{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:XWC3PBKS4FHQTTHVJ77Y4GC6R2","short_pith_number":"pith:XWC3PBKS","schema_version":"1.0","canonical_sha256":"bd85b78552e14f09ccf54fff8e185e8e8e5c2f73c5a368ebae67262d55df1739","source":{"kind":"arxiv","id":"2412.17029","version":1},"attestation_state":"computed","paper":{"title":"GraphAgent: Agentic Graph Language Assistant","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Chao Huang, Jiabin Tang, Lianghao Xia, Xingchen Zou, Yuhao Yang, Yuxuan Liang","submitted_at":"2024-12-22T14:13:32Z","abstract_excerpt":"Real-world data is represented in both structured (e.g., graph connections) and unstructured (e.g., textual, visual information) formats, encompassing complex relationships that include explicit links (such as social connections and user behaviors) and implicit interdependencies among semantic entities, often illustrated through knowledge graphs. In this work, we propose GraphAgent, an automated agent pipeline that addresses both explicit graph dependencies and implicit graph-enhanced semantic inter-dependencies, aligning with practical data scenarios for predictive tasks (e.g., node classific"},"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":"2412.17029","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2024-12-22T14:13:32Z","cross_cats_sorted":[],"title_canon_sha256":"5505efbe2018c35bb3398003e384a73399c9b163ed3f33c6080e4976d4516033","abstract_canon_sha256":"6f3e6f1ff5ed915c8b4a894cae602cb0cc196da0c25814db2d6bffa0a006432c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:53:16.803455Z","signature_b64":"JtmRZBeGWkd4ES/Y+Uu7dv0M3ZeemtI5PRkWNc/k+THbBhbYzOk0Go4VGk3P9nHNAWC956Bdq1gvKagWwuclAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bd85b78552e14f09ccf54fff8e185e8e8e5c2f73c5a368ebae67262d55df1739","last_reissued_at":"2026-07-05T09:53:16.803070Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:53:16.803070Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GraphAgent: Agentic Graph Language Assistant","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Chao Huang, Jiabin Tang, Lianghao Xia, Xingchen Zou, Yuhao Yang, Yuxuan Liang","submitted_at":"2024-12-22T14:13:32Z","abstract_excerpt":"Real-world data is represented in both structured (e.g., graph connections) and unstructured (e.g., textual, visual information) formats, encompassing complex relationships that include explicit links (such as social connections and user behaviors) and implicit interdependencies among semantic entities, often illustrated through knowledge graphs. In this work, we propose GraphAgent, an automated agent pipeline that addresses both explicit graph dependencies and implicit graph-enhanced semantic inter-dependencies, aligning with practical data scenarios for predictive tasks (e.g., node classific"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.17029","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/2412.17029/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":"2412.17029","created_at":"2026-07-05T09:53:16.803120+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.17029v1","created_at":"2026-07-05T09:53:16.803120+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.17029","created_at":"2026-07-05T09:53:16.803120+00:00"},{"alias_kind":"pith_short_12","alias_value":"XWC3PBKS4FHQ","created_at":"2026-07-05T09:53:16.803120+00:00"},{"alias_kind":"pith_short_16","alias_value":"XWC3PBKS4FHQTTHV","created_at":"2026-07-05T09:53:16.803120+00:00"},{"alias_kind":"pith_short_8","alias_value":"XWC3PBKS","created_at":"2026-07-05T09:53:16.803120+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.18019","citing_title":"Graphs Meet AI Agents: Taxonomy, Progress, and Future Opportunities","ref_index":139,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XWC3PBKS4FHQTTHVJ77Y4GC6R2","json":"https://pith.science/pith/XWC3PBKS4FHQTTHVJ77Y4GC6R2.json","graph_json":"https://pith.science/api/pith-number/XWC3PBKS4FHQTTHVJ77Y4GC6R2/graph.json","events_json":"https://pith.science/api/pith-number/XWC3PBKS4FHQTTHVJ77Y4GC6R2/events.json","paper":"https://pith.science/paper/XWC3PBKS"},"agent_actions":{"view_html":"https://pith.science/pith/XWC3PBKS4FHQTTHVJ77Y4GC6R2","download_json":"https://pith.science/pith/XWC3PBKS4FHQTTHVJ77Y4GC6R2.json","view_paper":"https://pith.science/paper/XWC3PBKS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.17029&json=true","fetch_graph":"https://pith.science/api/pith-number/XWC3PBKS4FHQTTHVJ77Y4GC6R2/graph.json","fetch_events":"https://pith.science/api/pith-number/XWC3PBKS4FHQTTHVJ77Y4GC6R2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XWC3PBKS4FHQTTHVJ77Y4GC6R2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XWC3PBKS4FHQTTHVJ77Y4GC6R2/action/storage_attestation","attest_author":"https://pith.science/pith/XWC3PBKS4FHQTTHVJ77Y4GC6R2/action/author_attestation","sign_citation":"https://pith.science/pith/XWC3PBKS4FHQTTHVJ77Y4GC6R2/action/citation_signature","submit_replication":"https://pith.science/pith/XWC3PBKS4FHQTTHVJ77Y4GC6R2/action/replication_record"}},"created_at":"2026-07-05T09:53:16.803120+00:00","updated_at":"2026-07-05T09:53:16.803120+00:00"}