{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:46W4BEFKZ7SH7ZNFNC6UTNZCZ4","short_pith_number":"pith:46W4BEFK","schema_version":"1.0","canonical_sha256":"e7adc090aacfe47fe5a568bd49b722cf28215655d01d11e9941776a58ec553fb","source":{"kind":"arxiv","id":"2607.10159","version":1},"attestation_state":"computed","paper":{"title":"UNIT: Unleash Large Language Models Potential for Graph Continual Learning","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Beibei Hu, Changlong He, Jianliang Gao, Qiutong Li, Tairan Huang, Yili Wang, Yiting Shi","submitted_at":"2026-07-11T06:47:26Z","abstract_excerpt":"In real-world multimodal web scenarios, graph-structured data often arrives in a streaming manner, making graph continual learning a crucial paradigm for continuously modeling such evolving structures. However, existing graph continual learning methods still face two fundamental challenges. 1) semantic-structural separation, where the graph-based methods excel at modeling topological relationships but neglect deep semantics. 2) imbalanced knowledge transfer, where existing models fail to effectively leverage general knowledge gained from early tasks to benefit subsequent new tasks. To address "},"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":"2607.10159","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.AI","submitted_at":"2026-07-11T06:47:26Z","cross_cats_sorted":[],"title_canon_sha256":"9137ef404f4842451f318ff36a495b25103875dcd08b891f73380b68e048c927","abstract_canon_sha256":"bf8246ce80f3d31eac68d2af158cde7d938157ae4ae7a8371aa91437fbddc4da"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-14T01:20:28.112072Z","signature_b64":"s2frOdxnP73EOWgNQYLvf0BOXOFV4haLvbzaUvN8BodZbf9zginOkMJJUbOJHwb9tsyJRcqTP82AvZ3Q5jqTCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e7adc090aacfe47fe5a568bd49b722cf28215655d01d11e9941776a58ec553fb","last_reissued_at":"2026-07-14T01:20:28.111237Z","signature_status":"signed_v1","first_computed_at":"2026-07-14T01:20:28.111237Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"UNIT: Unleash Large Language Models Potential for Graph Continual Learning","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Beibei Hu, Changlong He, Jianliang Gao, Qiutong Li, Tairan Huang, Yili Wang, Yiting Shi","submitted_at":"2026-07-11T06:47:26Z","abstract_excerpt":"In real-world multimodal web scenarios, graph-structured data often arrives in a streaming manner, making graph continual learning a crucial paradigm for continuously modeling such evolving structures. However, existing graph continual learning methods still face two fundamental challenges. 1) semantic-structural separation, where the graph-based methods excel at modeling topological relationships but neglect deep semantics. 2) imbalanced knowledge transfer, where existing models fail to effectively leverage general knowledge gained from early tasks to benefit subsequent new tasks. To address "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.10159","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/2607.10159/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":"2607.10159","created_at":"2026-07-14T01:20:28.111670+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.10159v1","created_at":"2026-07-14T01:20:28.111670+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.10159","created_at":"2026-07-14T01:20:28.111670+00:00"},{"alias_kind":"pith_short_12","alias_value":"46W4BEFKZ7SH","created_at":"2026-07-14T01:20:28.111670+00:00"},{"alias_kind":"pith_short_16","alias_value":"46W4BEFKZ7SH7ZNF","created_at":"2026-07-14T01:20:28.111670+00:00"},{"alias_kind":"pith_short_8","alias_value":"46W4BEFK","created_at":"2026-07-14T01:20:28.111670+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/46W4BEFKZ7SH7ZNFNC6UTNZCZ4","json":"https://pith.science/pith/46W4BEFKZ7SH7ZNFNC6UTNZCZ4.json","graph_json":"https://pith.science/api/pith-number/46W4BEFKZ7SH7ZNFNC6UTNZCZ4/graph.json","events_json":"https://pith.science/api/pith-number/46W4BEFKZ7SH7ZNFNC6UTNZCZ4/events.json","paper":"https://pith.science/paper/46W4BEFK"},"agent_actions":{"view_html":"https://pith.science/pith/46W4BEFKZ7SH7ZNFNC6UTNZCZ4","download_json":"https://pith.science/pith/46W4BEFKZ7SH7ZNFNC6UTNZCZ4.json","view_paper":"https://pith.science/paper/46W4BEFK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.10159&json=true","fetch_graph":"https://pith.science/api/pith-number/46W4BEFKZ7SH7ZNFNC6UTNZCZ4/graph.json","fetch_events":"https://pith.science/api/pith-number/46W4BEFKZ7SH7ZNFNC6UTNZCZ4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/46W4BEFKZ7SH7ZNFNC6UTNZCZ4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/46W4BEFKZ7SH7ZNFNC6UTNZCZ4/action/storage_attestation","attest_author":"https://pith.science/pith/46W4BEFKZ7SH7ZNFNC6UTNZCZ4/action/author_attestation","sign_citation":"https://pith.science/pith/46W4BEFKZ7SH7ZNFNC6UTNZCZ4/action/citation_signature","submit_replication":"https://pith.science/pith/46W4BEFKZ7SH7ZNFNC6UTNZCZ4/action/replication_record"}},"created_at":"2026-07-14T01:20:28.111670+00:00","updated_at":"2026-07-14T01:20:28.111670+00:00"}