{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:5GSMZEXNGTPN2DR6LUHHJTWL73","short_pith_number":"pith:5GSMZEXN","schema_version":"1.0","canonical_sha256":"e9a4cc92ed34dedd0e3e5d0e74cecbfee439eb6f2f21e22afad9391af73744e0","source":{"kind":"arxiv","id":"2607.05750","version":1},"attestation_state":"computed","paper":{"title":"ArtisanCAD: An Industrial-Level CAD Agent with Expert-Grounded Knowledge Distillation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.GR"],"primary_cat":"cs.AI","authors_text":"Guan Wang, Huiyu Yang, Jianghang Gu, Jiao Xiang, Qifeng Wu, Qingsong Yao, Shiyi Chen, Weihao Lv, Wenfa Luo, Xunjin Li, Yuanwei Bin, Yunhan Xu, Yuntian Chen","submitted_at":"2026-07-07T02:11:50Z","abstract_excerpt":"Computer-aided design (CAD) for industrial components requires long-horizon procedural modeling, robust feature dependencies, editable parametric geometry, and production-grade B-Rep execution. Existing text-to-CAD methods have made promising progress in generating CAD programs from natural-language descriptions, but they still struggle when user prompts are ambiguous, underspecified, or only describe high-level design intent. They also rarely exploit expert procedural knowledge naturally available in industrial workflows, such as CATIA operation recordings, macro logs, drawing notes, and engi"},"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":"2607.05750","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-07-07T02:11:50Z","cross_cats_sorted":["cs.GR"],"title_canon_sha256":"054baecd388f3e28600e9f867257d76c7cbb070ed77e079798adc78aa1866acd","abstract_canon_sha256":"8be4a64c9e9c41d1f6dce356e176f749a214e060373b21cf19dc08a5f719e2ee"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-08T01:18:43.857330Z","signature_b64":"mgZADBVs2jFdTgq4U69alTE+MFODMaqzfcmMQHEUg8oE9pste88+3YhBqOlnV2dCWn7wSmve2kcBBgDmmfbmCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e9a4cc92ed34dedd0e3e5d0e74cecbfee439eb6f2f21e22afad9391af73744e0","last_reissued_at":"2026-07-08T01:18:43.856854Z","signature_status":"signed_v1","first_computed_at":"2026-07-08T01:18:43.856854Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ArtisanCAD: An Industrial-Level CAD Agent with Expert-Grounded Knowledge Distillation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.GR"],"primary_cat":"cs.AI","authors_text":"Guan Wang, Huiyu Yang, Jianghang Gu, Jiao Xiang, Qifeng Wu, Qingsong Yao, Shiyi Chen, Weihao Lv, Wenfa Luo, Xunjin Li, Yuanwei Bin, Yunhan Xu, Yuntian Chen","submitted_at":"2026-07-07T02:11:50Z","abstract_excerpt":"Computer-aided design (CAD) for industrial components requires long-horizon procedural modeling, robust feature dependencies, editable parametric geometry, and production-grade B-Rep execution. Existing text-to-CAD methods have made promising progress in generating CAD programs from natural-language descriptions, but they still struggle when user prompts are ambiguous, underspecified, or only describe high-level design intent. They also rarely exploit expert procedural knowledge naturally available in industrial workflows, such as CATIA operation recordings, macro logs, drawing notes, and engi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.05750","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/2607.05750/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":"2607.05750","created_at":"2026-07-08T01:18:43.856923+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.05750v1","created_at":"2026-07-08T01:18:43.856923+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.05750","created_at":"2026-07-08T01:18:43.856923+00:00"},{"alias_kind":"pith_short_12","alias_value":"5GSMZEXNGTPN","created_at":"2026-07-08T01:18:43.856923+00:00"},{"alias_kind":"pith_short_16","alias_value":"5GSMZEXNGTPN2DR6","created_at":"2026-07-08T01:18:43.856923+00:00"},{"alias_kind":"pith_short_8","alias_value":"5GSMZEXN","created_at":"2026-07-08T01:18:43.856923+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/5GSMZEXNGTPN2DR6LUHHJTWL73","json":"https://pith.science/pith/5GSMZEXNGTPN2DR6LUHHJTWL73.json","graph_json":"https://pith.science/api/pith-number/5GSMZEXNGTPN2DR6LUHHJTWL73/graph.json","events_json":"https://pith.science/api/pith-number/5GSMZEXNGTPN2DR6LUHHJTWL73/events.json","paper":"https://pith.science/paper/5GSMZEXN"},"agent_actions":{"view_html":"https://pith.science/pith/5GSMZEXNGTPN2DR6LUHHJTWL73","download_json":"https://pith.science/pith/5GSMZEXNGTPN2DR6LUHHJTWL73.json","view_paper":"https://pith.science/paper/5GSMZEXN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.05750&json=true","fetch_graph":"https://pith.science/api/pith-number/5GSMZEXNGTPN2DR6LUHHJTWL73/graph.json","fetch_events":"https://pith.science/api/pith-number/5GSMZEXNGTPN2DR6LUHHJTWL73/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5GSMZEXNGTPN2DR6LUHHJTWL73/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5GSMZEXNGTPN2DR6LUHHJTWL73/action/storage_attestation","attest_author":"https://pith.science/pith/5GSMZEXNGTPN2DR6LUHHJTWL73/action/author_attestation","sign_citation":"https://pith.science/pith/5GSMZEXNGTPN2DR6LUHHJTWL73/action/citation_signature","submit_replication":"https://pith.science/pith/5GSMZEXNGTPN2DR6LUHHJTWL73/action/replication_record"}},"created_at":"2026-07-08T01:18:43.856923+00:00","updated_at":"2026-07-08T01:18:43.856923+00:00"}