{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:GI6PZPSUPWL373AFZGN2H75OIK","short_pith_number":"pith:GI6PZPSU","schema_version":"1.0","canonical_sha256":"323cfcbe547d97bfec05c99ba3ffae4293cc0739537b86cae6d4a81ed98b4ee4","source":{"kind":"arxiv","id":"2508.04118","version":1},"attestation_state":"computed","paper":{"title":"AgREE: Agentic Reasoning for Knowledge Graph Completion on Emerging Entities","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.AI","authors_text":"Eric Peng, Min Li, Ruochen Zhao, Saloni Potdar, Simone Conia","submitted_at":"2025-08-06T06:34:22Z","abstract_excerpt":"Open-domain Knowledge Graph Completion (KGC) faces significant challenges in an ever-changing world, especially when considering the continual emergence of new entities in daily news. Existing approaches for KGC mainly rely on pretrained language models' parametric knowledge, pre-constructed queries, or single-step retrieval, typically requiring substantial supervision and training data. Even so, they often fail to capture comprehensive and up-to-date information about unpopular and/or emerging entities. To this end, we introduce Agentic Reasoning for Emerging Entities (AgREE), a novel agent-b"},"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":"2508.04118","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-08-06T06:34:22Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"afccbf4c3211931ca7600afe0b33d82f0ed81cbe669eb895076842dad7cf131f","abstract_canon_sha256":"efb81c4ee33cf307bc413fd67d01bb6b18a95db73dae3affc4411559c30930ae"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:49:20.815171Z","signature_b64":"kEBmkyRnFFJnkctZ6xhqHugrhJJX5HQwm8B3QCchkd3CVyobRImeXFcEfMcydl3ak5tYyaqp+ZymfjT40JJMAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"323cfcbe547d97bfec05c99ba3ffae4293cc0739537b86cae6d4a81ed98b4ee4","last_reissued_at":"2026-07-05T11:49:20.814694Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:49:20.814694Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"AgREE: Agentic Reasoning for Knowledge Graph Completion on Emerging Entities","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.AI","authors_text":"Eric Peng, Min Li, Ruochen Zhao, Saloni Potdar, Simone Conia","submitted_at":"2025-08-06T06:34:22Z","abstract_excerpt":"Open-domain Knowledge Graph Completion (KGC) faces significant challenges in an ever-changing world, especially when considering the continual emergence of new entities in daily news. Existing approaches for KGC mainly rely on pretrained language models' parametric knowledge, pre-constructed queries, or single-step retrieval, typically requiring substantial supervision and training data. Even so, they often fail to capture comprehensive and up-to-date information about unpopular and/or emerging entities. To this end, we introduce Agentic Reasoning for Emerging Entities (AgREE), a novel agent-b"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.04118","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/2508.04118/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":"2508.04118","created_at":"2026-07-05T11:49:20.814754+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.04118v1","created_at":"2026-07-05T11:49:20.814754+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.04118","created_at":"2026-07-05T11:49:20.814754+00:00"},{"alias_kind":"pith_short_12","alias_value":"GI6PZPSUPWL3","created_at":"2026-07-05T11:49:20.814754+00:00"},{"alias_kind":"pith_short_16","alias_value":"GI6PZPSUPWL373AF","created_at":"2026-07-05T11:49:20.814754+00:00"},{"alias_kind":"pith_short_8","alias_value":"GI6PZPSU","created_at":"2026-07-05T11:49:20.814754+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.15951","citing_title":"Integrating Graphs, Large Language Models, and Agents: Reasoning and Retrieval","ref_index":96,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GI6PZPSUPWL373AFZGN2H75OIK","json":"https://pith.science/pith/GI6PZPSUPWL373AFZGN2H75OIK.json","graph_json":"https://pith.science/api/pith-number/GI6PZPSUPWL373AFZGN2H75OIK/graph.json","events_json":"https://pith.science/api/pith-number/GI6PZPSUPWL373AFZGN2H75OIK/events.json","paper":"https://pith.science/paper/GI6PZPSU"},"agent_actions":{"view_html":"https://pith.science/pith/GI6PZPSUPWL373AFZGN2H75OIK","download_json":"https://pith.science/pith/GI6PZPSUPWL373AFZGN2H75OIK.json","view_paper":"https://pith.science/paper/GI6PZPSU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.04118&json=true","fetch_graph":"https://pith.science/api/pith-number/GI6PZPSUPWL373AFZGN2H75OIK/graph.json","fetch_events":"https://pith.science/api/pith-number/GI6PZPSUPWL373AFZGN2H75OIK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GI6PZPSUPWL373AFZGN2H75OIK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GI6PZPSUPWL373AFZGN2H75OIK/action/storage_attestation","attest_author":"https://pith.science/pith/GI6PZPSUPWL373AFZGN2H75OIK/action/author_attestation","sign_citation":"https://pith.science/pith/GI6PZPSUPWL373AFZGN2H75OIK/action/citation_signature","submit_replication":"https://pith.science/pith/GI6PZPSUPWL373AFZGN2H75OIK/action/replication_record"}},"created_at":"2026-07-05T11:49:20.814754+00:00","updated_at":"2026-07-05T11:49:20.814754+00:00"}