{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:DSNFVXGDBUYWY6PIXZO7JMPG5I","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":"ad05e32a3a735e0cb9c79d1bc3aba923f6e7ab17d07e73301784d51e1492492a","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-02-09T19:53:29Z","title_canon_sha256":"1384534814911cf07ca4f10d498831d11df0f5aeb9cac7f0d5d215f4ad08a888"},"schema_version":"1.0","source":{"id":"2402.06764","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.06764","created_at":"2026-07-05T08:09:12Z"},{"alias_kind":"arxiv_version","alias_value":"2402.06764v3","created_at":"2026-07-05T08:09:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.06764","created_at":"2026-07-05T08:09:12Z"},{"alias_kind":"pith_short_12","alias_value":"DSNFVXGDBUYW","created_at":"2026-07-05T08:09:12Z"},{"alias_kind":"pith_short_16","alias_value":"DSNFVXGDBUYWY6PI","created_at":"2026-07-05T08:09:12Z"},{"alias_kind":"pith_short_8","alias_value":"DSNFVXGD","created_at":"2026-07-05T08:09:12Z"}],"graph_snapshots":[{"event_id":"sha256:f0ad9b68300af71937a499f02dd606ae53c3a858c12f98f6a81d965645b12779","target":"graph","created_at":"2026-07-05T08:09:12Z","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/2402.06764/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Integrating large language models (LLMs) with knowledge graphs derived from domain-specific data represents an important advancement towards more powerful and factual reasoning. As these models grow more capable, it is crucial to enable them to perform multi-step inferences over real-world knowledge graphs while minimizing hallucination. While large language models excel at conversation and text generation, their ability to reason over domain-specialized graphs of interconnected entities remains limited. For example, can we query a LLM to identify the optimal contact in a professional network ","authors_text":"Alejandro Zuniga, Khushbu Agarwal, Michael Henry, Stefan Dernbach, Sutanay Choudhury","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-02-09T19:53:29Z","title":"GLaM: Fine-Tuning Large Language Models for Domain Knowledge Graph Alignment via Neighborhood Partitioning and Generative Subgraph Encoding"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.06764","kind":"arxiv","version":3},"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":"sha256:4ff160c638f3ff5767127bc8692d5d7612cebc50be4930fc6822138487510c03","target":"record","created_at":"2026-07-05T08:09:12Z","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":"ad05e32a3a735e0cb9c79d1bc3aba923f6e7ab17d07e73301784d51e1492492a","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-02-09T19:53:29Z","title_canon_sha256":"1384534814911cf07ca4f10d498831d11df0f5aeb9cac7f0d5d215f4ad08a888"},"schema_version":"1.0","source":{"id":"2402.06764","kind":"arxiv","version":3}},"canonical_sha256":"1c9a5adcc30d316c79e8be5df4b1e6ea32900b83bc808e454e17ce3e0697b227","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1c9a5adcc30d316c79e8be5df4b1e6ea32900b83bc808e454e17ce3e0697b227","first_computed_at":"2026-07-05T08:09:12.996162Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:09:12.996162Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"+TRiIsjP2SxsL6gBdoZdgqw9lg/IEyCa3duk3ybPDiQN1bIs0OZ13k1S2jE2vZ0Tzj4Vn059UZd09FYJ9LZrDA==","signature_status":"signed_v1","signed_at":"2026-07-05T08:09:12.996607Z","signed_message":"canonical_sha256_bytes"},"source_id":"2402.06764","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4ff160c638f3ff5767127bc8692d5d7612cebc50be4930fc6822138487510c03","sha256:f0ad9b68300af71937a499f02dd606ae53c3a858c12f98f6a81d965645b12779"],"state_sha256":"ef4d170f8e9aa540a9fc7b7335bda8e5c01ba8f78152b6abbe5d64e1151d5f5e"}