{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:LZN34KAOQMPOJGMX5CTYCVPEXJ","short_pith_number":"pith:LZN34KAO","canonical_record":{"source":{"id":"2606.01982","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.AI","submitted_at":"2026-06-01T09:44:37Z","cross_cats_sorted":[],"title_canon_sha256":"7fe829198b4cd8498dc88949c71a2a255686ed0e98a692ff22a9f0e3ffc9e42f","abstract_canon_sha256":"67b03637b747eec1887317d082674f39d22fe448d5c9bfaf12d4162110fdce8e"},"schema_version":"1.0"},"canonical_sha256":"5e5bbe280e831ee49997e8a78155e4ba5459860df434a08d24f40a0a690089b8","source":{"kind":"arxiv","id":"2606.01982","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.01982","created_at":"2026-06-02T02:05:02Z"},{"alias_kind":"arxiv_version","alias_value":"2606.01982v1","created_at":"2026-06-02T02:05:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.01982","created_at":"2026-06-02T02:05:02Z"},{"alias_kind":"pith_short_12","alias_value":"LZN34KAOQMPO","created_at":"2026-06-02T02:05:02Z"},{"alias_kind":"pith_short_16","alias_value":"LZN34KAOQMPOJGMX","created_at":"2026-06-02T02:05:02Z"},{"alias_kind":"pith_short_8","alias_value":"LZN34KAO","created_at":"2026-06-02T02:05:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:LZN34KAOQMPOJGMX5CTYCVPEXJ","target":"record","payload":{"canonical_record":{"source":{"id":"2606.01982","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.AI","submitted_at":"2026-06-01T09:44:37Z","cross_cats_sorted":[],"title_canon_sha256":"7fe829198b4cd8498dc88949c71a2a255686ed0e98a692ff22a9f0e3ffc9e42f","abstract_canon_sha256":"67b03637b747eec1887317d082674f39d22fe448d5c9bfaf12d4162110fdce8e"},"schema_version":"1.0"},"canonical_sha256":"5e5bbe280e831ee49997e8a78155e4ba5459860df434a08d24f40a0a690089b8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-02T02:05:02.732214Z","signature_b64":"RY3ppgorf7uwLZd9i9+p+38/PXj0P0444JjfO/L+cw1pDoKYetHVB0HjQX5N66BHBsIpfPL6FiwSD853nznODw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5e5bbe280e831ee49997e8a78155e4ba5459860df434a08d24f40a0a690089b8","last_reissued_at":"2026-06-02T02:05:02.731743Z","signature_status":"signed_v1","first_computed_at":"2026-06-02T02:05:02.731743Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.01982","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-02T02:05:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2IAFBsFsjGuSipM5Sla/K18tsx7iWiIroZZrqiHRvHPbdYcNNmdRWFW6AMDFBriS0jbRvdxmF9OEkebP62cJAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-09T01:11:08.297914Z"},"content_sha256":"e728f5f46dec761170283edaed155fff77df63c2a3e26bd9a19e631e99ca1288","schema_version":"1.0","event_id":"sha256:e728f5f46dec761170283edaed155fff77df63c2a3e26bd9a19e631e99ca1288"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:LZN34KAOQMPOJGMX5CTYCVPEXJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"An NLP-Driven Framework for Curriculum-Labor Market Alignment: Schema-Constrained LLM Extraction, ESCO-Anchored Semantic Matching, and Multi-Dimensional Gap Quantification","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Khaled Shuaib, Mamoun Awad, Mary John, Nazar Zaki, Sherzod Turaev","submitted_at":"2026-06-01T09:44:37Z","abstract_excerpt":"Schema-constrained information extraction from diverse educational and labor-market corpora remains an open challenge in natural language processing because existing pipelines rely primarily on lexical-surface methods that cannot recover implicit competencies, lack grounding in shared taxonomies, and provide no formal measures of extraction reliability or document-level completeness. To address these limitations, this paper proposes a four-stage NLP framework that combines (i) schema-constrained prompting of a two-model frontier-LLM ensemble against a JSON Schema-enforced seven-slot competency"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.01982","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/2606.01982/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-02T02:05:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ox/GlqAJ+zivwTun+3YgxfZSdw3hYuOPNJVsPednH+AKEM6qkSEcuFuoDW7jjDVZOMGXRcaMp8MuRzGHBrNJAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-09T01:11:08.298617Z"},"content_sha256":"541b13a7bb905350765e85e81651ce2c15c2f4bd00624c9885769168747f913c","schema_version":"1.0","event_id":"sha256:541b13a7bb905350765e85e81651ce2c15c2f4bd00624c9885769168747f913c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LZN34KAOQMPOJGMX5CTYCVPEXJ/bundle.json","state_url":"https://pith.science