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About this paper
Aman Sharma, Sushrut Thorat, Paras Chopra
An evaluation of large language model (LLM) coding agents using unfamiliar programming languages revealed significant performance disparities, showing that leading agents spontaneously adopt metaprogramming—generating target language code from a familiar host language—as a critical strategy for successful adaptation.
Key insights
- Evaluating agents on unfamiliar languages exposed a wide performance spread (88.4 percentage points), revealing substantial, otherwise hidden, capability differences among contemporary coding agents.
- Highest-performing agents spontaneously discovered and utilized metaprogramming strategies, writing host-language (e.g., Python) programs to generate target esolang code, which served as a form of agentic generalization.
- Successful adaptation for weaker agents required executable scaffolds (reusable code generators) rather than just textual descriptions of effective strategies, highlighting the importance of concrete tool construction.
Summary by alphaXiv.
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