arxiv: 2604.12379 · v1 · submitted 2026-04-14 · 💻 cs.SE · cs.AI· cs.LG

Recognition: unknown

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

Yuangang Li , Justin Tian Jin Chen , Ethan Yu , David Hong , Iftekhar Ahmed

Authors on Pith no claims yet

Pith reviewed 2026-05-10 15:53 UTC · model grok-4.3

classification 💻 cs.SE cs.AIcs.LG

keywords LLM reasoning evaluationcoding tasks benchmarkreasoning quality assessmentVERA evaluatorevidence verificationambiguity correctionCodeRQ-Bench

0 comments

The pith

VERA, a two-stage evaluator, outperforms baselines in assessing LLM reasoning quality for coding tasks by improving AUCROC up to 0.26.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper seeks to evaluate not just whether LLMs produce correct code but the quality of their underlying reasoning in coding tasks. It does this by releasing CodeRQ-Bench, a benchmark that spans code generation, summarization, and classification. Through detailed study of 1,069 cases where prior evaluators mismatched human assessments, the authors identify recurring limitations and extract four design insights. These insights inform the creation of VERA, which uses evidence-grounded verification and ambiguity-aware correction to achieve higher accuracy in detecting good reasoning.

Core claim

The authors present CodeRQ-Bench as the first benchmark for LLM reasoning quality in multiple coding task categories and propose VERA as an evaluator that combines evidence-grounded verification with ambiguity-aware score correction, demonstrating consistent outperformance over strong baselines with AUCROC gains up to 0.26 and AUPRC gains up to 0.21 across four datasets.

What carries the argument

VERA, a two-stage evaluator consisting of evidence-grounded verification followed by ambiguity-aware score correction.

If this is right

Reasoning evaluation in coding must incorporate checks for evidence support and handling of ambiguous outputs to be effective.
Benchmarks for this purpose need to include diverse task types such as summarization and classification in addition to generation.
Systematic analysis of evaluator-human mismatches can reveal generalizable principles for better evaluation design.
Deploying improved evaluators like VERA allows more reliable assessment of whether LLMs are truly reasoning well rather than just producing right answers by chance.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Similar mismatch-driven design processes could be applied to evaluate reasoning in other LLM application areas like math or science tasks.
Future benchmarks might expand CodeRQ-Bench to include more languages or complex coding scenarios to test generalizability.
LLM developers could use VERA during fine-tuning to penalize poor reasoning explicitly and encourage better step-by-step thinking.

Load-bearing premise

The 1,069 analyzed mismatch cases sufficiently represent the key shortcomings of existing reasoning evaluators in coding tasks so that the four design insights produce a broadly superior method.

What would settle it

If VERA fails to show similar performance gains on a held-out set of coding problems or with different LLMs not included in the four datasets, the superiority claim would be falsified.

Figures

Figures reproduced from arXiv: 2604.12379 by David Hong, Ethan Yu, Iftekhar Ahmed, Justin Tian Jin Chen, Yuangang Li.

**Figure 2.** Figure 2: Distribution of five limitation categories [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: Overview of VERA. The left panel shows an input sample with the task description, the LLM-generated [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

read the original 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 https://github.com/MrLYG/CodeRQ-Bench, supporting future investigations.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

CodeRQ-Bench and VERA give a practical new angle on evaluating reasoning quality in coding LLMs, but the reported gains depend on mismatch analysis whose representativeness is not yet clear.

