arXiv Computation and Language
LangSelect: Cost-Aware Target-Language Routing for LLM Code Generation
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Frontier Coding Agents Use Metaprogramming to Adapt to Unfamiliar Programming Languages
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.
arXiv AI
Jun 9
Beyond Pass Rate: A Multilingual, Execution-Grounded Evaluation of Open Code LLMs
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.
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Route-Align-Verify for Functional Correctness in Code Generation
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Can LLMs Reason About Runtime Behavior? A Repository-Level Dynamic Benchmark
The paper introduces SWE-Flux, a repository‑level benchmark designed to test large language models’ ability to reason about runtime behavior. It contains 480 execution‑grounded instances from 12 real Python repositories, with gold answers automatically harvested from instrumented test executions. Evaluation of five LLMs shows the task remains difficult, with the best model achieving only 37% accuracy, and the benchmark can generate challenging variants through input perturbation.
arXiv AI
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TestJack: Should you trust the results in coding benchmarks? Agentic Coding Benchmarks Auditing via Evaluator Evolution
TestJack is a scalable framework that audits large language model (LLM) coding agents by generating new tests for each trial, focusing on prompt requirements that may be violated. It keeps only the tests passed by the ground‑truth patch and re‑examines failures, providing replayable evidence for each confirmed error. In experiments across six model backends and five benchmarks, TestJack found that 34.4% of trials deemed correct actually violated task requirements, reducing the overall resolution rate from 50.6% to 33.2%.
arXiv AI
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Accuracy, Stability, and Repeated-Run Reliability of Large Language Models on Deterministic Programming Tasks
arXiv:2606. 00920v1 Announce Type: cross Abstract: Run-level pass rate overstates retry-free coverage by up to 17.