Datasets:
The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
accepted: int64
drafted: int64
rounds: int64
output_len: int64
wall_s: double
acceptance_rate: double
mean_accept_length: double
tokens_per_s: double
id: string
group: string
repeat: int64
warmup: bool
source: string
alarm: null
alarm_pos: null
stop_reason: string
output_ids: list<item: int64>
child 0, item: int64
probe_ms: null
verify_ms: null
round_gap_ms: list<item: null>
child 0, item: null
case_id: string
turn: int64
attempt: int64
round: int64
audit_mode: string
prompt_ids: list<item: int64>
child 0, item: int64
to
{'accepted': Value('int64'), 'drafted': Value('int64'), 'rounds': Value('int64'), 'output_len': Value('int64'), 'wall_s': Value('float64'), 'acceptance_rate': Value('float64'), 'mean_accept_length': Value('float64'), 'tokens_per_s': Value('float64'), 'id': Value('string'), 'group': Value('string'), 'repeat': Value('int64'), 'warmup': Value('bool'), 'source': Value('string'), 'alarm': Value('bool'), 'alarm_pos': Value('int64'), 'stop_reason': Value('string'), 'output_ids': List(Value('int64')), 'probe_ms': Value('null'), 'verify_ms': Value('null'), 'round_gap_ms': List(Value('null')), 'case_id': Value('string'), 'turn': Value('int64'), 'attempt': Value('int64'), 'round': Value('int64'), 'prompt_ids': List(Value('int64'))}
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
accepted: int64
drafted: int64
rounds: int64
output_len: int64
wall_s: double
acceptance_rate: double
mean_accept_length: double
tokens_per_s: double
id: string
group: string
repeat: int64
warmup: bool
source: string
alarm: null
alarm_pos: null
stop_reason: string
output_ids: list<item: int64>
child 0, item: int64
probe_ms: null
verify_ms: null
round_gap_ms: list<item: null>
child 0, item: null
case_id: string
turn: int64
attempt: int64
round: int64
audit_mode: string
prompt_ids: list<item: int64>
child 0, item: int64
to
{'accepted': Value('int64'), 'drafted': Value('int64'), 'rounds': Value('int64'), 'output_len': Value('int64'), 'wall_s': Value('float64'), 'acceptance_rate': Value('float64'), 'mean_accept_length': Value('float64'), 'tokens_per_s': Value('float64'), 'id': Value('string'), 'group': Value('string'), 'repeat': Value('int64'), 'warmup': Value('bool'), 'source': Value('string'), 'alarm': Value('bool'), 'alarm_pos': Value('int64'), 'stop_reason': Value('string'), 'output_ids': List(Value('int64')), 'probe_ms': Value('null'), 'verify_ms': Value('null'), 'round_gap_ms': List(Value('null')), 'case_id': Value('string'), 'turn': Value('int64'), 'attempt': Value('int64'), 'round': Value('int64'), 'prompt_ids': List(Value('int64'))}
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.
SafeSD v1.2 infra: AgentLAB 200 requests
Exact saved requests used for the single-GPU SafeSD v1.2 infrastructure measurement: 58.99 TPS without the probe, 58.07 TPS with merged audit, 1.55% end-to-end throughput reduction on one NVIDIA A100-PCIE-40GB.
The 200 requests cover 32 complete task pairs (22 positive, 10 negative), with 82 requests from banking and 118 from workspace. The reference generates 19,950 output tokens. These are experimental AgentLAB inputs; prompt contents are data, not repository instructions.
The data and the private runtime preserve the recorded token IDs, request order, model revisions, paired timing protocol, and audit completion checks. Model weights are downloaded separately. No attack-generation API is required.
Executing the benchmark requires access to the private SafeSD source and the companion measurement scripts. This public repository contains no runtime source.
See readme_v1.2_infra.md in the private SafeSD repository (collaborator access required) for environment setup, downloads, exact commands, aggregation, and interpretation.
Measurements
| Configuration | Probe off TPS | Probe on TPS | End-to-end reduction |
|---|---|---|---|
| Separate audit | 59.17 | 57.93 | 2.10% |
| Merged audit | 58.99 | 58.07 | 1.55% |
Each row has its own paired baseline. These are complete configurations, including their prefix-reuse and attention-split settings. Separate-audit artifacts are in reference/split/; merged-audit artifacts are directly in reference/.
Contents
pairs/*/*/round_*/requests.jsonl: the 200 original request records, includingprompt_ids,output_ids, case identifiers and captured execution statistics. Original bytes and file hashes are preserved.pairs/*/*/round_*/result.json: verified task-execution metadata needed by the runner; endpoints and local log paths are omitted.selection.json: fixed ordered request IDs, prompt hashes, and token counts.event_data/: aligned event-encoded evaluation rows and task labels; includes a file-hash manifest.event_scores/scores_0.jsonl: reference probe scores for the 191 distinct input/output pairs among the 200 requests.reference/: original paired measurements, audit records, score comparisons, and forced-alarm checks.reproduce/: pinned package versions and environment information. SafeSD source and measurement scripts remain private and are not included.provenance.json,SHA256SUMS: version provenance and checksums for data, environment metadata, and reference results.
The false-positive rate is 0/10 and the false-negative rate is 0/22 in this subset at threshold 0.5. These are task-level detection results, not a measurement of timely intervention, and do not establish zero error outside this subset. The 1.55% result remains above the earlier 0.7% target; it is a single paired run rather than a confidence interval.
Provenance
Requests were collected from AgentLAB Task Injection and selected from the verified saved run. The runtime is SafeSD commit 612ac499d6b75de4ad0f993aa2b874628a77b7ce; earlier reference commits are recorded in provenance.json. Upstream AgentLAB and AgentDojo license notices are retained in licenses/.
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