arXiv:2603. 14501v2 Announce Type: replace-cross Abstract: Large Language Models excel in high-resource programming languages but struggle with low-resource ones.
arXiv:2606. 10933v1 Announce Type: new Abstract: LLM-based coding agents are usually evaluated in familiar software settings: mainstream languages, common libraries, and public repositories.
ORCA is a new benchmark for evaluating large language models on Data Science Code Translation (DSCT), comprising two settings: ORCA-MAIN with 1,600 grounding-level tasks across data querying, manipulation, and deep learning, and ORCA-PROJECT with 200 full-project translation tasks across seven data‑science task types. Each task includes reference translations and test cases to verify functional equivalence, and a multi‑stage quality verification process ensures task correctness. Experiments show that even state‑of‑the‑art LLMs perform poorly on DSCT, with Claude‑Opus‑4.6 achieving only 56.92% success on ORCA‑MAIN and 33.67% on ORCA‑PROJECT, while an intent‑augmented approach improves success rates by 4.80% and 5.33% respectively.
arXiv:2606. 10087v1 Announce Type: cross Abstract: Pre-training on raw code teaches syntax but provides sparse signal for diverse real-world task formats.
arXiv:2606. 08840v1 Announce Type: new Abstract: Code generation models are typically compared using compact execution benchmarks and aggregate pass rates, but such summaries obscure how performance varies across programming languages, problem families, and failure modes.
arXiv:2607. 20456v1 Announce Type: cross Abstract: Large language models excel at code generation for mainstream programming languages but struggle with rare, domain-specific languages such as MiniZinc, a constraint modeling language for combinatorial problems.
arXiv:2512. 03086v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown remarkable capabilities in code translation, yet their performance deteriorates in low-resource programming domains such as Fortran and emerging frameworks like CUDA, where high-quality parallel data are scarce.
arXiv:2609.37143v1 Announce Type: cross Abstract: Modern coding agents can deliver increasingly large repository-level changes, and recent benchmarks reflect this by emphasizing long-horizon tasks wi...
The survey reviews how Large Language Models (LLMs) are being used in High‑Performance Computing (HPC) programming, covering code generation, parallelization, frameworks, evaluation, and broader challenges. It finds that general‑purpose LLMs perform adequately on serial and OpenMP‑style tasks but struggle with distributed MPI workloads, while domain‑specialized models achieve higher accuracy yet are limited in scope and evaluation. The authors argue that LLMs will not replace HPC experts soon but can act as powerful collaborators, provided richer datasets, integration with performance tools, rigorous evaluation, and governance are developed.
arXiv:2607. 14816v1 Announce Type: cross Abstract: Large Language Models (LLMs) perform differently on identical programming tasks when prompted in different natural languages, a phenomenon known as language bias.
arXiv:2505. 03818v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) can achieve strong performance on everyday coding tasks, but they can fail on complex tasks that require non-trivial reasoning about program semantics.
arXiv:2609.23490v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly execute multi-step workflows through tool use and interaction with users and environments. However, curr...
arXiv:2507. 11059v3 Announce Type: replace-cross Abstract: The rapid advancement of Large Language Models (LLMs) in software engineering has revealed critical limitations in existing benchmarks, particularly the widely used SWE-bench dataset.