# Permission audit and federated content

Permission is not a single Boolean. The registry records, for every canonical
source, separate findings for public access, local research use, model
training, commercial training, redistribution of original data, and
redistribution of transformed canonical trajectories. Each finding has both a
permission value and a verification value, plus source-specific conditions and
evidence URLs.

The audit vocabulary is:

- permission: `permitted`, `permitted_with_conditions`, `uncertain`, or
  `restricted`;
- verification: `verified`, `inferred`, or `uncertain`;
- public payload: `hosted` or `metadata_only`.

The complete local inventory remains 155,278 trajectories. Publication does
not change scores, outcomes, ranks, splits, lineage or canonical hashes.
Instead, the public Hugging Face release contains the complete metadata index
and the payloads supported by the audit. Metadata-only sources retain pinned
upstream URLs and canonical shard hashes.

The initial public payload decision is:

- hosted: `swegym_openhands`, `nvidia_open_swe_traces`, `terminaltraj`,
  `official_tau3`, `mind2web`, `toolbench`,
  `swebench_verified_trajectories`,
  `swebench_multilingual_trajectories`, `terminalbench_trajectories`, and
  `swesmith_trajectories`;
- metadata-only: `agentsuite_tau2`, `swegym_sampled`, and
  `browsecomp_rollouts`.

This is not a blanket declaration that hosted data is unrestricted or that
metadata-only data cannot be used. For example, Mind2Web combines CC-BY-4.0
metadata with a research-purpose statement; tau benchmark training creates
evaluation contamination that must be disclosed; and software-engineering
records can embed code governed by per-repository licenses.

Downstream projects can select a policy directly:

```python
from agent_trajectories import CanonicalDatasetAdapter, DatasetQuery

dataset = CanonicalDatasetAdapter("./sft-benchmark-trajectories")
strict_research = DatasetQuery.for_training(
    content_availability=("hosted",),
    training_permissions=("permitted", "permitted_with_conditions"),
    training_verifications=("verified",),
    commercial_training_permissions=("permitted", "permitted_with_conditions"),
)
for metadata in dataset.metadata(strict_research):
    print(metadata.source_id, metadata.source_permissions.to_dict())
```

An authorized user can explicitly ingest a metadata-only source at its pinned
revision and overlay the canonical shards:

```python
dataset = CanonicalDatasetAdapter(
    "./sft-benchmark-trajectories",
    content_overlays={"agentsuite_tau2": "./canonical/agentsuite_tau2"},
)
```

The overlay is local and is never uploaded by the adapter. Canonical trajectory
IDs are matched against the persisted full-inventory index. Queries that select
missing content fail with `MissingTrajectoryContentError` and the relevant
source IDs instead of silently omitting records.