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# Known limitations

- NVIDIA Open-SWE-Traces is very large. The complete v1.2/OpenHands population
  is imported, but the other 626,189 rows across v1.0, v1.1 and the v1.2
  SWE-agent/mini-SWE-agent splits are not persisted locally. Their source
  Parquet volume is 55,056,684,304 bytes.
- No benchmark container was independently replayed. Outcomes are clearly
  labelled as source-evaluator/source-reported evidence, never as local
  reproduction. This leaves quality tiers 1 and 2 empty.
- No baseline language model, model weights or inference accelerator is
  configured. The pinned Nemotron tokenizer/chat-template demonstration builds
  correct labels and batches, but teacher-forced model NLL remains not run.
- The public dataset is federated. It hosts full canonical payloads for ten
  sources and complete metadata/pinned pointers for three sources whose
  transformed-redistribution rights remain uncertain. Authorized users can
  supply locally ingested overlays; access is not automated or implied.
- AgentSuite BFCL contains evaluated final function-call answers but no returned
  observations. ALFWorld planner files likewise omit action-paired environment
  observations. They are coverage findings, not trajectory-library records.
- DeepSWE/Datacurve did not yield an exact public, pinned full-trajectory
  artifact during discovery. This means none was found, not proof that none
  exists.
- The checked-in ToolBench repository exposes only 15 examples; 11 satisfy the
  strict trajectory gate. Full upstream generation data was not independently
  enumerated.
- Mind2Web's hosted Parquet conversion exposes 1,009 records. The conversion is
  marked partial upstream, so this is complete for the exposed conversion but
  not claimed as the entirety of every historical Mind2Web artifact.
- Cost, latency and token fields are sparse and source-specific. Raw values are
  retained, but incompatible units are not pooled into a misleading global
  comparison.
- Tool definitions are absent from several releases. Calls and returned
  observations remain usable, while the missing definitions are declared
  rather than reconstructed.
- Multimodal artifacts are referenced, not embedded or fetched. Raw records
  can be large because lossless source preservation is intentional.
- Task overlap prevention depends on stable/native IDs and the explicit family
  normalization implemented by adapters. It cannot discover semantic overlap
  between unrelated identifiers automatically.

Exact per-source gaps and denominators are in
[`trajectory_sources.md`](trajectory_sources.md) and
`data/coverage/trajectory_sources.json`.