages and definitions.
Public-set scores are not a measure of AGI progress. The ARC-AGI-3 technical report (§4.3.1) says so explicitly and ships a human-replay harness scoring 100% to make the point. This dataset supports a reproduction question: does a published result hold up? It does not support a general capability claim.
The ablation is retracted. Read this before citing anything about the
harness's contribution. We ran the same model with the methodology stripped
out, but the control workspaces lived inside the repository, so every one of the
six baseline agents found harness/ws_tools/ on disk and rebuilt the
methodology they were meant to be a control for (9,148 tool invocations on the
worst one). That measured harness against harness. Every conclusion drawn from
it is withdrawn, including a "net advantage is roughly zero" headline we had
published. When historical roots are selected, those runs appear under
baseline-* labels so the contamination is inspectable. The harness's contribution is currently
unmeasured, not small and not large.
The source repository separately preserves a source-reading incident that produced a natural-looking 100 before scoring 46.91 in a clean rerun, plus evidence that a bundled planner was broken across five experimental boards while agents silently routed around it. High outcome scores therefore do not establish tool health.
This dataset is narrower than the full development archive. It contains exactly the two final 25-game boards. Earlier stages, failed experiments, quarantined incidents, and superseded runs remain documented in the source repository and are not part of this 50-run export.
Layout
runs.jsonl, one row per (run_root, model, game) run.events/<label>/<game>.jsonl.gz, one row per recorded environment transition of that run, in order. This is the append-only ground-truth ledger written by the harness daemon; the agent could read but never write it.agent_logs/<label>/<game>/sessions/*.log.gz, the harness's captured CLI output: 95 gzipped session logs, the only agent behavioural records in this dataset. Across this export, 50 of 50 runs have at least one retained log, but coverage depth is uneven. Some Claude tee files contain only final summaries or quota markers. These are the captured CLI records available on the execution host, not a platform attestation that every event was retained.arc_agi_3_human_baseline_actions.csv, the per-level human action baselines used to recompute RHAE.score_trajectories.py,verify_scores.py, andaudit_integrity.py, dependency-free verification programs copied into the dataset.
Labels
| label prefix | meaning |
|---|---|
release-opus |
canonical Claude Opus 5 board: 100.00, exact official replay |
release-gpt |
canonical GPT-5.6 Sol board: 95.97, exact official replay |
Internal run-root names are provenance identifiers, not public release versions. The public software and paper have one identity: Kepler 1.0.
runs.jsonl schema
| field | type | description |
|---|---|---|
model |
str | exported board label, such as release-opus or release-gpt |
game |
str | 4-char public ARC-AGI-3 game id (e.g. ft09) |
game_id |
str | full versioned environment id |
state |
str | terminal state of the run (WIN, NOT_FINISHED, ...) |
levels_completed / win_levels |
int | levels cleared / levels needed to win |
actions |
int | real actions committed to the environment |
elapsed_hours |
float | wall-clock duration of the run |
score |
float | official per-game RHAE score (0..100) |
level_scores / level_actions |
list | per-level score / action count |
note |
str | how the run ended (win, budget exhaustion reason, ...) |
notes_md |
str | the agent's final lab notebook, verbatim |
world_model_py |
str | the agent's final executable world model, verbatim |
scorecard |
json str | the official local arc_agi scorecard for the run |
n_events |
int | number of rows in the matching events file |
events_file |
str | relative path to the matching events file |
grids_compacted |
int/null | --compact N used at export time, if any |
run_root |
str | internal source directory retained as provenance, not a public version |
base_model |
str | model id without the label prefix |
complete |
bool | false for aborted runs (score is null) |
n_log_files / log_bytes_gz |
int | archived agent session files and their gzipped size |
events/*/*.jsonl.gz schema
One JSON object per line, fields as recorded live by the harness daemon:
| field | type | description |
|---|---|---|
model, game |
str | added at export time (join keys to runs.jsonl) |
i |
int | event index, 0-based, contiguous (append-only ledger) |
ts |
float | unix timestamp |
action |
obj | {"name": "RESET"|"ACTION1".."ACTION7", ["x","y" for ACTION6]} |
reset |
bool | this event is a reset, not a scored action |
full_reset |
bool | reset restarted the whole game (vs the current level) |
prev_level / level |
int | levels completed before / after the event |
level_up |
bool | this action completed a level |
state |
str | NOT_FINISHED / WIN / GAME_OVER ... |
win / game_over |
bool | terminal flags |
win_levels |
int | levels needed to win the game |
available_actions |
list[int] | action ids the env accepts here |
n_anim_frames |
int | animation frames the env returned (last one is grid) |
grid |
64x64 list[int] or null | the observed frame after the action (null only when exported with --compact) |
grid_stripped |
bool | present+true when --compact removed this row's grid |
prev_grid_changed_cells |
int/null | cells changed vs the previous grid |
Verify the export
From the downloaded dataset directory:
python3 score_trajectories.py .
python3 verify_scores.py --traces-dir .
python3 audit_integrity.py --traces-dir .
score_trajectories.py independently recomputes both board scores from the
event ledgers and the human baselines. verify_scores.py checks that no level
was scored with fewer actions than its ledger records. audit_integrity.py
scans the captured CLI session-log bytes for known leakage signatures.
A clean result applies to the records present here; it does not prove that the
client preserved every event or that the pattern set detects every possible
violation. An export without agent_logs/ is reported as unauditable and exits
nonzero.
Provenance & license
Produced by scripts/export_traces.py in the harness repository from the raw
run workspaces. Environments are the 25 public ARC-AGI-3 games run locally via
the arc-agi toolkit. MIT.
Generated: 2026-09-03. 50 runs, 58098 events, 95 session logs, labels: release-gpt, release-opus.
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