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Get Started Free →LLM-based extraction of per-paper workflow graphs and content/structural reproducibility scoring.
| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-21 | ✗→✓ | ▲ Improved | 9% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 69% | 0% |
| case-03 | ✗→✓ | ▲ Improved | -37% | 0% |
| case-04 | ✗→✓ | ▲ Improved | -46% | 0% |
| case-05 | ✗→✓ | ▲ Improved | -61% | 0% |
You are interacting with the src/reproducibility/ module. This decomposes a paper's own described pipeline (source/method/experiment/sink steps) into a workflow graph and scores how reproducible that pipeline is from the paper's own text.
ContentWeights, StructuralWeights) within Python scripts. Bind them to LLMConfig and the reproducibility_assessment block of config.yaml, loaded via config_loader.load_reproducibility_config().WorkflowNode must carry a non-empty source_quote copied verbatim from the paper's full text. Never accept or fabricate a node without one — this is the node's entire evidentiary basis.project_config.fulltext.enabled is false and no --fulltext-dir override is supplied, treat a reproducibility_assessment run as a no-op that still yields valid, empty-but-well-formed outputs — never mistake the warning for a crash.Other measured skills in the registry, with their headline benchmark lift.