{"paper":{"title":"Customizing an LLM for Enterprise Software Engineering","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Aditya Kini, Aditya Pandey, Ahmed Omran, Alexander Fr\\\"ommgen Ranganathan, Amy Hua, Anita Gergely, Danny Tarlow, Franjo Ivan\\v{c}i\\'c, Gufeng Zhang, Marc Brockschmidt, Martin Sevenich, Milad Hashemi, Parthasarathy Ranganathan, Petros Maniatis, Saksham Thakur, Satish Chandra, Vincent Nguyen, Zaheer Abbas","submitted_at":"2026-05-15T18:11:55Z","abstract_excerpt":"Enterprise software development is a continuous evolutionary process, characterized by incremental additions, architectural revisions, production deployments and rigorous maintenance. These activities generate valuable data that modern LLMs could be finetuned on, to unlock additional tool possibilities for enterprise software engineering. While frontier LLMs are already very capable, this form of customization offers a compelling path for enterprise-specific optimization.\n  We introduce Gemini for Google (GfG)}, an adaptation of Gemini specialized for Google's internal software engineering eco"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.16517","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/2605.16517/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"ai_meta_artifact","ran_at":"2026-05-19T19:33:23.084121Z","status":"skipped","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T19:21:56.954421Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"51ccf0b373ffd7e02c3ee6812b413a690788a019ee9f0f772a7f4ae4375c8dd0"},"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"}