{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:4H2GOTHVMTPGY34R7UMG335OA3","short_pith_number":"pith:4H2GOTHV","schema_version":"1.0","canonical_sha256":"e1f4674cf564de6c6f91fd186defae06d8d140700ba8c26d4a39639ab715e20f","source":{"kind":"arxiv","id":"2501.06314","version":1},"attestation_state":"computed","paper":{"title":"BioAgents: Democratizing Bioinformatics Analysis with Multi-Agent Systems","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.MA"],"primary_cat":"cs.AI","authors_text":"Ahmed Alaa, Amanda K. Hall, Daniel Tsirulnikov, David Bamman, Nikita Mehandru, Olesya Melnichenko, Scott Saponas, Venkat S. Malladi, Yulia Dubinina","submitted_at":"2025-01-10T19:30:59Z","abstract_excerpt":"Creating end-to-end bioinformatics workflows requires diverse domain expertise, which poses challenges for both junior and senior researchers as it demands a deep understanding of both genomics concepts and computational techniques. While large language models (LLMs) provide some assistance, they often fall short in providing the nuanced guidance needed to execute complex bioinformatics tasks, and require expensive computing resources to achieve high performance. We thus propose a multi-agent system built on small language models, fine-tuned on bioinformatics data, and enhanced with retrieval "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2501.06314","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-01-10T19:30:59Z","cross_cats_sorted":["cs.MA"],"title_canon_sha256":"6aa2457e2a42b2791b4a9e84aaacb89eedcf09d22ea072f84f58744b5addf499","abstract_canon_sha256":"5740709722410a7122c702beff5282ef0be36b29130b07eca884b1a42a51e026"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:00:01.233059Z","signature_b64":"lAjCJRkNg7I1K9c+MUIyOJ6OAAsu62BsjituySnCx0R4Y3KYlwe4pCBGuky/uq6d2wr9Me7DE+/oFx97nCHdBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e1f4674cf564de6c6f91fd186defae06d8d140700ba8c26d4a39639ab715e20f","last_reissued_at":"2026-07-05T10:00:01.232548Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:00:01.232548Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"BioAgents: Democratizing Bioinformatics Analysis with Multi-Agent Systems","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.MA"],"primary_cat":"cs.AI","authors_text":"Ahmed Alaa, Amanda K. Hall, Daniel Tsirulnikov, David Bamman, Nikita Mehandru, Olesya Melnichenko, Scott Saponas, Venkat S. Malladi, Yulia Dubinina","submitted_at":"2025-01-10T19:30:59Z","abstract_excerpt":"Creating end-to-end bioinformatics workflows requires diverse domain expertise, which poses challenges for both junior and senior researchers as it demands a deep understanding of both genomics concepts and computational techniques. While large language models (LLMs) provide some assistance, they often fall short in providing the nuanced guidance needed to execute complex bioinformatics tasks, and require expensive computing resources to achieve high performance. We thus propose a multi-agent system built on small language models, fine-tuned on bioinformatics data, and enhanced with retrieval "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.06314","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2501.06314/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2501.06314","created_at":"2026-07-05T10:00:01.232615+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.06314v1","created_at":"2026-07-05T10:00:01.232615+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.06314","created_at":"2026-07-05T10:00:01.232615+00:00"},{"alias_kind":"pith_short_12","alias_value":"4H2GOTHVMTPG","created_at":"2026-07-05T10:00:01.232615+00:00"},{"alias_kind":"pith_short_16","alias_value":"4H2GOTHVMTPGY34R","created_at":"2026-07-05T10:00:01.232615+00:00"},{"alias_kind":"pith_short_8","alias_value":"4H2GOTHV","created_at":"2026-07-05T10:00:01.232615+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4H2GOTHVMTPGY34R7UMG335OA3","json":"https://pith.science/pith/4H2GOTHVMTPGY34R7UMG335OA3.json","graph_json":"https://pith.science/api/pith-number/4H2GOTHVMTPGY34R7UMG335OA3/graph.json","events_json":"https://pith.science/api/pith-number/4H2GOTHVMTPGY34R7UMG335OA3/events.json","paper":"https://pith.science/paper/4H2GOTHV"},"agent_actions":{"view_html":"https://pith.science/pith/4H2GOTHVMTPGY34R7UMG335OA3","download_json":"https://pith.science/pith/4H2GOTHVMTPGY34R7UMG335OA3.json","view_paper":"https://pith.science/paper/4H2GOTHV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.06314&json=true","fetch_graph":"https://pith.science/api/pith-number/4H2GOTHVMTPGY34R7UMG335OA3/graph.json","fetch_events":"https://pith.science/api/pith-number/4H2GOTHVMTPGY34R7UMG335OA3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4H2GOTHVMTPGY34R7UMG335OA3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4H2GOTHVMTPGY34R7UMG335OA3/action/storage_attestation","attest_author":"https://pith.science/pith/4H2GOTHVMTPGY34R7UMG335OA3/action/author_attestation","sign_citation":"https://pith.science/pith/4H2GOTHVMTPGY34R7UMG335OA3/action/citation_signature","submit_replication":"https://pith.science/pith/4H2GOTHVMTPGY34R7UMG335OA3/action/replication_record"}},"created_at":"2026-07-05T10:00:01.232615+00:00","updated_at":"2026-07-05T10:00:01.232615+00:00"}