{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:DFYQAFTNYPDYSMEA2DHRQMVP64","short_pith_number":"pith:DFYQAFTN","schema_version":"1.0","canonical_sha256":"197100166dc3c7893080d0cf1832aff73c09f5fc25d02bca1adabba7812a72ed","source":{"kind":"arxiv","id":"2508.08228","version":1},"attestation_state":"computed","paper":{"title":"LL3M: Large Language 3D Modelers","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.GR","authors_text":"Ari Holtzman, Guan Chen, Itai Lang, Nam Anh Dinh, Rana Hanocka, Sining Lu","submitted_at":"2025-08-11T17:48:02Z","abstract_excerpt":"We present LL3M, a multi-agent system that leverages pretrained large language models (LLMs) to generate 3D assets by writing interpretable Python code in Blender. We break away from the typical generative approach that learns from a collection of 3D data. Instead, we reformulate shape generation as a code-writing task, enabling greater modularity, editability, and integration with artist workflows. Given a text prompt, LL3M coordinates a team of specialized LLM agents to plan, retrieve, write, debug, and refine Blender scripts that generate and edit geometry and appearance. The generated code"},"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":"2508.08228","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.GR","submitted_at":"2025-08-11T17:48:02Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"7508f68df7db7ccdb4f963b2fbd98168dc87bd981a3a123907a2701a158c5eee","abstract_canon_sha256":"5354d8b4bf225dfd6763f564aae803ebe5eb27fc742c40885fcfc7e6a217b8a6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:52:10.065214Z","signature_b64":"4e9mZRiNn0keuBB9UqKbkcDWHE50KYJyu9pbqXCo2LB6k3A+7q6UgMnRZ62YKbOOFKtUfETxp3qNl6QVRH79Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"197100166dc3c7893080d0cf1832aff73c09f5fc25d02bca1adabba7812a72ed","last_reissued_at":"2026-07-05T11:52:10.064747Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:52:10.064747Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LL3M: Large Language 3D Modelers","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.GR","authors_text":"Ari Holtzman, Guan Chen, Itai Lang, Nam Anh Dinh, Rana Hanocka, Sining Lu","submitted_at":"2025-08-11T17:48:02Z","abstract_excerpt":"We present LL3M, a multi-agent system that leverages pretrained large language models (LLMs) to generate 3D assets by writing interpretable Python code in Blender. We break away from the typical generative approach that learns from a collection of 3D data. Instead, we reformulate shape generation as a code-writing task, enabling greater modularity, editability, and integration with artist workflows. Given a text prompt, LL3M coordinates a team of specialized LLM agents to plan, retrieve, write, debug, and refine Blender scripts that generate and edit geometry and appearance. The generated code"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.08228","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/2508.08228/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":"2508.08228","created_at":"2026-07-05T11:52:10.064805+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.08228v1","created_at":"2026-07-05T11:52:10.064805+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.08228","created_at":"2026-07-05T11:52:10.064805+00:00"},{"alias_kind":"pith_short_12","alias_value":"DFYQAFTNYPDY","created_at":"2026-07-05T11:52:10.064805+00:00"},{"alias_kind":"pith_short_16","alias_value":"DFYQAFTNYPDYSMEA","created_at":"2026-07-05T11:52:10.064805+00:00"},{"alias_kind":"pith_short_8","alias_value":"DFYQAFTN","created_at":"2026-07-05T11:52:10.064805+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":12,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.07663","citing_title":"Recursive Self-Improvement in AI: From Bounded Self-Refinement to Autonomous Research Loops","ref_index":33,"is_internal_anchor":true},{"citing_arxiv_id":"2607.01766","citing_title":"SimWorlds: A Multi-Agent System for Dynamic 3D Scene Creation","ref_index":6,"is_internal_anchor":false},{"citing_arxiv_id":"2606.11152","citing_title":"P3D-Bench: Benchmarking MLLMs for Parametric 3D Generation and Structural Reasoning","ref_index":11,"is_internal_anchor":false},{"citing_arxiv_id":"2606.10478","citing_title":"3D-CoS: A New 3D Reconstruction Paradigm Based on VLM Code Synthesis","ref_index":29,"is_internal_anchor":false},{"citing_arxiv_id":"2606.02580","citing_title":"Thinking in Blender: Staged Executable Inverse Graphics with Vision-Language Models","ref_index":30,"is_internal_anchor":false},{"citing_arxiv_id":"2606.27738","citing_title":"HandMade: Spatial Prompting for Generative 3D Creation with Part-Labeled VR Sketches","ref_index":34,"is_internal_anchor":false},{"citing_arxiv_id":"2605.18451","citing_title":"Code-as-Room: Generating 3D Rooms from Top-Down View Images via Agentic Code Synthesis","ref_index":15,"is_internal_anchor":false},{"citing_arxiv_id":"2605.18680","citing_title":"CMAG: Concept-Scaffolded Retrieval for Marketplace Avatar Generation","ref_index":9,"is_internal_anchor":false},{"citing_arxiv_id":"2605.19305","citing_title":"Mat\\'ern Noise for Triangulation-Agnostic Flow Matching on Meshes","ref_index":79,"is_internal_anchor":false},{"citing_arxiv_id":"2604.26943","citing_title":"ProcFunc: Function-Oriented Abstractions for Procedural 3D Generation in Python","ref_index":17,"is_internal_anchor":false},{"citing_arxiv_id":"2604.23774","citing_title":"Prox-E: Fine-Grained 3D Shape Editing via Primitive-Based Abstractions","ref_index":6,"is_internal_anchor":false},{"citing_arxiv_id":"2605.05886","citing_title":"Training-Free Dense Hand Contact Estimation with Multi-Modal Large Language Models","ref_index":26,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DFYQAFTNYPDYSMEA2DHRQMVP64","json":"https://pith.science/pith/DFYQAFTNYPDYSMEA2DHRQMVP64.json","graph_json":"https://pith.science/api/pith-number/DFYQAFTNYPDYSMEA2DHRQMVP64/graph.json","events_json":"https://pith.science/api/pith-number/DFYQAFTNYPDYSMEA2DHRQMVP64/events.json","paper":"https://pith.science/paper/DFYQAFTN"},"agent_actions":{"view_html":"https://pith.science/pith/DFYQAFTNYPDYSMEA2DHRQMVP64","download_json":"https://pith.science/pith/DFYQAFTNYPDYSMEA2DHRQMVP64.json","view_paper":"https://pith.science/paper/DFYQAFTN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.08228&json=true","fetch_graph":"https://pith.science/api/pith-number/DFYQAFTNYPDYSMEA2DHRQMVP64/graph.json","fetch_events":"https://pith.science/api/pith-number/DFYQAFTNYPDYSMEA2DHRQMVP64/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DFYQAFTNYPDYSMEA2DHRQMVP64/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DFYQAFTNYPDYSMEA2DHRQMVP64/action/storage_attestation","attest_author":"https://pith.science/pith/DFYQAFTNYPDYSMEA2DHRQMVP64/action/author_attestation","sign_citation":"https://pith.science/pith/DFYQAFTNYPDYSMEA2DHRQMVP64/action/citation_signature","submit_replication":"https://pith.science/pith/DFYQAFTNYPDYSMEA2DHRQMVP64/action/replication_record"}},"created_at":"2026-07-05T11:52:10.064805+00:00","updated_at":"2026-07-05T11:52:10.064805+00:00"}