read the letter

The paper introduces CodeRQ-Bench, the first benchmark that measures reasoning quality across code generation, summarization, and classification, and pairs it with VERA, a two-stage evaluator that combines evidence verification with ambiguity correction. They collected 1,069 mismatch cases from existing evaluators, extracted five limitations, turned them into four design insights, and show VERA lifting AUCROC by up to 0.26 and AUPRC by up to 0.21 on four datasets. Releasing the benchmark and code is a clear positive step that lets others build on it directly. The shift from output correctness to reasoning quality is useful for anyone trying to make AI coding assistants more trustworthy. The multi-task coverage also moves beyond the usual generation-only focus. The soft spot is the causal link between the mismatch cases and VERA's improvements. The abstract gives no detail on how the 1,069 cases were sampled, which models or prompts produced them, or how the human labels were collected and validated. If those cases cluster around particular error types or base models, the four insights could be benchmark-specific heuristics rather than general principles. The stress-test concern about overfitting to the same distribution therefore looks plausible from what is visible. No cross-benchmark transfer results are mentioned to rule it out. This work is aimed at researchers in AI evaluation and software engineering who need better tools to assess reasoning in code tasks. Readers who want a released dataset and a concrete evaluator design will get value from it. It deserves a serious referee to examine the data collection and labeling procedures and to test whether the gains hold under broader conditions.

Referee Report

3 major / 2 minor

Summary. The manuscript introduces CodeRQ-Bench, the first benchmark for evaluating LLM reasoning quality across coding task categories of generation, summarization, and classification. It analyzes 1,069 mismatch cases from existing evaluators to identify five recurring limitations, derives four design insights, and proposes VERA—a two-stage evaluator combining evidence-grounded verification with ambiguity-aware score correction. Experiments on CodeRQ-Bench across four datasets report that VERA outperforms strong baselines, with AUCROC gains up to 0.26 and AUPRC gains up to 0.21. The benchmark is released publicly.

Significance. If the empirical results hold under scrutiny, the work addresses a timely gap in moving beyond output correctness to assess reasoning quality in LLM-based coding tasks, which is increasingly relevant for software engineering. The public release of CodeRQ-Bench at the provided GitHub repository is a clear strength supporting reproducibility and community follow-up work.

major comments (3)

[§4] §4 (Analysis of Mismatch Cases): The description of how the 1,069 mismatch cases were collected, including sampling strategy from the base models and tasks, criteria for mismatch identification, and details on obtaining human labels (e.g., annotator expertise, guidelines, inter-annotator agreement), is insufficient. This is load-bearing because the five limitations and four design insights are extracted directly from this set, and without these controls the representativeness and lack of overfitting to specific error distributions cannot be assessed.
[§6] §6 (Experiments and Results): The reported AUCROC and AUPRC improvements are presented as maximum values across four datasets without per-dataset tables, statistical significance tests, or ablation studies isolating the evidence-grounded verification stage versus the ambiguity-aware correction stage. This weakens the causal link between the design insights and the performance gains, as it leaves open whether VERA's superiority is due to the proposed architecture or other factors.
[§5] §5 (VERA Architecture): There is no explicit mapping or validation showing how each of the four design insights translates into concrete components of the two-stage evaluator. Without this or an independent validation set outside CodeRQ-Bench, the claim that the insights produce a reliably superior evaluator remains under-supported.

minor comments (2)

[Abstract / §4] The abstract states 'five recurring limitations' but the introduction or §4 should confirm the exact count and list them explicitly for clarity; minor inconsistency in phrasing across sections.
[§6] Table 2 or equivalent results table: Ensure all baseline evaluators are fully named and cited in the caption or text to avoid ambiguity in comparing VERA's gains.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback and positive assessment of the work's timeliness and reproducibility. We address each major comment point-by-point below, indicating planned revisions to improve clarity and rigor without misrepresenting the current manuscript.

read point-by-point responses

Referee: [§4] §4 (Analysis of Mismatch Cases): The description of how the 1,069 mismatch cases were collected, including sampling strategy from the base models and tasks, criteria for mismatch identification, and details on obtaining human labels (e.g., annotator expertise, guidelines, inter-annotator agreement), is insufficient. This is load-bearing because the five limitations and four design insights are extracted directly from this set, and without these controls the representativeness and lack of overfitting to specific error distributions cannot be assessed.

