Duplicate from rashmi8235/genbench-coding-qa

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README.md

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+---
+license: unknown
+task_categories:
+- question-answering
+tags:
+- genomics
+- knowledge-graph
+- multi-hop-reasoning
+- biology
+pretty_name: GenBench CoCG QA Dataset
+---
+
+# GenBench CoCG QA Dataset
+
+Multi-hop genetic reasoning QA items generated from GenBench's knowledge
+graph (Ensembl, ClinVar, VEP, BioGRID, STRING, Reactome, UniProt, GO,
+SIGNOR, OmniPath, KEGG, DisGeNET, OpenTargets, PubTator3, GTEx, and more),
+built for CoCG (Co-Evolving Confidence Graph) agent training.
+
+**2513 items across 11 task types.**
+
+## Task types
+
+| task_type | count |
+|---|---|
+| `coding_variant` | 53 |
+| `conservation_reasoning` | 246 |
+| `counterfactual` | 246 |
+| `disease_reasoning` | 246 |
+| `evidence_attribution` | 246 |
+| `hallucination_detection` | 246 |
+| `interaction_propagation` | 246 |
+| `mechanistic_explanation` | 246 |
+| `path_traversal` | 246 |
+| `structural_effect` | 246 |
+| `tissue_specific` | 246 |
+
+## Schema
+
+Each item has:
+- `id`, `task_type`, `pipeline` (`coding_variant`/`noncoding_regulatory`), `difficulty`
+- `question`, `answer`, `choices` (MCQ options, when applicable)
+- `context` -- either a templated chain narration, or (if `llm_rewrite` was
+  applied) an LLM-rewritten fluent Step/Evidence/Interpretation/Conclusion
+  narrative
+- `reasoning_chain` -- the grounded, machine-checkable multi-hop path
+  (`steps`: each with `source_node_id`/`target_node_id`/`edge_relation`/
+  `edge_confidence`/`edge_source_db`), never touched by any LLM step
+- `modality_data` -- raw modality payloads (sequence, structural,
+  transcriptomic, post_translational, signaling_role, etc.) attached to the
+  chain's anchor nodes
+- `evidence` -- supporting evidence entries with source database/PMID
+- `path_confidence_score` -- continuous, confidence-derived difficulty score
+
+## Companion graph
+
+`graph.json` (if included in this repo) is the exact knowledge graph these
+items' `reasoning_chain` node IDs refer to -- load it with
+[GenBench's `GraphBuilder.load()`](https://github.com/murk07/GenBench) to
+resolve full node/edge attributes beyond what's inlined in each item.
+
+## Source
+
+Generated from `data\curated\qa_dataset.jsonl` in [GenBench](https://github.com/murk07/GenBench),
+the substrate for CoCG (Co-Evolving Confidence Graph) agent training --
+per-edge, per-modality KG confidence that co-adapts with an RL policy
+during training rather than treating the KG as a frozen oracle.

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