{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:H5KMET5O73I6PBA364KX6LPORK","short_pith_number":"pith:H5KMET5O","schema_version":"1.0","canonical_sha256":"3f54c24faefed1e7841bf7157f2dee8abe4ed3005cbfcff983b1278ddc5816f5","source":{"kind":"arxiv","id":"2605.28483","version":1},"attestation_state":"computed","paper":{"title":"From Learning Resources to Competencies: LLM-Based Tagging with Evidence and Graph Constraints","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.IR"],"primary_cat":"cs.AI","authors_text":"Bertrand Laforge, Marie-H\\'el\\`ene Abel, Ngoc Luyen Le","submitted_at":"2026-05-27T13:41:10Z","abstract_excerpt":"Linking learning resources to a structured competency framework is key to enabling competency-based search and curriculum analytics in Learning Management Systems (LMS). However, manual tagging is labor-intensive, and fully automatic methods often lack transparency. In this paper, we present an end-to-end alignment pipeline that uses a large language model (LLM) as a constrained, evidence-producing tagger. LMS resources -both instructional content and assessments -are first segmented into meaningful pedagogical fragments. For each fragment, a small set of candidate competencies is retrieved fr"},"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":"2605.28483","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-05-27T13:41:10Z","cross_cats_sorted":["cs.IR"],"title_canon_sha256":"4829279d699639b395f5665f881496853a75abe9c1013fb0df737a3dbde73c08","abstract_canon_sha256":"5ee601733ced8758680dc40520b20c73c3bcd7b43b34f2627f96c91e05d23189"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-28T02:04:54.264139Z","signature_b64":"bkZ6eoUO3m6cWFjzMOzKv5Z7MjpsbfKqKpDjPmtU0pzDmWYwwd/k7HDFj+jcak+y9vnytuccytzAfUoMR9x5CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3f54c24faefed1e7841bf7157f2dee8abe4ed3005cbfcff983b1278ddc5816f5","last_reissued_at":"2026-05-28T02:04:54.263727Z","signature_status":"signed_v1","first_computed_at":"2026-05-28T02:04:54.263727Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"From Learning Resources to Competencies: LLM-Based Tagging with Evidence and Graph Constraints","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.IR"],"primary_cat":"cs.AI","authors_text":"Bertrand Laforge, Marie-H\\'el\\`ene Abel, Ngoc Luyen Le","submitted_at":"2026-05-27T13:41:10Z","abstract_excerpt":"Linking learning resources to a structured competency framework is key to enabling competency-based search and curriculum analytics in Learning Management Systems (LMS). However, manual tagging is labor-intensive, and fully automatic methods often lack transparency. In this paper, we present an end-to-end alignment pipeline that uses a large language model (LLM) as a constrained, evidence-producing tagger. LMS resources -both instructional content and assessments -are first segmented into meaningful pedagogical fragments. For each fragment, a small set of candidate competencies is retrieved fr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.28483","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/2605.28483/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":"2605.28483","created_at":"2026-05-28T02:04:54.263788+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.28483v1","created_at":"2026-05-28T02:04:54.263788+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.28483","created_at":"2026-05-28T02:04:54.263788+00:00"},{"alias_kind":"pith_short_12","alias_value":"H5KMET5O73I6","created_at":"2026-05-28T02:04:54.263788+00:00"},{"alias_kind":"pith_short_16","alias_value":"H5KMET5O73I6PBA3","created_at":"2026-05-28T02:04:54.263788+00:00"},{"alias_kind":"pith_short_8","alias_value":"H5KMET5O","created_at":"2026-05-28T02:04:54.263788+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/H5KMET5O73I6PBA364KX6LPORK","json":"https://pith.science/pith/H5KMET5O73I6PBA364KX6LPORK.json","graph_json":"https://pith.science/api/pith-number/H5KMET5O73I6PBA364KX6LPORK/graph.json","events_json":"https://pith.science/api/pith-number/H5KMET5O73I6PBA364KX6LPORK/events.json","paper":"https://pith.science/paper/H5KMET5O"},"agent_actions":{"view_html":"https://pith.science/pith/H5KMET5O73I6PBA364KX6LPORK","download_json":"https://pith.science/pith/H5KMET5O73I6PBA364KX6LPORK.json","view_paper":"https://pith.science/paper/H5KMET5O","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.28483&json=true","fetch_graph":"https://pith.science/api/pith-number/H5KMET5O73I6PBA364KX6LPORK/graph.json","fetch_events":"https://pith.science/api/pith-number/H5KMET5O73I6PBA364KX6LPORK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/H5KMET5O73I6PBA364KX6LPORK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/H5KMET5O73I6PBA364KX6LPORK/action/storage_attestation","attest_author":"https://pith.science/pith/H5KMET5O73I6PBA364KX6LPORK/action/author_attestation","sign_citation":"https://pith.science/pith/H5KMET5O73I6PBA364KX6LPORK/action/citation_signature","submit_replication":"https://pith.science/pith/H5KMET5O73I6PBA364KX6LPORK/action/replication_record"}},"created_at":"2026-05-28T02:04:54.263788+00:00","updated_at":"2026-05-28T02:04:54.263788+00:00"}