Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
model: string
benchmark: string
thinking: bool
runs: int64
tasks_per_run: int64
run_correct: struct<1: string, 2: string>
child 0, 1: string
child 1, 2: string
mean_accuracy: double
combined: string
rerun: struct<date: timestamp[s], tasks: list<item: string>, reasons: struct<calendar_5: string, calendar_7 (... 466 chars omitted)
child 0, date: timestamp[s]
child 1, tasks: list<item: string>
child 0, item: string
child 2, reasons: struct<calendar_5: string, calendar_7: string, calendar_8: string, calendar_11: string, calendar_12: (... 373 chars omitted)
child 0, calendar_5: string
child 1, calendar_7: string
child 2, calendar_8: string
child 3, calendar_11: string
child 4, calendar_12: string
child 5, calendar_14: string
child 6, cantook_8: string
child 7, clock_20: string
child 8, pimusic_7: string
child 9, pimusic_11: string
child 10, contacts_2: string
child 11, contacts_5: string
child 12, contacts_6: string
child 13, zoom_2: string
child 14, contacts_10: string
child 15, pimusic_2: string
child 16, pimusic_5: string
child 17, setting_15: string
child 18, setting_18: string
child 19, setting_6: string
child 20, calendar_9: string
child 21, cantook_9: string
child 22, clock_4: string
child 23, clock_19: string
child 3, passed: int64
child 4, note: string
time: double
endpoint: string
sampling: struct<temperature:
...
g: bool, (... 53 chars omitted)
child 0, temperature: int64
child 1, top_p: double
child 2, max_new_tokens: int64
child 3, request_seed: null
child 4, thinking: bool
child 5, engine_seed: int64
child 6, request_interval_seconds: int64
evaluation: struct<tasks: int64, apps: int64, parallel: int64, max_rounds: int64, task_class: string, docker_ima (... 63 chars omitted)
child 0, tasks: int64
child 1, apps: int64
child 2, parallel: int64
child 3, max_rounds: int64
child 4, task_class: string
child 5, docker_image: string
child 6, avd: string
child 7, seed_semantics: string
child 8, judge: string
served_model_name: string
campaign: string
serving: struct<vllm: string, instance: struct<gpus: list<item: int64>, port: int64, tensor_parallel: int64, (... 120 chars omitted)
child 0, vllm: string
child 1, instance: struct<gpus: list<item: int64>, port: int64, tensor_parallel: int64, gpu_memory_utilization: double, (... 88 chars omitted)
child 0, gpus: list<item: int64>
child 0, item: int64
child 1, port: int64
child 2, tensor_parallel: int64
child 3, gpu_memory_utilization: double
child 4, max_model_len: int64
child 5, max_num_seqs: int64
child 6, seed: int64
child 7, extra_args: list<item: string>
child 0, item: string
agent: struct<name: string, hint: bool, thinking: bool, note: string>
child 0, name: string
child 1, hint: bool
child 2, thinking: bool
child 3, note: string
to
{'model': Value('string'), 'served_model_name': Value('string'), 'thinking': Value('bool'), 'serving': {'vllm': Value('string'), 'instance': {'gpus': List(Value('int64')), 'port': Value('int64'), 'tensor_parallel': Value('int64'), 'gpu_memory_utilization': Value('float64'), 'max_model_len': Value('int64'), 'max_num_seqs': Value('int64'), 'seed': Value('int64'), 'extra_args': List(Value('string'))}}, 'agent': {'name': Value('string'), 'hint': Value('bool'), 'thinking': Value('bool'), 'note': Value('string')}, 'sampling': {'temperature': Value('int64'), 'top_p': Value('float64'), 'max_new_tokens': Value('int64'), 'request_seed': Value('null'), 'thinking': Value('bool'), 'engine_seed': Value('int64'), 'request_interval_seconds': Value('int64')}, 'evaluation': {'tasks': Value('int64'), 'apps': Value('int64'), 'parallel': Value('int64'), 'max_rounds': Value('int64'), 'task_class': Value('string'), 'docker_image': Value('string'), 'avd': Value('string'), 'seed_semantics': Value('string'), 'judge': Value('string')}, 'endpoint': Value('string'), 'campaign': Value('string'), 'time': Value('float64')}
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
model: string
benchmark: string
thinking: bool
runs: int64
tasks_per_run: int64
run_correct: struct<1: string, 2: string>
child 0, 1: string
child 1, 2: string
mean_accuracy: double
combined: string
rerun: struct<date: timestamp[s], tasks: list<item: string>, reasons: struct<calendar_5: string, calendar_7 (... 466 chars omitted)
child 0, date: timestamp[s]
child 1, tasks: list<item: string>
child 0, item: string
child 2, reasons: struct<calendar_5: string, calendar_7: string, calendar_8: string, calendar_11: string, calendar_12: (... 373 chars omitted)
child 0, calendar_5: string
child 1, calendar_7: string
child 2, calendar_8: string
child 3, calendar_11: string
child 4, calendar_12: string
child 5, calendar_14: string
child 6, cantook_8: string
child 7, clock_20: string
child 8, pimusic_7: string
child 9, pimusic_11: string
child 10, contacts_2: string
child 11, contacts_5: string
child 12, contacts_6: string
child 13, zoom_2: string
child 14, contacts_10: string
child 15, pimusic_2: string
child 16, pimusic_5: string
child 17, setting_15: string
child 18, setting_18: string
child 19, setting_6: string
child 20, calendar_9: string
child 21, cantook_9: string
child 22, clock_4: string
child 23, clock_19: string
child 3, passed: int64
child 4, note: string
time: double
endpoint: string
sampling: struct<temperature:
...
