> ## Documentation Index > Fetch the complete documentation index at: https://docs.worldflux.ai/llms.txt > Use this file to discover all available pages before exploring further. # Eval bridges > An eval bridge maps a third-party benchmark or uploaded result bundle into WorldFlux audit input or manifest-friendly artifacts. ## Module path ``` src/worldflux/eval_bridges/ ├── annex_iv_derived.py # Annex IV derived raw result import ├── cosmos.py # Cosmos generation bridge ├── cosmos_predict.py # Cosmos-Predict raw result import ├── external_demo_harness.py # external demo harness wrapper ├── gr00t_isaac_lab.py # GR00T N1.7 demo and raw result import ├── libero.py # LIBERO suites and PRO/PLUS perturbation resolver ├── libero_plus.py # LIBERO-Plus difficulty config helper ├── libero_pro.py # LIBERO-PRO perturbation demo/import helper ├── minecraft.py # Minecraft offline RL replay ├── uploaded_eval.py # uploaded eval raw result import └── vjepa.py # V-JEPA latent embedding eval ``` A bridge is a function (sometimes plus dataclass output types). Adapters call the bridge from their `run.py`; the manifest writer picks up the result. ## Pattern LIBERO is the cleanest example. ```python theme={null} # src/worldflux/eval_bridges/libero.py LIBERO_SUITES = ("libero_spatial", "libero_object", "libero_goal", "libero_10") @dataclass(frozen=True) class LIBEROEpisodeRecord: suite: str task_id: int seed: int success: bool episode_return: float steps: int duration_seconds: float @dataclass(frozen=True) class LIBEROEvalSummary: suite: str num_episodes: int success_rate: float mean_episode_return: float mean_steps_per_episode: float episodes: list[LIBEROEpisodeRecord] def run_libero_eval( *, server_command: tuple[str, ...], server_cwd: Path, server_env: dict[str, str], server_port: int = 8000, suites: tuple[str, ...] = LIBERO_SUITES, num_episodes_per_task: int = 10, output_dir: Path | None = None, ) -> dict[str, LIBEROEvalSummary]: ... ``` The bridge starts the adapter's WebSocket policy server, waits for it to be ready, runs the LIBERO benchmark client per suite, and returns one `LIBEROEvalSummary` per suite. If `output_dir` is set, it also writes a JSON artifact with the same content. ## What ships | Bridge | Suite or source | Entry function | Output | | ----------------------- | --------------------------------------------------------- | --------------------------------------------------------------------- | ------------------------------------------------------- | | `annex_iv_derived` | Annex IV derived public claims | `import_annex_iv_derived_raw_results(...)` | `audit_input_annex_iv_derived.json` | | `cosmos` | Cosmos generation | `run_cosmos_generation(...)` | `CosmosGenerationRecord` list plus generation artifacts | | `cosmos_predict` | Cosmos-Predict raw results | `run_cosmos_predict(...)`, `import_cosmos_predict_raw_results(...)` | normalized audit input | | `external_demo_harness` | external demo harness | `run_external_demo_harness(...)` | harness result payload | | `gr00t_isaac_lab` | GR00T N1.7 Isaac Lab / official GR00T LIBERO sim evidence | `run_gr00t_isaac_lab(...)`, `import_groot_isaac_lab_raw_results(...)` | `audit_input_groot.json` | | `libero` | LIBERO 4-suite benchmark | `run_libero_eval(...)` | `dict[str, LIBEROEvalSummary]` | | `libero_plus` | LIBERO-Plus perturbation config | `build_libero_plus_difficulty_config(...)` | config payload | | `libero_pro` | LIBERO-PRO perturbation demo/import | `run_libero_pro(...)`, `import_openvla_libero_raw_results(...)` | normalized audit input | | `minecraft` | Minecraft offline RL replay | `run_minecraft_eval(...)` | replay summary | | `uploaded_eval` | uploaded eval evidence | `import_uploaded_eval(...)` | normalized audit input | | `vjepa` | V-JEPA latent embedding probe | `run_vjepa_embed(...)` | `VJEPAEmbedRecord` list plus embed artifacts | Some bridges run a local adapter and return dataclasses; others import raw external results into the audit-input schema. The manifest writer or audit flow then owns the final JSON surface. Import bridges do not upload artifacts or decide whether a claim is publishable. ## Wiring an adapter A curated adapter's `run.py` typically does three things: 1. Start the adapter's policy server (or call its inference function directly). 2. Call the bridge with the right `task` argument. 3. Hand the bridge's return value to the manifest writer. ```python theme={null} # src/worldflux/templates/openpi/run.py (sketch) from worldflux.eval_bridges.libero import run_libero_eval results = run_libero_eval( server_command=("uv", "run", "python", "serve.py"), server_cwd=adapter_dir, server_env=os.environ.copy(), output_dir=output_dir, ) write_manifest(results) ``` ## Adding a bridge Add `src/worldflux/eval_bridges/.py`. Keep it function-first; only add a class if state genuinely needs to live across episodes. A frozen `@dataclass` with the fields the dashboard will need to render. Avoid optionals where you can; `mean_*` should always be a float, even if it's 0.0. If the suite produces per-episode detail, write a JSON file under `output_dir` and reference it from the dataclass. The dashboard's run detail panel previews JSON inline. `tests/eval_bridges/test_.py` plus a snapshot or fixture for the smallest deterministic path. Use dry-run or raw-import fixtures for GPU-only suites; do not label smoke data as Grade A benchmark evidence. ## When you do not need a bridge If the suite already returns JSON in roughly the shape the manifest expects, the adapter can write straight into `manifest.metrics` and skip the bridge layer. Bridges exist for suites whose native output (per-episode logs, per-task success matrices, vector embeddings) does not map onto the manifest 1:1.