{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:6AWPNAXHJV5G7PDIODUND3WX23","short_pith_number":"pith:6AWPNAXH","canonical_record":{"source":{"id":"2501.14224","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-01-24T04:06:50Z","cross_cats_sorted":["cs.DB","cs.LG"],"title_canon_sha256":"84172042d56f33df198ce71ef642c19974896679755120322fe73f034263ba36","abstract_canon_sha256":"95143757606829a9fd74dbe06f368873fb94ec521b2fcaa30d413d02029992a8"},"schema_version":"1.0"},"canonical_sha256":"f02cf682e74d7a6fbc6870e8d1eed7d6ce8e639163526054fe1e588b43dc489c","source":{"kind":"arxiv","id":"2501.14224","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.14224","created_at":"2026-07-05T10:04:47Z"},{"alias_kind":"arxiv_version","alias_value":"2501.14224v1","created_at":"2026-07-05T10:04:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.14224","created_at":"2026-07-05T10:04:47Z"},{"alias_kind":"pith_short_12","alias_value":"6AWPNAXHJV5G","created_at":"2026-07-05T10:04:47Z"},{"alias_kind":"pith_short_16","alias_value":"6AWPNAXHJV5G7PDI","created_at":"2026-07-05T10:04:47Z"},{"alias_kind":"pith_short_8","alias_value":"6AWPNAXH","created_at":"2026-07-05T10:04:47Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:6AWPNAXHJV5G7PDIODUND3WX23","target":"record","payload":{"canonical_record":{"source":{"id":"2501.14224","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-01-24T04:06:50Z","cross_cats_sorted":["cs.DB","cs.LG"],"title_canon_sha256":"84172042d56f33df198ce71ef642c19974896679755120322fe73f034263ba36","abstract_canon_sha256":"95143757606829a9fd74dbe06f368873fb94ec521b2fcaa30d413d02029992a8"},"schema_version":"1.0"},"canonical_sha256":"f02cf682e74d7a6fbc6870e8d1eed7d6ce8e639163526054fe1e588b43dc489c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:04:47.507520Z","signature_b64":"BYt4trtZyF/SwjFVpHuDRr2MMF+BTMwm5I9wzaKJe7ie7Z5eFC7qVJI04Ape0IBDTagze5GoHZiXyLg+2ShrDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f02cf682e74d7a6fbc6870e8d1eed7d6ce8e639163526054fe1e588b43dc489c","last_reissued_at":"2026-07-05T10:04:47.506973Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:04:47.506973Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2501.14224","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:04:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EmaexpfovjIR7TFuYm0WD6bw0lLMvrePQ7Qe71ezTPGKTFlymV8Asz1UcNSDGO2HUO0ptM1Otc1rkXzMnicDDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T10:37:23.714749Z"},"content_sha256":"397a75a8f236ffb9b20b2926ee89082e501550397ccdbff0613436bff21b423e","schema_version":"1.0","event_id":"sha256:397a75a8f236ffb9b20b2926ee89082e501550397ccdbff0613436bff21b423e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:6AWPNAXHJV5G7PDIODUND3WX23","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Top Ten Challenges Towards Agentic Neural Graph Databases","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.DB","cs.LG"],"primary_cat":"cs.AI","authors_text":"Binhang Yuan, Hang Yin, Hong Ting Tsang, Jiaxin Bai, Jiayang Cheng, Lei Chen, Lixin Fan, Qi Hu, Tianshi Zheng, Wei Wang, Weizhi Fei, Xiaofang Zhou, Yangqiu Song, Yisen Gao, Yufei Li, Yukun Zhou, Zheye Deng, Zhongwei Xie, Zihao Wang","submitted_at":"2025-01-24T04:06:50Z","abstract_excerpt":"Graph databases (GDBs) like Neo4j and TigerGraph excel at handling interconnected data but lack advanced inference capabilities. Neural Graph Databases (NGDBs) address this by integrating Graph Neural Networks (GNNs) for predictive analysis and reasoning over incomplete or noisy data. However, NGDBs rely on predefined queries and lack autonomy and adaptability. This paper introduces Agentic Neural Graph Databases (Agentic NGDBs), which extend NGDBs with three core functionalities: autonomous query construction, neural query execution, and continuous learning. We identify ten key challenges in "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.14224","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/2501.14224/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:04:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"O/mUzLbZLGzfeIXvB7HtZ4XkR3i91wjSmpMYWFn82UhcV7SCNqKf71C/xNzJ9Dtdmnxnxv/CxodiKi/YFfuPBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T10:37:23.715951Z"},"content_sha256":"63d000c15f59bb9e7f9de09b481c0d6597a112da51d65adb68a00ce889e08f61","schema_version":"1.0","event_id":"sha256:63d000c15f59bb9e7f9de09b481c0d6597a112da51d65adb68a00ce889e08f61"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6AWPNAXHJV5G7PDIODUND3WX23/bundle.json","state_url":"https://pith.science/pith