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Datasets:
saulkang
/
tracetune-trajectories

Languages:
English
Size:
n<1K
Tags:
agenttune
auditkit
cohere
tiny-aya
ai-safety
agentic-fine-tuning
License:
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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
messages: list<item: struct<role: string, content: string>>
  child 0, item: struct<role: string, content: string>
      child 0, role: string
      child 1, content: string
session_id: string
tuned: struct<mode: string, run_fingerprint: string, metrics: struct<schema_validity_index_pct: double, ste (... 393 chars omitted)
  child 0, mode: string
  child 1, run_fingerprint: string
  child 2, metrics: struct<schema_validity_index_pct: double, step_efficiency_ratio: double, loop_trajectory_pct: double (... 76 chars omitted)
      child 0, schema_validity_index_pct: double
      child 1, step_efficiency_ratio: double
      child 2, loop_trajectory_pct: double
      child 3, grounded_citation_precision_pct: double
      child 4, task_completion_rate_pct: double
  child 3, summary: struct<total_tasks: int64, total_tool_calls: int64, duplicate_calls: int64, tasks_completed: int64>
      child 0, total_tasks: int64
      child 1, total_tool_calls: int64
      child 2, duplicate_calls: int64
      child 3, tasks_completed: int64
  child 4, task_breakdown: list<item: struct<task_id: string, target_file: string, steps: int64, completed: bool, has_final_rep (... 11 chars omitted)
      child 0, item: struct<task_id: string, target_file: string, steps: int64, completed: bool, has_final_report: bool>
          child 0, task_id: string
          child 1, target_file: string
          child 2, steps: int64
          child 3, completed: bool
          child 4, has_final_report: bool
  child 
...
ing
  child 1, run_fingerprint: string
  child 2, metrics: struct<schema_validity_index_pct: double, step_efficiency_ratio: double, loop_trajectory_pct: double (... 76 chars omitted)
      child 0, schema_validity_index_pct: double
      child 1, step_efficiency_ratio: double
      child 2, loop_trajectory_pct: double
      child 3, grounded_citation_precision_pct: double
      child 4, task_completion_rate_pct: double
  child 3, summary: struct<total_tasks: int64, total_tool_calls: int64, duplicate_calls: int64, tasks_completed: int64>
      child 0, total_tasks: int64
      child 1, total_tool_calls: int64
      child 2, duplicate_calls: int64
      child 3, tasks_completed: int64
  child 4, task_breakdown: list<item: struct<task_id: string, target_file: string, steps: int64, completed: bool, has_final_rep (... 11 chars omitted)
      child 0, item: struct<task_id: string, target_file: string, steps: int64, completed: bool, has_final_report: bool>
          child 0, task_id: string
          child 1, target_file: string
          child 2, steps: int64
          child 3, completed: bool
          child 4, has_final_report: bool
  child 5, evaluated_at: double
auditkit_spec: string
deltas: struct<schema_validity_improvement: double, step_efficiency_gain: double, loop_reduction_pct: double (... 34 chars omitted)
  child 0, schema_validity_improvement: double
  child 1, step_efficiency_gain: double
  child 2, loop_reduction_pct: double
  child 3, citation_precision_gain: double
to
{'baseline': {'mode': Value('string'), 'run_fingerprint': Value('string'), 'metrics': {'schema_validity_index_pct': Value('float64'), 'step_efficiency_ratio': Value('float64'), 'loop_trajectory_pct': Value('float64'), 'grounded_citation_precision_pct': Value('float64'), 'task_completion_rate_pct': Value('float64')}, 'summary': {'total_tasks': Value('int64'), 'total_tool_calls': Value('int64'), 'duplicate_calls': Value('int64'), 'tasks_completed': Value('int64')}, 'task_breakdown': List({'task_id': Value('string'), 'target_file': Value('string'), 'steps': Value('int64'), 'completed': Value('bool'), 'has_final_report': Value('bool')}), 'evaluated_at': Value('float64')}, 'tuned': {'mode': Value('string'), 'run_fingerprint': Value('string'), 'metrics': {'schema_validity_index_pct': Value('float64'), 'step_efficiency_ratio': Value('float64'), 'loop_trajectory_pct': Value('float64'), 'grounded_citation_precision_pct': Value('float64'), 'task_completion_rate_pct': Value('float64')}, 'summary': {'total_tasks': Value('int64'), 'total_tool_calls': Value('int64'), 'duplicate_calls': Value('int64'), 'tasks_completed': Value('int64')}, 'task_breakdown': List({'task_id': Value('string'), 'target_file': Value('string'), 'steps': Value('int64'), 'completed': Value('bool'), 'has_final_report': Value('bool')}), 'evaluated_at': Value('float64')}, 'deltas': {'schema_validity_improvement': Value('float64'), 'step_efficiency_gain': Value('float64'), 'loop_reduction_pct': Value('float64'), 'citation_precision_gain': Value('float64')}, 'auditkit_spec': 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
              messages: list<item: struct<role: string, content: string>>
                child 0, item: struct<role: string, content: string>
                    child 0, role: string
                    child 1, content: string
              session_id: string
              tuned: struct<mode: string, run_fingerprint: string, metrics: struct<schema_validity_index_pct: double, ste (... 393 chars omitted)
                child 0, mode: string
                child 1, run_fingerprint: string
                child 2, metrics: struct<schema_validity_index_pct: double, step_efficiency_ratio: double, loop_trajectory_pct: double (... 76 chars omitted)
                    child 0, schema_validity_index_pct: double
