{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:QCAWBYXVC6DPCLPIT4JCCETLHE","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":"76a81cb14c5fac4aa134ac02362a6f53fdf0dee7d07e4fdd2f1200a50d466e1f","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.AI","submitted_at":"2024-12-05T11:14:01Z","title_canon_sha256":"0ab8ea2efeeb92483dbcb9d57f3d00379b3f0db22d90245ba577bfab3b2c4851"},"schema_version":"1.0","source":{"id":"2412.04075","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.04075","created_at":"2026-07-05T09:44:58Z"},{"alias_kind":"arxiv_version","alias_value":"2412.04075v1","created_at":"2026-07-05T09:44:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.04075","created_at":"2026-07-05T09:44:58Z"},{"alias_kind":"pith_short_12","alias_value":"QCAWBYXVC6DP","created_at":"2026-07-05T09:44:58Z"},{"alias_kind":"pith_short_16","alias_value":"QCAWBYXVC6DPCLPI","created_at":"2026-07-05T09:44:58Z"},{"alias_kind":"pith_short_8","alias_value":"QCAWBYXV","created_at":"2026-07-05T09:44:58Z"}],"graph_snapshots":[{"event_id":"sha256:eedca33d99a1f4532c199e2cde5f914432ca8357d8b72101ccebe573b86085d0","target":"graph","created_at":"2026-07-05T09:44:58Z","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/2412.04075/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The application of deep learning methods, particularly foundation models, in biological research has surged in recent years. These models can be text-based or trained on underlying biological data, especially omics data of various types. However, comparing the performance of these models consistently has proven to be a challenge due to differences in training data and downstream tasks. To tackle this problem, we developed an architecture-agnostic benchmarking approach that, instead of evaluating the models directly, leverages entity representation vectors from each model and trains simple pred","authors_text":"Eden Zohar, Matan Ninio, Michael Morris Danziger, Yishai Shimoni, Yoav Kan-Tor","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.AI","submitted_at":"2024-12-05T11:14:01Z","title":"Does your model understand genes? A benchmark of gene properties for biological and text models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.04075","kind":"arxiv","version":1},"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:c1f7173b6bc33efa58c19b567f8545478861cd5543e608f64c7b2efdb9e457f3","target":"record","created_at":"2026-07-05T09:44:58Z","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":"76a81cb14c5fac4aa134ac02362a6f53fdf0dee7d07e4fdd2f1200a50d466e1f","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.AI","submitted_at":"2024-12-05T11:14:01Z","title_canon_sha256":"0ab8ea2efeeb92483dbcb9d57f3d00379b3f0db22d90245ba577bfab3b2c4851"},"schema_version":"1.0","source":{"id":"2412.04075","kind":"arxiv","version":1}},"canonical_sha256":"808160e2f51786f12de89f1221126b390c60cd224b4b9d29cc77945af0cbbcdc","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"808160e2f51786f12de89f1221126b390c60cd224b4b9d29cc77945af0cbbcdc","first_computed_at":"2026-07-05T09:44:58.738052Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:44:58.738052Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"DpUYsWDtYgGge+YQljaIPLgugu8CnHWF0Kb7XSxRVVpzHzmxB3uu/ZpfGgN7nFKwY9SPNVhskzlG9GSnbBdRAA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:44:58.738549Z","signed_message":"canonical_sha256_bytes"},"source_id":"2412.04075","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c1f7173b6bc33efa58c19b567f8545478861cd5543e608f64c7b2efdb9e457f3","sha256:eedca33d99a1f4532c199e2cde5f914432ca8357d8b72101ccebe573b86085d0"],"state_sha256":"cec133686d71cea907544314ba5750f43f013d8460deaa3f236fdcc71100e881"}