/6AWPNAXHJV5G7PDIODUND3WX23/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6AWPNAXHJV5G7PDIODUND3WX23/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-07T10:37:23Z","links":{"resolver":"https://pith.science/pith/6AWPNAXHJV5G7PDIODUND3WX23","bundle":"https://pith.science/pith/6AWPNAXHJV5G7PDIODUND3WX23/bundle.json","state":"https://pith.science/pith/6AWPNAXHJV5G7PDIODUND3WX23/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6AWPNAXHJV5G7PDIODUND3WX23/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:6AWPNAXHJV5G7PDIODUND3WX23","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":"95143757606829a9fd74dbe06f368873fb94ec521b2fcaa30d413d02029992a8","cross_cats_sorted":["cs.DB","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-01-24T04:06:50Z","title_canon_sha256":"84172042d56f33df198ce71ef642c19974896679755120322fe73f034263ba36"},"schema_version":"1.0","source":{"id":"2501.14224","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.14224","created_at":"2026-07-05T10:04:47Z"},{"alias_kind":"arxiv_version","alias_value":"2501.14224v1","created_at":"2026-07-05T10:04:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.14224","created_at":"2026-07-05T10:04:47Z"},{"alias_kind":"pith_short_12","alias_value":"6AWPNAXHJV5G","created_at":"2026-07-05T10:04:47Z"},{"alias_kind":"pith_short_16","alias_value":"6AWPNAXHJV5G7PDI","created_at":"2026-07-05T10:04:47Z"},{"alias_kind":"pith_short_8","alias_value":"6AWPNAXH","created_at":"2026-07-05T10:04:47Z"}],"graph_snapshots":[{"event_id":"sha256:63d000c15f59bb9e7f9de09b481c0d6597a112da51d65adb68a00ce889e08f61","target":"graph","created_at":"2026-07-05T10:04:47Z","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/2501.14224/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Graph databases (GDBs) like Neo4j and TigerGraph excel at handling interconnected data but lack advanced inference capabilities. Neural Graph Databases (NGDBs) address this by integrating Graph Neural Networks (GNNs) for predictive analysis and reasoning over incomplete or noisy data. However, NGDBs rely on predefined queries and lack autonomy and adaptability. This paper introduces Agentic Neural Graph Databases (Agentic NGDBs), which extend NGDBs with three core functionalities: autonomous query construction, neural query execution, and continuous learning. We identify ten key challenges in ","authors_text":"Binhang Yuan, Hang Yin, Hong Ting Tsang, Jiaxin Bai, Jiayang Cheng, Lei Chen, Lixin Fan, Qi Hu, Tianshi Zheng, Wei Wang, Weizhi Fei, Xiaofang Zhou, Yangqiu Song, Yisen Gao, Yufei Li, Yukun Zhou, Zheye Deng, Zhongwei Xie, Zihao Wang","cross_cats":["cs.DB","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-01-24T04:06:50Z","title":"Top Ten Challenges Towards Agentic Neural Graph Databases"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.14224","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":"sha256:397a75a8f236ffb9b20b2926ee89082e501550397ccdbff0613436bff21b423e","target":"record","created_at":"2026-07-05T10:04:47Z","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":"95143757606829a9fd74dbe06f368873fb94ec521b2fcaa30d413d02029992a8","cross_cats_sorted":["cs.DB","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-01-24T04:06:50Z","title_canon_sha256":"84172042d56f33df198ce71ef642c19974896679755120322fe73f034263ba36"},"schema_version":"1.0","source":{"id":"2501.14224","kind":"arxiv","version":1}},"canonical_sha256":"f02cf682e74d7a6fbc6870e8d1eed7d6ce8e639163526054fe1e588b43dc489c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f02cf682e74d7a6fbc6870e8d1eed7d6ce8e639163526054fe1e588b43dc489c","first_computed_at":"2026-07-05T10:04:47.506973Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:04:47.506973Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"BYt4trtZyF/SwjFVpHuDRr2MMF+BTMwm5I9wzaKJe7ie7Z5eFC7qVJI04Ape0IBDTagze5GoHZiXyLg+2ShrDw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:04:47.507520Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.14224","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:397a75a8f236ffb9b20b2926ee89082e501550397ccdbff0613436bff21b423e","sha256:63d000c15f59bb9e7f9de09b481c0d6597a112da51d65adb68a00ce889e08f61"],"state_sha256":"acfae5379ce76e6be5acf0eca8536bca3ed29fddc63f0ea606ab1a807fc9fa7d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"enrJMJaa6oX3EfvCzOopW305GUUkSfqui+RDHusbWWkhRFaN/SUEMHIlh9ZyJB2zVvdYUef9oQRVpDBL5gtlDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T10:37:23.725664Z","bundle_sha256":"338c80f6f2d09424d99f44719e77c52add860a5e00a2f0fa2caf11756a4f1005"}}