Authors: We agree that the current description in §4 is insufficient for full reproducibility and assessment of representativeness. In the revised manuscript we will expand §4 to detail the sampling strategy (all mismatches identified across the four base models and three task categories were pooled, with 1,069 cases selected for human review), the mismatch identification criteria (disagreements between existing evaluator outputs and human judgments of reasoning quality), and the human labeling process (performed by three computer science graduate students with programming experience, following annotation guidelines that will be added to the appendix, with inter-annotator agreement reported). These additions will allow readers to evaluate the analysis more rigorously. revision: yes
Referee: [§6] §6 (Experiments and Results): The reported AUCROC and AUPRC improvements are presented as maximum values across four datasets without per-dataset tables, statistical significance tests, or ablation studies isolating the evidence-grounded verification stage versus the ambiguity-aware correction stage. This weakens the causal link between the design insights and the performance gains, as it leaves open whether VERA's superiority is due to the proposed architecture or other factors.

Authors: We acknowledge that presenting only maximum gains and omitting per-dataset breakdowns, significance tests, and ablations limits the strength of the causal claims. In the revised §6 we will include full per-dataset tables for AUCROC and AUPRC, add statistical significance tests on the improvements, and provide ablation studies that isolate the evidence-grounded verification stage from the ambiguity-aware correction stage. This will more clearly tie performance gains to the architecture components derived from the design insights. revision: yes
Referee: [§5] §5 (VERA Architecture): There is no explicit mapping or validation showing how each of the four design insights translates into concrete components of the two-stage evaluator. Without this or an independent validation set outside CodeRQ-Bench, the claim that the insights produce a reliably superior evaluator remains under-supported.

Authors: We will add to §5 an explicit table mapping each of the four design insights directly to the corresponding components in VERA's two-stage architecture. Regarding validation, CodeRQ-Bench is the first benchmark for reasoning quality in coding tasks, so it is used for both insight derivation and evaluation; we will clarify this design choice and note the consistent gains across four datasets as supporting evidence. We will not add an external independent validation set in this revision, as that is beyond the current scope, but the added mapping will strengthen the link between insights and implementation. revision: partial

Circularity Check

0 steps flagged

No circularity in empirical benchmark and evaluator proposal

full rationale

The paper introduces CodeRQ-Bench, analyzes 1,069 mismatch cases from existing evaluators to identify limitations and derive design insights, then proposes VERA and reports its experimental performance on the benchmark and other datasets. No equations, fitted parameters, self-citations, or uniqueness claims are present that reduce any prediction or result to the inputs by construction. The performance numbers (AUCROC/AUPRC gains) are presented as direct experimental outcomes rather than tautological derivations, making the work self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Claims rest on the representativeness of analyzed mismatch cases and the validity of design insights extracted from them; no free parameters or invented entities are mentioned.

axioms (1)

domain assumption Mismatch cases between existing evaluators and human judgment reveal the primary limitations of current reasoning evaluation methods for coding tasks.
Paper states it analyzed 1,069 such cases to identify five recurring limitations.

pith-pipeline@v0.9.0 · 5492 in / 1197 out tokens · 37840 ms · 2026-05-10T15:53:12.158420+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

15 extracted references · 3 canonical work pages · 1 internal anchor

[1]

CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation

Codexglue: A machine learning benchmark dataset for code understanding and generation.arXiv preprint arXiv:2102.04664. Vladimir Makharev and Vladimir Ivanov. 2025. Code summarization beyond function level.Preprint, arXiv:2502.16704. Yvette Oortwijn, Thijs Ossenkoppele, and Arianna Betti

work page internal anchor Pith review arXiv 2025
[2]

O’Reilly Me- dia, Inc

Interrater disagreement resolution: A system- atic procedure to reach consensus in annotation tasks. InProceedings of the workshop on human evaluation of NLP systems (HumEval), pages 131–141. OpenAI. 2024a. Gpt-4o system card. OpenAI. 2024b. New embedding models and api up- dates. OpenAI. 2025a. Api pricing. https: //developers.openai.com/api/docs/pricing...

