{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:BMHNU4VCGIZETTLEM4HGKYKATN","short_pith_number":"pith:BMHNU4VC","schema_version":"1.0","canonical_sha256":"0b0eda72a2323249cd64670e6561409b57c5b5315d72d0ee9f97fe131e7de83c","source":{"kind":"arxiv","id":"2506.22303","version":2},"attestation_state":"computed","paper":{"title":"GraphRAG-Induced Dual Knowledge Structure Graphs for Personalized Learning Path Recommendation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Chaobo He, Jiapu Wang, Liangda Fang, Quanlong Guan, Shirui Pan, Weiqi Luo, Xinghe Cheng, Zihan Zhang","submitted_at":"2025-06-27T15:15:42Z","abstract_excerpt":"Learning path recommendation seeks to provide learners with a structured sequence of learning items (\\eg, knowledge concepts or exercises) to optimize their learning efficiency. Despite significant efforts in this area, most existing methods primarily rely on prerequisite relationships, which present two major limitations: 1) Requiring prerequisite relationships between knowledge concepts, which are difficult to obtain due to the cost of expert annotation, hindering the application of current learning path recommendation methods. 2) Relying on a single, sequentially dependent knowledge structu"},"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":"2506.22303","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2025-06-27T15:15:42Z","cross_cats_sorted":[],"title_canon_sha256":"e4b39247ab82c86cff0d571bd8fd8e26d29e96140f7f3b2e553bd377b35daf8a","abstract_canon_sha256":"0c8d600ca64517cbd951ad188660144fc5233a2663faabb15666a7c7ba3bb67e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:49:11.086589Z","signature_b64":"J5laSLh6AC+4nGNisTKicrwFLejKgx2H+5PHB3I5kulUuM/QNDXWi1jK/g1CWBVPgdBoBeY5d9NZt1u6TkEIDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0b0eda72a2323249cd64670e6561409b57c5b5315d72d0ee9f97fe131e7de83c","last_reissued_at":"2026-07-05T11:49:11.086102Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:49:11.086102Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GraphRAG-Induced Dual Knowledge Structure Graphs for Personalized Learning Path Recommendation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Chaobo He, Jiapu Wang, Liangda Fang, Quanlong Guan, Shirui Pan, Weiqi Luo, Xinghe Cheng, Zihan Zhang","submitted_at":"2025-06-27T15:15:42Z","abstract_excerpt":"Learning path recommendation seeks to provide learners with a structured sequence of learning items (\\eg, knowledge concepts or exercises) to optimize their learning efficiency. Despite significant efforts in this area, most existing methods primarily rely on prerequisite relationships, which present two major limitations: 1) Requiring prerequisite relationships between knowledge concepts, which are difficult to obtain due to the cost of expert annotation, hindering the application of current learning path recommendation methods. 2) Relying on a single, sequentially dependent knowledge structu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.22303","kind":"arxiv","version":2},"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/2506.22303/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":"2506.22303","created_at":"2026-07-05T11:49:11.086161+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.22303v2","created_at":"2026-07-05T11:49:11.086161+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.22303","created_at":"2026-07-05T11:49:11.086161+00:00"},{"alias_kind":"pith_short_12","alias_value":"BMHNU4VCGIZE","created_at":"2026-07-05T11:49:11.086161+00:00"},{"alias_kind":"pith_short_16","alias_value":"BMHNU4VCGIZETTLE","created_at":"2026-07-05T11:49:11.086161+00:00"},{"alias_kind":"pith_short_8","alias_value":"BMHNU4VC","created_at":"2026-07-05T11:49:11.086161+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2602.17036","citing_title":"LiveGraph: Active-Structure Neural Re-ranking for Exercise Recommendation","ref_index":2,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BMHNU4VCGIZETTLEM4HGKYKATN","json":"https://pith.science/pith/BMHNU4VCGIZETTLEM4HGKYKATN.json","graph_json":"https://pith.science/api/pith-number/BMHNU4VCGIZETTLEM4HGKYKATN/graph.json","events_json":"https://pith.science/api/pith-number/BMHNU4VCGIZETTLEM4HGKYKATN/events.json","paper":"https://pith.science/paper/BMHNU4VC"},"agent_actions":{"view_html":"https://pith.science/pith/BMHNU4VCGIZETTLEM4HGKYKATN","download_json":"https://pith.science/pith/BMHNU4VCGIZETTLEM4HGKYKATN.json","view_paper":"https://pith.science/paper/BMHNU4VC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.22303&json=true","fetch_graph":"https://pith.science/api/pith-number/BMHNU4VCGIZETTLEM4HGKYKATN/graph.json","fetch_events":"https://pith.science/api/pith-number/BMHNU4VCGIZETTLEM4HGKYKATN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BMHNU4VCGIZETTLEM4HGKYKATN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BMHNU4VCGIZETTLEM4HGKYKATN/action/storage_attestation","attest_author":"https://pith.science/pith/BMHNU4VCGIZETTLEM4HGKYKATN/action/author_attestation","sign_citation":"https://pith.science/pith/BMHNU4VCGIZETTLEM4HGKYKATN/action/citation_signature","submit_replication":"https://pith.science/pith/BMHNU4VCGIZETTLEM4HGKYKATN/action/replication_record"}},"created_at":"2026-07-05T11:49:11.086161+00:00","updated_at":"2026-07-05T11:49:11.086161+00:00"}