g: bool, (... 53 chars omitted)
child 0, temperature: int64
child 1, top_p: double
child 2, max_new_tokens: int64
child 3, request_seed: null
child 4, thinking: bool
child 5, engine_seed: int64
child 6, request_interval_seconds: int64
evaluation: struct<tasks: int64, apps: int64, parallel: int64, max_rounds: int64, task_class: string, docker_ima (... 63 chars omitted)
child 0, tasks: int64
child 1, apps: int64
child 2, parallel: int64
child 3, max_rounds: int64
child 4, task_class: string
child 5, docker_image: string
child 6, avd: string
child 7, seed_semantics: string
child 8, judge: string
served_model_name: string
campaign: string
serving: struct<vllm: string, instance: struct<gpus: list<item: int64>, port: int64, tensor_parallel: int64, (... 120 chars omitted)
child 0, vllm: string
child 1, instance: struct<gpus: list<item: int64>, port: int64, tensor_parallel: int64, gpu_memory_utilization: double, (... 88 chars omitted)
child 0, gpus: list<item: int64>
child 0, item: int64
child 1, port: int64
child 2, tensor_parallel: int64
child 3, gpu_memory_utilization: double
child 4, max_model_len: int64
child 5, max_num_seqs: int64
child 6, seed: int64
child 7, extra_args: list<item: string>
child 0, item: string
agent: struct<name: string, hint: bool, thinking: bool, note: string>
child 0, name: string
child 1, hint: bool
child 2, thinking: bool
child 3, note: string
to
{'model': Value('string'), 'served_model_name': Value('string'), 'thinking': Value('bool'), 'serving': {'vllm': Value('string'), 'instance': {'gpus': List(Value('int64')), 'port': Value('int64'), 'tensor_parallel': Value('int64'), 'gpu_memory_utilization': Value('float64'), 'max_model_len': Value('int64'), 'max_num_seqs': Value('int64'), 'seed': Value('int64'), 'extra_args': List(Value('string'))}}, 'agent': {'name': Value('string'), 'hint': Value('bool'), 'thinking': Value('bool'), 'note': Value('string')}, 'sampling': {'temperature': Value('int64'), 'top_p': Value('float64'), 'max_new_tokens': Value('int64'), 'request_seed': Value('null'), 'thinking': Value('bool'), 'engine_seed': Value('int64'), 'request_interval_seconds': Value('int64')}, 'evaluation': {'tasks': Value('int64'), 'apps': Value('int64'), 'parallel': Value('int64'), 'max_rounds': Value('int64'), 'task_class': Value('string'), 'docker_image': Value('string'), 'avd': Value('string'), 'seed_semantics': Value('string'), 'judge': Value('string')}, 'endpoint': Value('string'), 'campaign': Value('string'), 'time': Value('float64')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Qwen3.8-27B — Android Lab — THINKING MODE
Two runs of the native Qwen3VLAgent on Android Lab (138 tasks, 9 apps) with
thinking mode enabled (vLLM enable_thinking:true), 25 rounds max, parallel 8,
temperature 0, top_p 0.7, max_new_tokens 8192 (non-thinking baseline used 2048).
Judge: gemini-3-flash for query tasks; rule-based for operation tasks.
2026-11-06 update: 24 environment-fault tasks rerun and merged into run_1.
Independent failure attribution confirmed 20 tasks with objective environment/judge/
executor faults (judge page-gate rejects of successful pages, fixture reference
mismatches, judge checking wrong settings/contact, executor stripping leading zeros,
missing pre-existing calendar events, no mobile network) and 4 harness deaths
(UnboundLocalError, fixed before the rerun). run_1/scores/results.jsonl and the
episode tables now carry the rerun outcomes (flagged rerun: true with reasons);
original traces remain in the run_1 archive, rerun traces are in run_1_rerun/.
| Run | Correct |
|---|---|
| 1 (merged) | 79/138 |
| 2 | 78/138 |
Mean accuracy: 157/276 = 56.88%.
run_N/config.yaml— native eval config.run_1_rerun/config.yaml— rerun config (same sampling).run_N/scores/— per-task judge/rule outcomes (run_1 merged).run_1_rerun/scores/— rerun judge outcomes.run_N/trajectories.tar.gz/run_1_rerun/trajectories.tar.gz— native traces with full thinking text.two_runs/— combined episodes, protocol, summary, package manifest.
- Downloads last month
- 81