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

Researchers introduce CodeRQ-Bench, the first benchmark for evaluating LLM reasoning quality across coding tasks including generation, summarization, and classification. They propose VERA, a two-stage evaluator combining evidence-grounded verification with ambiguity-aware score correction, achieving significant performance improvements over existing methods.

The development of CodeRQ-Bench addresses a critical gap in AI evaluation infrastructure. While LLMs have demonstrated impressive coding capabilities, assessing the quality of their reasoning—not just output correctness—remains poorly understood. Traditional benchmarks focus narrowly on code generation success rates, ignoring whether models understand why their solutions work or can perform auxiliary tasks like code summarization and classification.

This work emerged from recognizing that existing reasoning evaluators, primarily designed for general NLP tasks, fail to capture domain-specific nuances in coding contexts. The researchers analyzed over 1,000 mismatch cases where conventional evaluators disagreed with human judgment, uncovering systematic evaluation failures. Their insights revealed that coding reasoning evaluation requires fundamentally different approaches—accounting for multiple valid solutions, understanding algorithmic correctness beyond syntax, and recognizing ambiguous reasoning paths.

For developers and AI researchers, CodeRQ-Bench provides a more rigorous evaluation framework that moves beyond surface-level metrics. This directly impacts how coding-focused LLMs are developed and deployed, influencing decisions about model selection and fine-tuning strategies. Companies building AI-assisted coding tools gain empirical evidence for assessing reasoning quality, supporting claims about model reliability beyond simple accuracy benchmarks.

The broader significance extends to LLM evaluation methodology itself. As models increasingly tackle complex reasoning tasks across domains, developing task-specific evaluation frameworks becomes essential. VERA's performance improvements suggest that domain-aware evaluation strategies substantially outperform generic approaches, signaling a shift toward specialized benchmarking infrastructure across specialized domains.

• →CodeRQ-Bench is the first benchmark specifically designed to evaluate LLM reasoning quality in coding tasks, covering generation, summarization, and classification.
• →Analysis of 1,069 evaluation mismatches revealed five recurring limitations in existing reasoning evaluators for coding contexts.
• →VERA evaluator improves AUCROC by up to 0.26 and AUPRC by up to 0.21 through evidence-grounded verification and ambiguity-aware corrections.
• →Current coding benchmarks focus primarily on output correctness while ignoring reasoning quality assessment.
• →Domain-specific evaluation frameworks outperform generic reasoning evaluators significantly for specialized tasks.