{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:7P4WJKWLCL2SYE3QQSSJCXTN2C","short_pith_number":"pith:7P4WJKWL","schema_version":"1.0","canonical_sha256":"fbf964aacb12f52c137084a4915e6dd082aead78b7e00bc2b3d9b5935d6c1599","source":{"kind":"arxiv","id":"2505.04481","version":2},"attestation_state":"computed","paper":{"title":"CAD-Llama: Leveraging Large Language Models for Computer-Aided Design Parametric 3D Model Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guichun Zhou, Jiahao Li, Weijian Ma, Xiangdong Zhou, Xueyang Li, Yunzhong Lou","submitted_at":"2025-05-07T14:52:02Z","abstract_excerpt":"Recently, Large Language Models (LLMs) have achieved significant success, prompting increased interest in expanding their generative capabilities beyond general text into domain-specific areas. This study investigates the generation of parametric sequences for computer-aided design (CAD) models using LLMs. This endeavor represents an initial step towards creating parametric 3D shapes with LLMs, as CAD model parameters directly correlate with shapes in three-dimensional space. Despite the formidable generative capacities of LLMs, this task remains challenging, as these models neither encounter "},"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":"2505.04481","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-07T14:52:02Z","cross_cats_sorted":[],"title_canon_sha256":"07331adbbf96dd242dbcf6a8765cb595f738547582903649ca824d46933845be","abstract_canon_sha256":"77a1338337cc528461453ec606c7bcc81194f50dbd55feacb0aa24c2c5415b69"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:19:06.959400Z","signature_b64":"Y3ePZr3ukVU6b57kRFq4/2WDMHRDi+C1BQHSvDVv1gI8GFX0Rlh3SOZSNkxxzn7mIwGgfcm3DtSQQOWEmTWFCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fbf964aacb12f52c137084a4915e6dd082aead78b7e00bc2b3d9b5935d6c1599","last_reissued_at":"2026-07-05T11:19:06.958950Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:19:06.958950Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CAD-Llama: Leveraging Large Language Models for Computer-Aided Design Parametric 3D Model Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guichun Zhou, Jiahao Li, Weijian Ma, Xiangdong Zhou, Xueyang Li, Yunzhong Lou","submitted_at":"2025-05-07T14:52:02Z","abstract_excerpt":"Recently, Large Language Models (LLMs) have achieved significant success, prompting increased interest in expanding their generative capabilities beyond general text into domain-specific areas. This study investigates the generation of parametric sequences for computer-aided design (CAD) models using LLMs. This endeavor represents an initial step towards creating parametric 3D shapes with LLMs, as CAD model parameters directly correlate with shapes in three-dimensional space. Despite the formidable generative capacities of LLMs, this task remains challenging, as these models neither encounter "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.04481","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/2505.04481/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":"2505.04481","created_at":"2026-07-05T11:19:06.959005+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.04481v2","created_at":"2026-07-05T11:19:06.959005+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.04481","created_at":"2026-07-05T11:19:06.959005+00:00"},{"alias_kind":"pith_short_12","alias_value":"7P4WJKWLCL2S","created_at":"2026-07-05T11:19:06.959005+00:00"},{"alias_kind":"pith_short_16","alias_value":"7P4WJKWLCL2SYE3Q","created_at":"2026-07-05T11:19:06.959005+00:00"},{"alias_kind":"pith_short_8","alias_value":"7P4WJKWL","created_at":"2026-07-05T11:19:06.959005+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2505.19713","citing_title":"CAD-Coder: Text-to-CAD Generation with Chain-of-Thought and Geometric Reward","ref_index":15,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7P4WJKWLCL2SYE3QQSSJCXTN2C","json":"https://pith.science/pith/7P4WJKWLCL2SYE3QQSSJCXTN2C.json","graph_json":"https://pith.science/api/pith-number/7P4WJKWLCL2SYE3QQSSJCXTN2C/graph.json","events_json":"https://pith.science/api/pith-number/7P4WJKWLCL2SYE3QQSSJCXTN2C/events.json","paper":"https://pith.science/paper/7P4WJKWL"},"agent_actions":{"view_html":"https://pith.science/pith/7P4WJKWLCL2SYE3QQSSJCXTN2C","download_json":"https://pith.science/pith/7P4WJKWLCL2SYE3QQSSJCXTN2C.json","view_paper":"https://pith.science/paper/7P4WJKWL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.04481&json=true","fetch_graph":"https://pith.science/api/pith-number/7P4WJKWLCL2SYE3QQSSJCXTN2C/graph.json","fetch_events":"https://pith.science/api/pith-number/7P4WJKWLCL2SYE3QQSSJCXTN2C/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7P4WJKWLCL2SYE3QQSSJCXTN2C/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7P4WJKWLCL2SYE3QQSSJCXTN2C/action/storage_attestation","attest_author":"https://pith.science/pith/7P4WJKWLCL2SYE3QQSSJCXTN2C/action/author_attestation","sign_citation":"https://pith.science/pith/7P4WJKWLCL2SYE3QQSSJCXTN2C/action/citation_signature","submit_replication":"https://pith.science/pith/7P4WJKWLCL2SYE3QQSSJCXTN2C/action/replication_record"}},"created_at":"2026-07-05T11:19:06.959005+00:00","updated_at":"2026-07-05T11:19:06.959005+00:00"}