Beyond Output Correctness: Benchmarking and Evaluating Large Language Model Reasoning in Coding Tasks
Large language models (LLMs) increasingly rely on explicit reasoning to solve coding tasks, yet evaluating the quality of this reasoning remains challenging. Existing reasoning evaluators are not designed for coding, and current benchmarks focus primarily on code generation, leaving other coding tasks largely unexplored. We introduce CodeRQ-Bench, the first benchmark for evaluating LLM reasoning quality across three coding task categories: generation, summarization, and classification. Using this benchmark, we analyze 1,069 mismatch cases from existing evaluators, identify five recurring limitations, and derive four design insights for reasoning evaluation in coding tasks. Guided by these...
| Authors | Yuangang Li, Justin Tian Jin Chen, Ethan Yu et al. |
|---|---|
| Primary category | cs.SE |
| Published | 14 April 2026 |
| Last updated at source | 27 August 2026 |
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