                    child 1, step_efficiency_ratio: double
                    child 2, loop_trajectory_pct: double
                    child 3, grounded_citation_precision_pct: double
                    child 4, task_completion_rate_pct: double
                child 3, summary: struct<total_tasks: int64, total_tool_calls: int64, duplicate_calls: int64, tasks_completed: int64>
                    child 0, total_tasks: int64
                    child 1, total_tool_calls: int64
                    child 2, duplicate_calls: int64
                    child 3, tasks_completed: int64
                child 4, task_breakdown: list<item: struct<task_id: string, target_file: string, steps: int64, completed: bool, has_final_rep (... 11 chars omitted)
                    child 0, item: struct<task_id: string, target_file: string, steps: int64, completed: bool, has_final_report: bool>
                        child 0, task_id: string
                        child 1, target_file: string
                        child 2, steps: int64
                        child 3, completed: bool
                        child 4, has_final_report: bool
                child 
              ...
              ing
                child 1, run_fingerprint: string
                child 2, metrics: struct<schema_validity_index_pct: double, step_efficiency_ratio: double, loop_trajectory_pct: double (... 76 chars omitted)
                    child 0, schema_validity_index_pct: double
                    child 1, step_efficiency_ratio: double
                    child 2, loop_trajectory_pct: double
                    child 3, grounded_citation_precision_pct: double
                    child 4, task_completion_rate_pct: double
                child 3, summary: struct<total_tasks: int64, total_tool_calls: int64, duplicate_calls: int64, tasks_completed: int64>
                    child 0, total_tasks: int64
                    child 1, total_tool_calls: int64
                    child 2, duplicate_calls: int64
                    child 3, tasks_completed: int64
                child 4, task_breakdown: list<item: struct<task_id: string, target_file: string, steps: int64, completed: bool, has_final_rep (... 11 chars omitted)
                    child 0, item: struct<task_id: string, target_file: string, steps: int64, completed: bool, has_final_report: bool>
                        child 0, task_id: string
                        child 1, target_file: string
                        child 2, steps: int64
                        child 3, completed: bool
                        child 4, has_final_report: bool
                child 5, evaluated_at: double
              auditkit_spec: string
              deltas: struct<schema_validity_improvement: double, step_efficiency_gain: double, loop_reduction_pct: double (... 34 chars omitted)
                child 0, schema_validity_improvement: double
                child 1, step_efficiency_gain: double
                child 2, loop_reduction_pct: double
                child 3, citation_precision_gain: double
              to
              {'baseline': {'mode': Value('string'), 'run_fingerprint': Value('string'), 'metrics': {'schema_validity_index_pct': Value('float64'), 'step_efficiency_ratio': Value('float64'), 'loop_trajectory_pct': Value('float64'), 'grounded_citation_precision_pct': Value('float64'), 'task_completion_rate_pct': Value('float64')}, 'summary': {'total_tasks': Value('int64'), 'total_tool_calls': Value('int64'), 'duplicate_calls': Value('int64'), 'tasks_completed': Value('int64')}, 'task_breakdown': List({'task_id': Value('string'), 'target_file': Value('string'), 'steps': Value('int64'), 'completed': Value('bool'), 'has_final_report': Value('bool')}), 'evaluated_at': Value('float64')}, 'tuned': {'mode': Value('string'), 'run_fingerprint': Value('string'), 'metrics': {'schema_validity_index_pct': Value('float64'), 'step_efficiency_ratio': Value('float64'), 'loop_trajectory_pct': Value('float64'), 'grounded_citation_precision_pct': Value('float64'), 'task_completion_rate_pct': Value('float64')}, 'summary': {'total_tasks': Value('int64'), 'total_tool_calls': Value('int64'), 'duplicate_calls': Value('int64'), 'tasks_completed': Value('int64')}, 'task_breakdown': List({'task_id': Value('string'), 'target_file': Value('string'), 'steps': Value('int64'), 'completed': Value('bool'), 'has_final_report': Value('bool')}), 'evaluated_at': Value('float64')}, 'deltas': {'schema_validity_improvement': Value('float64'), 'step_efficiency_gain': Value('float64'), 'loop_reduction_pct': Value('float64'), 'citation_precision_gain': Value('float64')}, 'auditkit_spec': Value('string')}
              because column names don't match

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TraceTune: Agentic Fine-Tuning Trajectories (Lexsi Track 3)

Curated dataset of 300+ multi-step code and security investigation trajectories formatted for Lexsi Labs' AgentTune and AuditKIT, designed to post-train Cohere Tiny Aya (~3.5B).

Dataset Structure

  • data/tracetune_sft.jsonl: Conversational Supervised Fine-Tuning format containing system, user, thought, tool calls, and observations.
  • data/tracetune_trajectories.jsonl: Full Lexsi two-tier EventLog substrate (Tier 1 semantic transitions + Tier 2 granular tool tokens).

Target Tasks

  • CWE-89: SQL Injection
  • CWE-287: JWT Algorithm 'none' Bypass
  • CWE-798: Hardcoded Credentials
  • CWE-1321: Prototype Pollution

Citation & Hackathon

Built for the Lexsi - AI Safety and Alignment Hackathon (Track 3: Agentic Fine-Tuning) with Cohere Labs and Hugging Face. GitHub: https://github.com/grzdev/tracetune
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