Computer Science > Artificial Intelligence
arXiv:2603.09678 (cs)
[Submitted on 10 Mar 2026]
Title:EsoLang-Bench: Evaluating Genuine Reasoning in Large Language Models via Esoteric Programming Languages
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Abstract:Large language models achieve near-ceiling performance on code generation benchmarks, yet these results increasingly reflect memorization rather than genuine reasoning. We introduce EsoLang-Bench, a benchmark using five esoteric programming languages (Brainfuck, Befunge-98, Whitespace, Unlambda, and Shakespeare) that lack benchmark gaming incentives due to their economic irrationality for pre-training. These languages require the same computational primitives as mainstream programming but have 1,000-100,000x fewer public repositories than Python (based on GitHub search counts). We evaluate five frontier models across five prompting strategies and find a dramatic capability gap: models achieving 85-95% on standard benchmarks score only 0-11% on equivalent esoteric tasks, with 0% accuracy beyond the Easy tier. Few-shot learning and self-reflection fail to improve performance, suggesting these techniques exploit training priors rather than enabling genuine learning. EsoLang-Bench provides the first benchmark designed to mimic human learning by acquiring new languages through documentation, interpreter feedback, and iterative experimentation, measuring transferable reasoning skills resistant to data contamination.
| Comments: | |
| Subjects: | Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Software Engineering (cs.SE) |
| Cite as: | arXiv:2603.09678 [cs.AI] |
| (or arXiv:2603.09678v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2603.09678
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View a PDF of the paper titled EsoLang-Bench: Evaluating Genuine Reasoning in Large Language Models via Esoteric Programming Languages, by Aman Sharma and 1 other authors
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