{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:KQQFSRRAPVALHHS7FVIKM2YIP3","short_pith_number":"pith:KQQFSRRA","schema_version":"1.0","canonical_sha256":"54205946207d40b39e5f2d50a66b087ed748808df8bb73a90c6a7f49129daca4","source":{"kind":"arxiv","id":"2607.13608","version":1},"attestation_state":"computed","paper":{"title":"Automatic Ordinary Differential Equations Discovery For Biological Systems Using Large Language Model Powered Agentic System","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["math.DS"],"primary_cat":"cs.AI","authors_text":"Arad Zulti, David Krongauz, Eran Segal, Teddy Lazebnik","submitted_at":"2026-07-15T08:56:56Z","abstract_excerpt":"Automatic scientific discovery has long been a goal of computational scholars - a machine that can discover nature's secrets on its own, moving computational systems beyond data-fitting tools toward the generation and refinement of mechanistic models of the universe. Recent advances in symbolic regression (SR) and large-language-model (LLM)-based agents suggest that such systems can recover equations from data, incorporate domain priors, and automate parts of the research workflow. However, most existing approaches either focus on narrow equation-discovery benchmarks or broad end-to-end automa"},"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":"2607.13608","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.AI","submitted_at":"2026-07-15T08:56:56Z","cross_cats_sorted":["math.DS"],"title_canon_sha256":"76482ab5dbbd2b97fd60ab42f505010d22d953ede7d55cf0f82ac5bb5459568f","abstract_canon_sha256":"605087413de974085f42746b562bf160bcad1833827237241ba05ef938de44c2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-16T01:22:56.745132Z","signature_b64":"+rb1H6sHQhLD+WoBTEEsewgYPcsSRpQpKuuktXLQS1QcmgytQuhzhdlsaqdc8aeW7e3qYv1jW3BQ0ELztz6vBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"54205946207d40b39e5f2d50a66b087ed748808df8bb73a90c6a7f49129daca4","last_reissued_at":"2026-07-16T01:22:56.744257Z","signature_status":"signed_v1","first_computed_at":"2026-07-16T01:22:56.744257Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Automatic Ordinary Differential Equations Discovery For Biological Systems Using Large Language Model Powered Agentic System","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["math.DS"],"primary_cat":"cs.AI","authors_text":"Arad Zulti, David Krongauz, Eran Segal, Teddy Lazebnik","submitted_at":"2026-07-15T08:56:56Z","abstract_excerpt":"Automatic scientific discovery has long been a goal of computational scholars - a machine that can discover nature's secrets on its own, moving computational systems beyond data-fitting tools toward the generation and refinement of mechanistic models of the universe. Recent advances in symbolic regression (SR) and large-language-model (LLM)-based agents suggest that such systems can recover equations from data, incorporate domain priors, and automate parts of the research workflow. However, most existing approaches either focus on narrow equation-discovery benchmarks or broad end-to-end automa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.13608","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/2607.13608/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":"2607.13608","created_at":"2026-07-16T01:22:56.744720+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.13608v1","created_at":"2026-07-16T01:22:56.744720+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.13608","created_at":"2026-07-16T01:22:56.744720+00:00"},{"alias_kind":"pith_short_12","alias_value":"KQQFSRRAPVAL","created_at":"2026-07-16T01:22:56.744720+00:00"},{"alias_kind":"pith_short_16","alias_value":"KQQFSRRAPVALHHS7","created_at":"2026-07-16T01:22:56.744720+00:00"},{"alias_kind":"pith_short_8","alias_value":"KQQFSRRA","created_at":"2026-07-16T01:22:56.744720+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/KQQFSRRAPVALHHS7FVIKM2YIP3","json":"https://pith.science/pith/KQQFSRRAPVALHHS7FVIKM2YIP3.json","graph_json":"https://pith.science/api/pith-number/KQQFSRRAPVALHHS7FVIKM2YIP3/graph.json","events_json":"https://pith.science/api/pith-number/KQQFSRRAPVALHHS7FVIKM2YIP3/events.json","paper":"https://pith.science/paper/KQQFSRRA"},"agent_actions":{"view_html":"https://pith.science/pith/KQQFSRRAPVALHHS7FVIKM2YIP3","download_json":"https://pith.science/pith/KQQFSRRAPVALHHS7FVIKM2YIP3.json","view_paper":"https://pith.science/paper/KQQFSRRA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.13608&json=true","fetch_graph":"https://pith.science/api/pith-number/KQQFSRRAPVALHHS7FVIKM2YIP3/graph.json","fetch_events":"https://pith.science/api/pith-number/KQQFSRRAPVALHHS7FVIKM2YIP3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KQQFSRRAPVALHHS7FVIKM2YIP3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KQQFSRRAPVALHHS7FVIKM2YIP3/action/storage_attestation","attest_author":"https://pith.science/pith/KQQFSRRAPVALHHS7FVIKM2YIP3/action/author_attestation","sign_citation":"https://pith.science/pith/KQQFSRRAPVALHHS7FVIKM2YIP3/action/citation_signature","submit_replication":"https://pith.science/pith/KQQFSRRAPVALHHS7FVIKM2YIP3/action/replication_record"}},"created_at":"2026-07-16T01:22:56.744720+00:00","updated_at":"2026-07-16T01:22:56.744720+00:00"}