work page arXiv 2026
[3]

Hao Yu, Bo Shen, Dezhi Ran, Jiaxin Zhang, Qi Zhang, Yuchi Ma, Guangtai Liang, Ying Li, Qianxiang Wang, and Tao Xie

Tree of thoughts: Deliberate problem solving with large language models.Advances in neural information processing systems, 36:11809–11822. Hao Yu, Bo Shen, Dezhi Ran, Jiaxin Zhang, Qi Zhang, Yuchi Ma, Guangtai Liang, Ying Li, Qianxiang Wang, and Tao Xie. 2024. Codereval: A benchmark of prag- matic code generation with generative pre-trained models. InProc...

work page arXiv 2024
[4]

Supplementary Material A CodeRQ-Bench’s Dataset Details A.1 CoderEval-RE and SWEbench-RE Zhang et al

Judging llm-as-a-judge with mt-bench and chatbot arena.Advances in neural information pro- cessing systems, 36:46595–46623. Supplementary Material A CodeRQ-Bench’s Dataset Details A.1 CoderEval-RE and SWEbench-RE Zhang et al. (Zhang et al., 2025) evaluate three LLMs on CoderEval (Yu et al., 2024) and SWE- bench (Jimenez et al., 2024), providing expert ann...

2025
[5]

Execution Log

consists of real GitHub issues and their corre- sponding pull requests from popular Python repos- itories. SWEbench-RE augments this benchmark with expert-annotated reasoning traces, allowing as- sessment of whether LLMs reason correctly when resolving software engineering tasks. A.2 ClassEval-RE Dataset Details A.2.1 Source Dataset - Modified-ClassEval C...

2023
[6]

Identify which specific component/step in the pipeline produced the erroneous signal (e.g., which SRL parse was wrong, which NLI judgment was incorrect, which dimension scored too high, or how the self-generated reference was flawed)
[8]

Execution Log

What capability is missing from{method_name}that would be needed to correctly evaluate this case? Table A3: Prompt template used to generate diagnostic annotations for missed-error cases, where the evaluator assigns a high score to reasoning that is actually incorrect. Here,{case_content}includes the original task, the LLM-generated reasoning chain and fi...

2023
[9]

Identify which specific component/step in the pipeline produced the erroneous signal (e.g., which step was the bottleneck, which SRL parse was malformed, which NLI score was unreasonably low, or which dimension was incorrectly penalized)
[10]

Explain the root cause: why did that component fail on this particular input?
[11]

limitations

What are the characteristics of this correct reasoning that confused the{method_name} evaluator? Table A4: Prompt template used to generate diagnostic annotations for false-alarm cases, where the evaluator assigns a low score to reasoning that is actually correct. Here,{case_content}includes the original task, the LLM-generated reasoning chain and final o...

2024
[12]

preclusters

is a general evaluation paradigm that uses strong large language models to assess the quality of candidate responses across diverse tasks, includ- ing coding and mathematical reasoning. It supports both pairwise comparison and direct scoring; in the latter setting, the judge assigns a score, typically with a textual explanation. In our experiments, we use...

2024
[13]

First, read through all{num_pcs}preclusters and identify the natural fault lines
[14]

Propose categories. For each category, provide: - A clearname(2–5 words, lowercase_with_underscores) - A precisedefinition(1–2 sentences) - Aboundary rule: how to decide if a precluster belongs here vs. in another category
[15]

If you find overlap, revise

After proposing all categories, run apairwise MECE check: for each pair (X, Y), state in one sentence why a mismatch described by X cannot also be described by Y. If you find overlap, revise
[16]

reasoning

Finally, assign every precluster to exactly one category. ### Output Format Return a JSON object: { "reasoning": "Your analysis of the natural fault lines you identified", "categories": [ { "name": "category_name", "definition": "Precise definition of this category", "boundary_rule": "When a precluster is X vs Y, it belongs here if...", "assigned_preclust...