{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:V46WF5RAWUQIWKKYYFBTW2L2ZV","short_pith_number":"pith:V46WF5RA","schema_version":"1.0","canonical_sha256":"af3d62f620b5208b2958c1433b697acd572f33e88758dc9b8b499b8c210d8467","source":{"kind":"arxiv","id":"2410.09112","version":1},"attestation_state":"computed","paper":{"title":"HLM-Cite: Hybrid Language Model Workflow for Text-based Scientific Citation Prediction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.DL","authors_text":"Fengli Xu, Jian Yuan, Jingyang Fan, Qianyue Hao, Yong Li","submitted_at":"2024-10-10T10:46:06Z","abstract_excerpt":"Citation networks are critical in modern science, and predicting which previous papers (candidates) will a new paper (query) cite is a critical problem. However, the roles of a paper's citations vary significantly, ranging from foundational knowledge basis to superficial contexts. Distinguishing these roles requires a deeper understanding of the logical relationships among papers, beyond simple edges in citation networks. The emergence of LLMs with textual reasoning capabilities offers new possibilities for discerning these relationships, but there are two major challenges. First, in practice,"},"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":"2410.09112","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DL","submitted_at":"2024-10-10T10:46:06Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"4cef08af4d687f5c59fe8454f5c332f5e60afdcbe242817f1f244c50d0477e47","abstract_canon_sha256":"2855af37e81396f182a654873538dc7c09282aed0ca28997cf7b0a913fab2d48"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:19:34.039796Z","signature_b64":"5EkmciWY5ahJPDyOSfeVVmPiQZ77LYZboNEdNmP3xPi2vShqQzN9mbbm/6RrsvB1JD95a435maCkV+WrMAzpAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"af3d62f620b5208b2958c1433b697acd572f33e88758dc9b8b499b8c210d8467","last_reissued_at":"2026-07-05T09:19:34.039290Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:19:34.039290Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"HLM-Cite: Hybrid Language Model Workflow for Text-based Scientific Citation Prediction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.DL","authors_text":"Fengli Xu, Jian Yuan, Jingyang Fan, Qianyue Hao, Yong Li","submitted_at":"2024-10-10T10:46:06Z","abstract_excerpt":"Citation networks are critical in modern science, and predicting which previous papers (candidates) will a new paper (query) cite is a critical problem. However, the roles of a paper's citations vary significantly, ranging from foundational knowledge basis to superficial contexts. Distinguishing these roles requires a deeper understanding of the logical relationships among papers, beyond simple edges in citation networks. The emergence of LLMs with textual reasoning capabilities offers new possibilities for discerning these relationships, but there are two major challenges. First, in practice,"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.09112","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/2410.09112/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":"2410.09112","created_at":"2026-07-05T09:19:34.039366+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.09112v1","created_at":"2026-07-05T09:19:34.039366+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.09112","created_at":"2026-07-05T09:19:34.039366+00:00"},{"alias_kind":"pith_short_12","alias_value":"V46WF5RAWUQI","created_at":"2026-07-05T09:19:34.039366+00:00"},{"alias_kind":"pith_short_16","alias_value":"V46WF5RAWUQIWKKY","created_at":"2026-07-05T09:19:34.039366+00:00"},{"alias_kind":"pith_short_8","alias_value":"V46WF5RA","created_at":"2026-07-05T09:19:34.039366+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.01903","citing_title":"AI4Research: A Survey of Artificial Intelligence for Scientific Research","ref_index":277,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/V46WF5RAWUQIWKKYYFBTW2L2ZV","json":"https://pith.science/pith/V46WF5RAWUQIWKKYYFBTW2L2ZV.json","graph_json":"https://pith.science/api/pith-number/V46WF5RAWUQIWKKYYFBTW2L2ZV/graph.json","events_json":"https://pith.science/api/pith-number/V46WF5RAWUQIWKKYYFBTW2L2ZV/events.json","paper":"https://pith.science/paper/V46WF5RA"},"agent_actions":{"view_html":"https://pith.science/pith/V46WF5RAWUQIWKKYYFBTW2L2ZV","download_json":"https://pith.science/pith/V46WF5RAWUQIWKKYYFBTW2L2ZV.json","view_paper":"https://pith.science/paper/V46WF5RA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.09112&json=true","fetch_graph":"https://pith.science/api/pith-number/V46WF5RAWUQIWKKYYFBTW2L2ZV/graph.json","fetch_events":"https://pith.science/api/pith-number/V46WF5RAWUQIWKKYYFBTW2L2ZV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/V46WF5RAWUQIWKKYYFBTW2L2ZV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/V46WF5RAWUQIWKKYYFBTW2L2ZV/action/storage_attestation","attest_author":"https://pith.science/pith/V46WF5RAWUQIWKKYYFBTW2L2ZV/action/author_attestation","sign_citation":"https://pith.science/pith/V46WF5RAWUQIWKKYYFBTW2L2ZV/action/citation_signature","submit_replication":"https://pith.science/pith/V46WF5RAWUQIWKKYYFBTW2L2ZV/action/replication_record"}},"created_at":"2026-07-05T09:19:34.039366+00:00","updated_at":"2026-07-05T09:19:34.039366+00:00"}