{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:3C5RZIWOO35TCCYE6QMUSCQ3W5","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":"210099ea180e8a191fc42ac03ad142f72aaba990a555d082ed3fbe4b1ad7505f","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-08T05:49:58Z","title_canon_sha256":"c4b7e467dd1f7211d9959c99201886a65791bdd41763d98756e656c655b423d8"},"schema_version":"1.0","source":{"id":"2412.06849","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.06849","created_at":"2026-07-05T09:46:58Z"},{"alias_kind":"arxiv_version","alias_value":"2412.06849v1","created_at":"2026-07-05T09:46:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.06849","created_at":"2026-07-05T09:46:58Z"},{"alias_kind":"pith_short_12","alias_value":"3C5RZIWOO35T","created_at":"2026-07-05T09:46:58Z"},{"alias_kind":"pith_short_16","alias_value":"3C5RZIWOO35TCCYE","created_at":"2026-07-05T09:46:58Z"},{"alias_kind":"pith_short_8","alias_value":"3C5RZIWO","created_at":"2026-07-05T09:46:58Z"}],"graph_snapshots":[{"event_id":"sha256:678e6b14f5a14949d8a7ee476776a5cd3bd015e7f26f8077f1437f1bc8f18022","target":"graph","created_at":"2026-07-05T09:46:58Z","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/2412.06849/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent research on integrating Large Language Models (LLMs) with Graph Neural Networks (GNNs) typically follows two approaches: LLM-centered models, which convert graph data into tokens for LLM processing, and GNN-centered models, which use LLMs to encode text features into node and edge representations for GNN input. LLM-centered models often struggle to capture graph structures effectively, while GNN-centered models compress variable-length textual data into fixed-size vectors, limiting their ability to understand complex semantics. Additionally, GNN-centered approaches require converting ta","authors_text":"Haotong Yang, Muhan Zhang, Qian Tao, Shuxian Hu, Xiyuan Wang, Zhouchen Lin","cross_cats":["cs.AI","cs.CL"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-08T05:49:58Z","title":"GL-Fusion: Rethinking the Combination of Graph Neural Network and Large Language model"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.06849","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:b7346cb8d46c45be223a7296814b32efc62a393aa19271ba23a0c0902b3a2ea2","target":"record","created_at":"2026-07-05T09:46:58Z","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":"210099ea180e8a191fc42ac03ad142f72aaba990a555d082ed3fbe4b1ad7505f","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-08T05:49:58Z","title_canon_sha256":"c4b7e467dd1f7211d9959c99201886a65791bdd41763d98756e656c655b423d8"},"schema_version":"1.0","source":{"id":"2412.06849","kind":"arxiv","version":1}},"canonical_sha256":"d8bb1ca2ce76fb310b04f419490a1bb75d09fd4178b34d8cc27254435e5d3c64","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d8bb1ca2ce76fb310b04f419490a1bb75d09fd4178b34d8cc27254435e5d3c64","first_computed_at":"2026-07-05T09:46:58.352237Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:46:58.352237Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"x0HGDRUy+ZQXjrSmf6Ish8lcKoFWyxM4NJhKA6EwUuYmBmsl6T/Bqu5qCOqoo8OxrOG7MlWBRTo2B/2kCGpWBg==","signature_status":"signed_v1","signed_at":"2026-07-05T09:46:58.353214Z","signed_message":"canonical_sha256_bytes"},"source_id":"2412.06849","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b7346cb8d46c45be223a7296814b32efc62a393aa19271ba23a0c0902b3a2ea2","sha256:678e6b14f5a14949d8a7ee476776a5cd3bd015e7f26f8077f1437f1bc8f18022"],"state_sha256":"e12230d6608269ecaff06617588afe8ad8135f46c181483d7f5287f5f3ad68e1"}