/pith/LZN34KAOQMPOJGMX5CTYCVPEXJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LZN34KAOQMPOJGMX5CTYCVPEXJ/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-09T01:11:08Z","links":{"resolver":"https://pith.science/pith/LZN34KAOQMPOJGMX5CTYCVPEXJ","bundle":"https://pith.science/pith/LZN34KAOQMPOJGMX5CTYCVPEXJ/bundle.json","state":"https://pith.science/pith/LZN34KAOQMPOJGMX5CTYCVPEXJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LZN34KAOQMPOJGMX5CTYCVPEXJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:LZN34KAOQMPOJGMX5CTYCVPEXJ","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"67b03637b747eec1887317d082674f39d22fe448d5c9bfaf12d4162110fdce8e","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.AI","submitted_at":"2026-06-01T09:44:37Z","title_canon_sha256":"7fe829198b4cd8498dc88949c71a2a255686ed0e98a692ff22a9f0e3ffc9e42f"},"schema_version":"1.0","source":{"id":"2606.01982","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.01982","created_at":"2026-06-02T02:05:02Z"},{"alias_kind":"arxiv_version","alias_value":"2606.01982v1","created_at":"2026-06-02T02:05:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.01982","created_at":"2026-06-02T02:05:02Z"},{"alias_kind":"pith_short_12","alias_value":"LZN34KAOQMPO","created_at":"2026-06-02T02:05:02Z"},{"alias_kind":"pith_short_16","alias_value":"LZN34KAOQMPOJGMX","created_at":"2026-06-02T02:05:02Z"},{"alias_kind":"pith_short_8","alias_value":"LZN34KAO","created_at":"2026-06-02T02:05:02Z"}],"graph_snapshots":[{"event_id":"sha256:541b13a7bb905350765e85e81651ce2c15c2f4bd00624c9885769168747f913c","target":"graph","created_at":"2026-06-02T02:05:02Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2606.01982/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Schema-constrained information extraction from diverse educational and labor-market corpora remains an open challenge in natural language processing because existing pipelines rely primarily on lexical-surface methods that cannot recover implicit competencies, lack grounding in shared taxonomies, and provide no formal measures of extraction reliability or document-level completeness. To address these limitations, this paper proposes a four-stage NLP framework that combines (i) schema-constrained prompting of a two-model frontier-LLM ensemble against a JSON Schema-enforced seven-slot competency","authors_text":"Khaled Shuaib, Mamoun Awad, Mary John, Nazar Zaki, Sherzod Turaev","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.AI","submitted_at":"2026-06-01T09:44:37Z","title":"An NLP-Driven Framework for Curriculum-Labor Market Alignment: Schema-Constrained LLM Extraction, ESCO-Anchored Semantic Matching, and Multi-Dimensional Gap Quantification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.01982","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:e728f5f46dec761170283edaed155fff77df63c2a3e26bd9a19e631e99ca1288","target":"record","created_at":"2026-06-02T02:05:02Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"67b03637b747eec1887317d082674f39d22fe448d5c9bfaf12d4162110fdce8e","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.AI","submitted_at":"2026-06-01T09:44:37Z","title_canon_sha256":"7fe829198b4cd8498dc88949c71a2a255686ed0e98a692ff22a9f0e3ffc9e42f"},"schema_version":"1.0","source":{"id":"2606.01982","kind":"arxiv","version":1}},"canonical_sha256":"5e5bbe280e831ee49997e8a78155e4ba5459860df434a08d24f40a0a690089b8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5e5bbe280e831ee49997e8a78155e4ba5459860df434a08d24f40a0a690089b8","first_computed_at":"2026-06-02T02:05:02.731743Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-02T02:05:02.731743Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"RY3ppgorf7uwLZd9i9+p+38/PXj0P0444JjfO/L+cw1pDoKYetHVB0HjQX5N66BHBsIpfPL6FiwSD853nznODw==","signature_status":"signed_v1","signed_at":"2026-06-02T02:05:02.732214Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.01982","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e728f5f46dec761170283edaed155fff77df63c2a3e26bd9a19e631e99ca1288","sha256:541b13a7bb905350765e85e81651ce2c15c2f4bd00624c9885769168747f913c"],"state_sha256":"b89c14040df0852fc4a7bcb83bee53d86500193114d0ab1536e4dc1dc7edc17c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"U0JjMiA8/pgbbSoXfO8XQn4YYTAolXY8mAPWP34UujKRyjgbi43Qs/82j3ivbmjqz14b/t8rsXna4OwrfULiAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-09T01:11:08.301147Z","bundle_sha256":"ff82e776673ac3a642b73fb794efe2185a137405884b3c8bb4434c96f3b860f1"}}