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RKVarma AI Universe ✦ R&D Intelligence · company 06 of 50

06 OpenAI

AI hardware and software R&D at OpenAI, from every daily edition, newest first · Curated by Dr.P.R.K.Varma

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21 October 2026

Full write-up from 21 October 2026

1. Company and research division. OpenAI (research and applied teams).

2. Date of publication / announcement. 'Advancing coding in low-resource programming languages', 19 October 2026 (widely discussed on 20 October). ChatGPT release notes, 20 October 2026: GPT-6 Pro parallel agents; GPT-Live-1.1 voices.

3. Research title and field. Low-resource programming-language capability (COBOL, Fortran, OCaml, Visual Basic) and two new evaluations: MultiLangBench and a COBOL migration evaluation. Field: code generation, evaluation.

4. Research background. Legacy languages have little public training data but run critical infrastructure. OpenAI cites COBOL's role in more than 40% of online banking systems and 95% of ATM transactions.

5. Scientific innovation. Long-horizon evaluation of building a SQLite-compatible engine from scratch in 20 languages, using database files as a language-neutral interface, together with a behaviour-preserving migration test requiring byte-for-byte parity on hidden tests.

6. Methodology. MultiLangBench adapts tests from SQLite's own suite. The candidate program receives a database and a SQL query and must output the modified database, which is compared against reference SQLite. It is reported as test pass rate and API cost. The COBOL migration evaluation targets C++, Java, Kotlin, Python and Rust; success requires all hidden tests to pass.

7. Results. At maximum effort, GPT-6 Astra averaged 87.4% (versus 26.8% for GPT-5.6 Sol). By language: COBOL 10.9%→72.1%, OCaml 25.6%→85.8%, Fortran 21.4%→83.5%, Visual Basic 27.5%→89.4%; Astra scores above 83% in 19 of 20 languages. GPT-6 Luna reached 40.3% at an estimated $0.77 per sample versus $3.48 (about 78% lower). COBOL migration: GPT-6.1 Sol scored 86.6% at low effort (versus 5.4%, at 61% of the cost) and 97.9% at higher effort.

8. Scientific significance. Strong evidence that frontier models now generalise to low-resource languages on long-horizon tasks. The open-sourced test suite allows external replication.

9. Technical limitations. These are company-run evaluations of its own models. The migration evaluation is near saturation. Real legacy systems involve JCL, CICS, copybooks and undocumented business rules not captured here. OpenAI itself notes that expert review is still required.

10. Technology readiness and evidence type. Commercial (models available via API). Evidence type: company evaluation (marketing claim, with the benchmark released for reproduction).

11. Competitive comparison. No third-party MultiLangBench results for competitors were available at the time of writing. Other vendors' COBOL tools (for example IBM watsonx Code Assistant for Z) report different metrics, so the figures are not directly comparable.

12. Future research (1–5 years) — FORECAST. FORECAST (1–5 years): independent leaderboards on MultiLangBench; AI-assisted mainframe migration programmes in banking and government; regulators asking for behaviour-equivalence evidence.

13. Original references.

In the 21 October 2026 report →