Beyond Output Correctness: Benchmarking and Evaluating Large Language Model Reasoning in Coding Tasks

[2604.12379] Beyond Output Correctness: Benchmarking and Evaluating Large Language Model Reasoning in Coding Tasks Skip to main content Search Submit Donate Log in Computer Science > Software Engineering arXiv:2604.12379 (cs) [Submitted on 14 Apr 2026 ( v1 ), last revised 27 Aug 2026 (this version, v2)] Title: Beyond Output Correctness: Benchmarking and Evaluating Large Language Model Reasoning in Coding Tasks Authors: Yuangang Li , Justin Tian Jin Chen , Ethan Yu , David Hong , Iftekhar Ahmed View PDF HTML (experimental) Abstract: 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 insights, we propose VERA, a two-stage evaluator that combines evidence-grounded verification with ambiguity-aware score correction. Experiments on CodeRQ-Bench show that VERA consistently outperforms strong baselines across four datasets, improving AUCROC by up to 0.26 and AUPRC by up to 0.21. We release CodeRQ-Bench at this https URL , supporting future investigations. Subjects: Software Engineering (cs.SE) ; Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2604.12379 [cs.SE] (or arXiv:2604.12379v2 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2604.12379 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Yuangang Li [ view email ] [v1] Tue, 14 Apr 2026 07:12:46 UTC (1,678 KB) [v2] Thu, 27 Aug 2026 23:16:42 UTC (1,678 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.SE < prev | next > new | recent | 2026-04 Change to browse by: cs cs.AI cs.LG References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from (opens in new tab) (opens in new tab) (opens in new tab)

Related documents

• Task Abstention for Large Language Models in Code Generation#200576
• Beyond Output Correctness: Benchmarking and Evaluating Large Language Model Reasoning in Coding Tasks#682643
• Beyond Output Correctness: Benchmarking and Evaluating Large Language Model Reasoning in Coding Tasks#13163
• (Towards) Scalable Reliable Automated Evaluation with Large Language Models#414122
• Beyond Fluency: A Human-Centered Evaluation Rubric for Grounding, Answerability, and Reliability in LLM Outputs#118367