{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:WISLYFXVHEXSPUDOKYCDJ6KGER","short_pith_number":"pith:WISLYFXV","schema_version":"1.0","canonical_sha256":"b224bc16f5392f27d06e560434f946246d6da77d32a304afeea575a5b92714c4","source":{"kind":"arxiv","id":"2411.05823","version":2},"attestation_state":"computed","paper":{"title":"FlexCAD: Unified and Versatile Controllable CAD Generation with Fine-tuned Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.GR"],"primary_cat":"cs.CV","authors_text":"Deng Cai, Jiang Bian, Shizhao Sun, Wenxiao Wang, Zhanwei Zhang","submitted_at":"2024-11-05T05:45:26Z","abstract_excerpt":"Recently, there is a growing interest in creating computer-aided design (CAD) models based on user intent, known as controllable CAD generation. Existing work offers limited controllability and needs separate models for different types of control, reducing efficiency and practicality. To achieve controllable generation across all CAD construction hierarchies, such as sketch-extrusion, extrusion, sketch, face, loop and curve, we propose FlexCAD, a unified model by fine-tuning large language models (LLMs). First, to enhance comprehension by LLMs, we represent a CAD model as a structured text by "},"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":"2411.05823","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-11-05T05:45:26Z","cross_cats_sorted":["cs.AI","cs.GR"],"title_canon_sha256":"bcae2794f2ca9016fe101513d04f480177672a60c212cc65700002e3d32847a8","abstract_canon_sha256":"9997fe2d01210179b1f409c491c2fd50daf60527f3533a3150dee47ca9a7904a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:15:21.282770Z","signature_b64":"zidVhwmQxxdMiXJZRfZ5xNqOFPSi1HvMe0I8DSwJdxL7DKn26Dvp2soW/ksNddfmdoy9QckrprjxE6+U19JcAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b224bc16f5392f27d06e560434f946246d6da77d32a304afeea575a5b92714c4","last_reissued_at":"2026-07-05T10:15:21.282239Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:15:21.282239Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"FlexCAD: Unified and Versatile Controllable CAD Generation with Fine-tuned Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.GR"],"primary_cat":"cs.CV","authors_text":"Deng Cai, Jiang Bian, Shizhao Sun, Wenxiao Wang, Zhanwei Zhang","submitted_at":"2024-11-05T05:45:26Z","abstract_excerpt":"Recently, there is a growing interest in creating computer-aided design (CAD) models based on user intent, known as controllable CAD generation. Existing work offers limited controllability and needs separate models for different types of control, reducing efficiency and practicality. To achieve controllable generation across all CAD construction hierarchies, such as sketch-extrusion, extrusion, sketch, face, loop and curve, we propose FlexCAD, a unified model by fine-tuning large language models (LLMs). First, to enhance comprehension by LLMs, we represent a CAD model as a structured text by "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.05823","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/2411.05823/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":"2411.05823","created_at":"2026-07-05T10:15:21.282299+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.05823v2","created_at":"2026-07-05T10:15:21.282299+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.05823","created_at":"2026-07-05T10:15:21.282299+00:00"},{"alias_kind":"pith_short_12","alias_value":"WISLYFXVHEXS","created_at":"2026-07-05T10:15:21.282299+00:00"},{"alias_kind":"pith_short_16","alias_value":"WISLYFXVHEXSPUDO","created_at":"2026-07-05T10:15:21.282299+00:00"},{"alias_kind":"pith_short_8","alias_value":"WISLYFXV","created_at":"2026-07-05T10:15:21.282299+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.19411","citing_title":"BrepForge: Factorized B-rep Synthesis via Wireframe Composition and Boundary-Conditioned Surface Instantiation","ref_index":113,"is_internal_anchor":false},{"citing_arxiv_id":"2604.19773","citing_title":"PR-CAD: Progressive Refinement for Unified Controllable and Faithful Text-to-CAD Generation with Large Language Models","ref_index":21,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WISLYFXVHEXSPUDOKYCDJ6KGER","json":"https://pith.science/pith/WISLYFXVHEXSPUDOKYCDJ6KGER.json","graph_json":"https://pith.science/api/pith-number/WISLYFXVHEXSPUDOKYCDJ6KGER/graph.json","events_json":"https://pith.science/api/pith-number/WISLYFXVHEXSPUDOKYCDJ6KGER/events.json","paper":"https://pith.science/paper/WISLYFXV"},"agent_actions":{"view_html":"https://pith.science/pith/WISLYFXVHEXSPUDOKYCDJ6KGER","download_json":"https://pith.science/pith/WISLYFXVHEXSPUDOKYCDJ6KGER.json","view_paper":"https://pith.science/paper/WISLYFXV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.05823&json=true","fetch_graph":"https://pith.science/api/pith-number/WISLYFXVHEXSPUDOKYCDJ6KGER/graph.json","fetch_events":"https://pith.science/api/pith-number/WISLYFXVHEXSPUDOKYCDJ6KGER/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WISLYFXVHEXSPUDOKYCDJ6KGER/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WISLYFXVHEXSPUDOKYCDJ6KGER/action/storage_attestation","attest_author":"https://pith.science/pith/WISLYFXVHEXSPUDOKYCDJ6KGER/action/author_attestation","sign_citation":"https://pith.science/pith/WISLYFXVHEXSPUDOKYCDJ6KGER/action/citation_signature","submit_replication":"https://pith.science/pith/WISLYFXVHEXSPUDOKYCDJ6KGER/action/replication_record"}},"created_at":"2026-07-05T10:15:21.282299+00:00","updated_at":"2026-07-05T10:15:21.282299+00:00"}