{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:EXQSKMG5PVSOQHVPU3LOZ4OEHJ","short_pith_number":"pith:EXQSKMG5","schema_version":"1.0","canonical_sha256":"25e12530dd7d64e81eafa6d6ecf1c43a6cca6eed4f17fbe804092f566e58bad9","source":{"kind":"arxiv","id":"2309.02465","version":1},"attestation_state":"computed","paper":{"title":"Towards Foundational AI Models for Additive Manufacturing: Language Models for G-Code Debugging, Manipulation, and Comprehension","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.LG"],"primary_cat":"cs.SE","authors_text":"Adarsh Krishnamurthy, Aditya Balu, Anushrut Jignasu, Baskar Ganapathysubramanian, Chinmay Hegde, Kelly Marshall","submitted_at":"2023-09-04T21:22:28Z","abstract_excerpt":"3D printing or additive manufacturing is a revolutionary technology that enables the creation of physical objects from digital models. However, the quality and accuracy of 3D printing depend on the correctness and efficiency of the G-code, a low-level numerical control programming language that instructs 3D printers how to move and extrude material. Debugging G-code is a challenging task that requires a syntactic and semantic understanding of the G-code format and the geometry of the part to be printed. In this paper, we present the first extensive evaluation of six state-of-the-art foundation"},"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":"2309.02465","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.SE","submitted_at":"2023-09-04T21:22:28Z","cross_cats_sorted":["cs.AI","cs.CL","cs.LG"],"title_canon_sha256":"6c6ae4b79e90e1bb8b2b8bcfc50c1868389239c4ce53138f0ecf451849da3726","abstract_canon_sha256":"e728bddce27ff082951aafc73878f942fd7a448da0063ff2ab1a04201faab9be"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:48:12.633272Z","signature_b64":"9YoKzZ0E+ikU8YMD5U7deflvMV4Y2eeCoIiqN7HNXXCw3xFwb1JsE39FKD7DCWWOff/bgvw43VM5J/U9oO/SDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"25e12530dd7d64e81eafa6d6ecf1c43a6cca6eed4f17fbe804092f566e58bad9","last_reissued_at":"2026-07-05T06:48:12.632804Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:48:12.632804Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards Foundational AI Models for Additive Manufacturing: Language Models for G-Code Debugging, Manipulation, and Comprehension","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.LG"],"primary_cat":"cs.SE","authors_text":"Adarsh Krishnamurthy, Aditya Balu, Anushrut Jignasu, Baskar Ganapathysubramanian, Chinmay Hegde, Kelly Marshall","submitted_at":"2023-09-04T21:22:28Z","abstract_excerpt":"3D printing or additive manufacturing is a revolutionary technology that enables the creation of physical objects from digital models. However, the quality and accuracy of 3D printing depend on the correctness and efficiency of the G-code, a low-level numerical control programming language that instructs 3D printers how to move and extrude material. Debugging G-code is a challenging task that requires a syntactic and semantic understanding of the G-code format and the geometry of the part to be printed. In this paper, we present the first extensive evaluation of six state-of-the-art foundation"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.02465","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/2309.02465/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":"2309.02465","created_at":"2026-07-05T06:48:12.632861+00:00"},{"alias_kind":"arxiv_version","alias_value":"2309.02465v1","created_at":"2026-07-05T06:48:12.632861+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.02465","created_at":"2026-07-05T06:48:12.632861+00:00"},{"alias_kind":"pith_short_12","alias_value":"EXQSKMG5PVSO","created_at":"2026-07-05T06:48:12.632861+00:00"},{"alias_kind":"pith_short_16","alias_value":"EXQSKMG5PVSOQHVP","created_at":"2026-07-05T06:48:12.632861+00:00"},{"alias_kind":"pith_short_8","alias_value":"EXQSKMG5","created_at":"2026-07-05T06:48:12.632861+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2412.09819","citing_title":"FDM-Bench: A Comprehensive Benchmark for Evaluating Large Language Models in Additive Manufacturing Tasks","ref_index":32,"is_internal_anchor":false},{"citing_arxiv_id":"2605.04003","citing_title":"Physics-Grounded Multi-Agent Architecture for Traceable, Risk-Aware Human-AI Decision Support in Manufacturing","ref_index":37,"is_internal_anchor":false},{"citing_arxiv_id":"2604.09889","citing_title":"In-situ process monitoring for defect detection in wire-arc additive manufacturing: an agentic AI approach","ref_index":78,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EXQSKMG5PVSOQHVPU3LOZ4OEHJ","json":"https://pith.science/pith/EXQSKMG5PVSOQHVPU3LOZ4OEHJ.json","graph_json":"https://pith.science/api/pith-number/EXQSKMG5PVSOQHVPU3LOZ4OEHJ/graph.json","events_json":"https://pith.science/api/pith-number/EXQSKMG5PVSOQHVPU3LOZ4OEHJ/events.json","paper":"https://pith.science/paper/EXQSKMG5"},"agent_actions":{"view_html":"https://pith.science/pith/EXQSKMG5PVSOQHVPU3LOZ4OEHJ","download_json":"https://pith.science/pith/EXQSKMG5PVSOQHVPU3LOZ4OEHJ.json","view_paper":"https://pith.science/paper/EXQSKMG5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2309.02465&json=true","fetch_graph":"https://pith.science/api/pith-number/EXQSKMG5PVSOQHVPU3LOZ4OEHJ/graph.json","fetch_events":"https://pith.science/api/pith-number/EXQSKMG5PVSOQHVPU3LOZ4OEHJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EXQSKMG5PVSOQHVPU3LOZ4OEHJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EXQSKMG5PVSOQHVPU3LOZ4OEHJ/action/storage_attestation","attest_author":"https://pith.science/pith/EXQSKMG5PVSOQHVPU3LOZ4OEHJ/action/author_attestation","sign_citation":"https://pith.science/pith/EXQSKMG5PVSOQHVPU3LOZ4OEHJ/action/citation_signature","submit_replication":"https://pith.science/pith/EXQSKMG5PVSOQHVPU3LOZ4OEHJ/action/replication_record"}},"created_at":"2026-07-05T06:48:12.632861+00:00","updated_at":"2026-07-05T06:48:12.632861+00:00"}