Datasets:

novcor
/

gold-trace-agent-trace-assay-001

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
artifact_type: string
content_sha256: string
derivation_method: string
evidence_posture: string
intended_uses: list<item: string>
  child 0, item: string
internal_ip_status: string
known_gaps: list<item: string>
  child 0, item: string
lane: string
language: string
license_profile: string
privacy_status: string
prohibited_claims: list<item: string>
  child 0, item: string
prompt_summary: string
quality_review: string
record_id: string
release_version: string
response_document: string
rights_status: string
source_artifact_hash_sha256: string
source_candidate_id: string
source_disclosure: string
source_hash_sha256: string
source_ref: string
title: string
transformation_notes: list<item: string>
  child 0, item: string
candidate_id: string
remediation: string
decision_reasons: list<item: string>
  child 0, item: string
decision: string
to
{'candidate_id': Value('string'), 'decision': Value('string'), 'decision_reasons': List(Value('string')), 'remediation': Value('string'), 'source_artifact_hash_sha256': Value('string'), 'source_hash_sha256': Value('string'), 'source_ref': 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 489, 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 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, 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 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, 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 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 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              artifact_type: string
              content_sha256: string
              derivation_method: string
              evidence_posture: string
              intended_uses: list<item: string>
                child 0, item: string
              internal_ip_status: string
              known_gaps: list<item: string>
                child 0, item: string
              lane: string
              language: string
              license_profile: string
              privacy_status: string
              prohibited_claims: list<item: string>
                child 0, item: string
              prompt_summary: string
              quality_review: string
              record_id: string
              release_version: string
              response_document: string
              rights_status: string
              source_artifact_hash_sha256: string
              source_candidate_id: string
              source_disclosure: string
              source_hash_sha256: string
              source_ref: string
              title: string
              transformation_notes: list<item: string>
                child 0, item: string
              candidate_id: string
              remediation: string
              decision_reasons: list<item: string>
                child 0, item: string
              decision: string
              to
              {'candidate_id': Value('string'), 'decision': Value('string'), 'decision_reasons': List(Value('string')), 'remediation': Value('string'), 'source_artifact_hash_sha256': Value('string'), 'source_hash_sha256': Value('string'), 'source_ref': Value('string')}
              because column names don't match

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Gold Trace Agent Trace Assay 001

Foundational source-derived public assay

Version: 1.1.0
Release date: 2026-07-31
Publisher: Gold Trace Dataworks
Contact: hello@gtdataworks.com

This is the first public source-derived assay set from Gold Trace Dataworks.

It contains five standalone technical reasoning artifacts derived from a private heterogeneous agent-trace archive. The five public questions and their reference answers are both released because this is an inspectable public set, not a secret benchmark. A separate first-ranked source candidate remains private as a true unseen holdout.

The private conversations, raw candidate archive, and holdout contents are excluded.

The four bars

Bar 01 — Public questions

Five prompt summaries for evaluation, rubric development, and method inspection:

data/public_questions.jsonl

Bar 02 — Public reference answers

The corresponding five transformed reference answers, with evidence posture and known gaps:

data/public_reference_answers.jsonl
data/public_assay_records.jsonl
data/PUBLIC_READING_EDITION.md

Because these answers are public, results against this split must not be called blind holdout performance.

Bar 03 — Assay decisions

The release preserves what was admitted and what stayed held:

data/selection_ledger.jsonl — five admitted transformed records;
data/held_candidate_ledger.jsonl — eighteen ordinary held candidates plus one designated private holdout;
docs/ASSAY_REPORT.md;
docs/HOLDOUT_AND_REFERENCE_ANSWER_POLICY.md.

Bar 04 — Evidence and validation

Schemas, hashes, manifests, validation code, and package receipts make the release inspectable and tamper-evident.

What makes this an assay

Stage	Count
Exported candidate rows	2,967
Unique `trace_id + turn_index` rows	2,252
Unique normalized user+assistant records	1,473
Gold-tier records after exact content deduplication	675
Strongest machine-rated candidates	224
Source-derived document candidates selected for manual review	24
Public records admitted in this bounded release	5
Designated private holdout	1

The automated ranking did not grant release. Public admission required manual prompt reconstruction, privacy review, internal-IP abstraction, editorial deduplication, explicit rights posture, known-gap documentation, and manifest sealing.

The five public records

From architecture prototype to evidence-backed containment system
Classify before deduplication in provenance-first artifact mining
Forensic receipt layer for agent work
High-grade signal mining for trace archives
Bounded drift audit and evidence-supported repair

Intended uses

method inspection;
evaluation and rubric prototyping;
source-derived reasoning analysis;
dataset-governance training;
agent-system claim analysis;
curation workflow examples.

Not represented as

raw private conversations;
chronological multi-turn chat;
original prompt-response pairs;
SFT-ready training data;
a statistically representative benchmark;
blind holdout performance on the five public questions;
proof of model or runtime performance;
legal clearance for excluded private source material.

Validate

python tools/validate_release.py .
python examples/load_sample.py data/public_assay_records.jsonl

Licensing

Public transformed data and documentation are released under CC BY 4.0. Included Python code is released under the MIT License. The private source archive, omitted raw conversations, designated holdout, withheld candidate artifacts, third-party marks, and excluded materials are not licensed or redistributed by this package.

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