iit-patna-cse-ai/GenBench_test

GenBench CoCG QA Test Set

Test split of GenBench's multi-hop genetic reasoning QA dataset, generated from genbench-iitp/genbench-coding-qa and genbench-iitp/genbench-noncoding-qa's companion knowledge graphs, built for CoCG (Co-Evolving Confidence Graph) agent training/evaluation.

2349 items total across two configs, one per pipeline:

coding config (1423 items, pipeline coding_variant)

task_type	count
coding_variant	22
conservation_reasoning	160
counterfactual	160
disease_reasoning	123
evidence_attribution	160
hallucination_detection	160
interaction_propagation	160
mechanistic_explanation	160
path_traversal	158
structural_effect	160

noncoding config (926 items, pipeline noncoding_regulatory)

task_type	count
disease_reasoning	126
hallucination_detection	160
interaction_propagation	160
mechanistic_explanation	160
path_traversal	160
regulatory_reasoning	160

Schema

Each item has:

• id, task_type, pipeline (coding_variant/noncoding_regulatory), difficulty
• question, answer, choices (MCQ options, when applicable)
• context -- templated chain narration
• 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 graphs

coding_graph.json / noncoding_graph.json are the exact knowledge graphs these items' reasoning_chain node IDs refer to -- load with GenBench's GraphBuilder.load() to resolve full node/edge attributes beyond what's inlined in each item.

Source

Generated from the coding/noncoding graphs in 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. Split via scripts/split_qa_dataset.py, stratified by (pipeline, task_type), 20% test / 80% train, seed=42.

GenBench CoCG QA Test Set

Test split of GenBench's multi-hop genetic reasoning QA dataset, generated from genbench-iitp/genbench-coding-qa and genbench-iitp/genbench-noncoding-qa's companion knowledge graphs, built for CoCG (Co-Evolving Confidence Graph) agent training/evaluation.

2349 items total across two configs, one per pipeline:

coding config (1390 items, pipeline coding_variant)

task_type	count
coding_variant	4
conservation_reasoning	124
counterfactual	190
disease_reasoning	76
evidence_attribution	160
hallucination_detection	163
interaction_propagation	145
mechanistic_explanation	135
path_traversal	301
structural_effect	92

noncoding config (959 items, pipeline noncoding_regulatory)

task_type	count
disease_reasoning	76
hallucination_detection	115
interaction_propagation	151
mechanistic_explanation	181
path_traversal	322
regulatory_reasoning	114

Schema

Each item has:

• id, task_type, pipeline (coding_variant/noncoding_regulatory), difficulty
• question, answer, choices (MCQ options, when applicable)
• context -- templated chain narration
• 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 graphs

coding_graph.json / noncoding_graph.json are the exact knowledge graphs these items' reasoning_chain node IDs refer to -- load with GenBench's GraphBuilder.load() to resolve full node/edge attributes beyond what's inlined in each item.

Source

Generated from the coding/noncoding graphs in 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. Split via scripts/split_qa_dataset.py, stratified by (pipeline, task_type), 20% test / 80% train, seed=42 -- plus a gene-level generalization guarantee (v5): 165 genes are held out entirely from train, and 31.1% of the test set (731/2349 items) touches NO gene that appears anywhere in train -- a genuine unseen-gene slice, not just an unseen-item one. Same item content as the v4 split; only the train/test assignment changed to add this guarantee (same overall 20% test size).