{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:4MI4MCB6X6XRAD57QAXZMQRDXV","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"efe4326326843b64c4b5660511c2290b44992fa76cf8faf71355e497bb707838","cross_cats_sorted":["cs.AI","q-bio.GN"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-06-21T17:55:24Z","title_canon_sha256":"8214ffab70434934384418b72ff9b96bd8f8c366e80f03d8da99239f5a864dfd"},"schema_version":"1.0","source":{"id":"2406.15341","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.15341","created_at":"2026-07-05T10:45:53Z"},{"alias_kind":"arxiv_version","alias_value":"2406.15341v3","created_at":"2026-07-05T10:45:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.15341","created_at":"2026-07-05T10:45:53Z"},{"alias_kind":"pith_short_12","alias_value":"4MI4MCB6X6XR","created_at":"2026-07-05T10:45:53Z"},{"alias_kind":"pith_short_16","alias_value":"4MI4MCB6X6XRAD57","created_at":"2026-07-05T10:45:53Z"},{"alias_kind":"pith_short_8","alias_value":"4MI4MCB6","created_at":"2026-07-05T10:45:53Z"}],"graph_snapshots":[{"event_id":"sha256:2a88c4ccd6aa794a06f3047bec6d5d1b0291822cad7a046d15b8a7dce1df5395","target":"graph","created_at":"2026-07-05T10:45:53Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2406.15341/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent advancements in machine learning have significantly improved the identification of disease-associated genes from gene expression datasets. However, these processes often require extensive expertise and manual effort, limiting their scalability. Large Language Model (LLM)-based agents have shown promise in automating these tasks due to their increasing problem-solving abilities. To support the evaluation and development of such methods, we introduce GenoTEX, a benchmark dataset for the automated analysis of gene expression data. GenoTEX provides analysis code and results for solving a wi","authors_text":"Haohan Wang, Haoyang Liu, Shuyu Chen, Ye Zhang","cross_cats":["cs.AI","q-bio.GN"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-06-21T17:55:24Z","title":"GenoTEX: An LLM Agent Benchmark for Automated Gene Expression Data Analysis"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.15341","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:d2d493a47a4c23431b67bc2a20f83a1a5223a6725b107ee9af9738757bd4ddff","target":"record","created_at":"2026-07-05T10:45:53Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"efe4326326843b64c4b5660511c2290b44992fa76cf8faf71355e497bb707838","cross_cats_sorted":["cs.AI","q-bio.GN"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-06-21T17:55:24Z","title_canon_sha256":"8214ffab70434934384418b72ff9b96bd8f8c366e80f03d8da99239f5a864dfd"},"schema_version":"1.0","source":{"id":"2406.15341","kind":"arxiv","version":3}},"canonical_sha256":"e311c6083ebfaf100fbf802f964223bd6a54292e23d2826cd6b6eabfa50ec5ee","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e311c6083ebfaf100fbf802f964223bd6a54292e23d2826cd6b6eabfa50ec5ee","first_computed_at":"2026-07-05T10:45:53.318207Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:45:53.318207Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"dYEicihyzeiePTXJkyGEerbeJO7lhGD82kKzrERt0z9ywiB0j8G1sLN/VAXrDxj114snLvg2U41QbziLdpoRBw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:45:53.318745Z","signed_message":"canonical_sha256_bytes"},"source_id":"2406.15341","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d2d493a47a4c23431b67bc2a20f83a1a5223a6725b107ee9af9738757bd4ddff","sha256:2a88c4ccd6aa794a06f3047bec6d5d1b0291822cad7a046d15b8a7dce1df5395"],"state_sha256":"15190f3633a680b6fd35130ea01cf684832c7f69fb1e9b42e570e6be3c56cbd3"}