{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:TLA2M4JGUN3ADEYSJ7V44ZKZZO","short_pith_number":"pith:TLA2M4JG","schema_version":"1.0","canonical_sha256":"9ac1a67126a3760193124febce6559cb93d3b670e82d69d6b8c76f4f04075553","source":{"kind":"arxiv","id":"2502.16171","version":1},"attestation_state":"computed","paper":{"title":"EPERM: An Evidence Path Enhanced Reasoning Model for Knowledge Graph Question and Answering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Aodi Li, Liansheng Zhuang, Minghong Yao, Shafei Wang, Xiao Long","submitted_at":"2025-02-22T10:05:22Z","abstract_excerpt":"Due to the remarkable reasoning ability, Large language models (LLMs) have demonstrated impressive performance in knowledge graph question answering (KGQA) tasks, which find answers to natural language questions over knowledge graphs (KGs). To alleviate the hallucinations and lack of knowledge issues of LLMs, existing methods often retrieve the question-related information from KGs to enrich the input context. However, most methods focus on retrieving the relevant information while ignoring the importance of different types of knowledge in reasoning, which degrades their performance. To this e"},"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":"2502.16171","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-02-22T10:05:22Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"e812a92cad1ccce0cafd29616313210880a06a78bb25818336533764c5663920","abstract_canon_sha256":"2c24608518779335e452721c4b8db7c9e036c420d2f50d9dfb189bbc8aa7a7ac"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:18:22.721056Z","signature_b64":"2WcBZwcshCWXwieho5CAzjQRU98w5sByiyCnpLQJwoCIRqjgJ5qHEcbMHfFrzbVz6oCotJAFtYI22KJTvyPZAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9ac1a67126a3760193124febce6559cb93d3b670e82d69d6b8c76f4f04075553","last_reissued_at":"2026-07-05T10:18:22.720511Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:18:22.720511Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"EPERM: An Evidence Path Enhanced Reasoning Model for Knowledge Graph Question and Answering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Aodi Li, Liansheng Zhuang, Minghong Yao, Shafei Wang, Xiao Long","submitted_at":"2025-02-22T10:05:22Z","abstract_excerpt":"Due to the remarkable reasoning ability, Large language models (LLMs) have demonstrated impressive performance in knowledge graph question answering (KGQA) tasks, which find answers to natural language questions over knowledge graphs (KGs). To alleviate the hallucinations and lack of knowledge issues of LLMs, existing methods often retrieve the question-related information from KGs to enrich the input context. However, most methods focus on retrieving the relevant information while ignoring the importance of different types of knowledge in reasoning, which degrades their performance. To this e"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.16171","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/2502.16171/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":"2502.16171","created_at":"2026-07-05T10:18:22.720583+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.16171v1","created_at":"2026-07-05T10:18:22.720583+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.16171","created_at":"2026-07-05T10:18:22.720583+00:00"},{"alias_kind":"pith_short_12","alias_value":"TLA2M4JGUN3A","created_at":"2026-07-05T10:18:22.720583+00:00"},{"alias_kind":"pith_short_16","alias_value":"TLA2M4JGUN3ADEYS","created_at":"2026-07-05T10:18:22.720583+00:00"},{"alias_kind":"pith_short_8","alias_value":"TLA2M4JG","created_at":"2026-07-05T10:18:22.720583+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.12476","citing_title":"Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering","ref_index":26,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TLA2M4JGUN3ADEYSJ7V44ZKZZO","json":"https://pith.science/pith/TLA2M4JGUN3ADEYSJ7V44ZKZZO.json","graph_json":"https://pith.science/api/pith-number/TLA2M4JGUN3ADEYSJ7V44ZKZZO/graph.json","events_json":"https://pith.science/api/pith-number/TLA2M4JGUN3ADEYSJ7V44ZKZZO/events.json","paper":"https://pith.science/paper/TLA2M4JG"},"agent_actions":{"view_html":"https://pith.science/pith/TLA2M4JGUN3ADEYSJ7V44ZKZZO","download_json":"https://pith.science/pith/TLA2M4JGUN3ADEYSJ7V44ZKZZO.json","view_paper":"https://pith.science/paper/TLA2M4JG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.16171&json=true","fetch_graph":"https://pith.science/api/pith-number/TLA2M4JGUN3ADEYSJ7V44ZKZZO/graph.json","fetch_events":"https://pith.science/api/pith-number/TLA2M4JGUN3ADEYSJ7V44ZKZZO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TLA2M4JGUN3ADEYSJ7V44ZKZZO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TLA2M4JGUN3ADEYSJ7V44ZKZZO/action/storage_attestation","attest_author":"https://pith.science/pith/TLA2M4JGUN3ADEYSJ7V44ZKZZO/action/author_attestation","sign_citation":"https://pith.science/pith/TLA2M4JGUN3ADEYSJ7V44ZKZZO/action/citation_signature","submit_replication":"https://pith.science/pith/TLA2M4JGUN3ADEYSJ7V44ZKZZO/action/replication_record"}},"created_at":"2026-07-05T10:18:22.720583+00:00","updated_at":"2026-07-05T10:18:22.720583+00:00"}