arxiv: 2604.25399 · v1 · submitted 2026-04-28 · 💻 cs.SE

Recognition: unknown

CoRE: A Fine-Grained Code Reasoning Benchmark Beyond Output Prediction

Jun Gao , Yun Peng , Qian Qiao , Changhai Zhou , Yuhua Zhou , Shiyang Zhang , Shichao Weng , Zhenchang Xing

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Xiaoxue Ren

Authors on Pith no claims yet

Pith reviewed 2026-05-07 15:57 UTC · model grok-4.3

classification 💻 cs.SE

keywords code reasoninglarge language modelsbenchmarkimplementation invarianceprocess transparencyrobustness gapsuperficial executioncode execution

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The pith

Large language models exhibit inconsistent code reasoning across equivalent implementations and fail to track intermediate execution states.

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

The paper introduces the CoRE benchmark to assess whether large language models reason about code execution rather than merely predicting outputs. CoRE tests implementation invariance by checking consistency across functionally equivalent code versions and process transparency by querying intermediate execution states. Evaluations across eight frontier models show significant performance drops on equivalent codes and errors in intermediate reasoning despite correct finals. This reveals that current output-focused benchmarks are inadequate for measuring true code reasoning abilities.

Core claim

Through the CoRE benchmark, which measures implementation invariance and process transparency, the authors demonstrate that large language models display a robustness gap in performance across equivalent code implementations and engage in superficial execution by producing correct outputs without accurately reasoning about intermediate states. These findings establish that output-only evaluations are insufficient for assessing code reasoning and highlight the need for benchmarks that probe deeper into the reasoning process.

What carries the argument

The CoRE benchmark, which evaluates code reasoning via implementation invariance across equivalent code variants and process transparency through intermediate state questions.

If this is right

Output-only evaluations do not sufficiently assess code reasoning in large language models.
Models show substantial variation in performance when presented with functionally equivalent but differently implemented code.
Correct final outputs often mask incorrect reasoning about execution intermediates.
New benchmarks are required to evaluate robust and faithful code reasoning.

Where Pith is reading between the lines

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

Practitioners relying on LLMs for code tasks may need to implement additional verification steps beyond output checking.
This pattern of superficial reasoning could apply to other structured reasoning tasks such as algorithm simulation or debugging.
Future model development might benefit from training objectives that explicitly reward accurate intermediate state prediction.

Load-bearing premise

The selected functionally equivalent implementations behave identically in all execution aspects relevant to reasoning, and the intermediate-state questions measure genuine process understanding rather than superficial pattern matching.

What would settle it

An experiment showing that a model achieves consistent high accuracy across multiple equivalent code implementations while also correctly answering detailed questions about every intermediate execution state would challenge the identified limitations.

Figures

Figures reproduced from arXiv: 2604.25399 by Changhai Zhou, Jun Gao, Qian Qiao, Shichao Weng, Shiyang Zhang, Xiaoxue Ren, Yuhua Zhou, Yun Peng, Zhenchang Xing.

**Figure 1.** Figure 1: The code reasoning comparison of standard view at source ↗

**Figure 2.** Figure 2: Comparison of CoRE against existing code reasoning benchmarks. (Left) Distribution of Average Cyclomatic Complexity and Average Number of Test Cases. (Right) Comparison of code reasoning benchmarks. Samples: Total number of samples for inference. Tests: Average number of test cases per instance. Impl.: Average number of diverse code implementations per coding problem. J : Average Jaccard similarity between… view at source ↗

**Figure 3.** Figure 3: The construction pipeline of CoRE. It consists of two stages: (I) view at source ↗

**Figure 4.** Figure 4: The taxonomy and distribution of Intermediate Probing dimensions. The left panel illustrates the four view at source ↗

**Figure 5.** Figure 5: Is scores performance heatmap across five LLM families. Red and gold boxes indicate the rank1 and rank-2 Is scores. The pronounced diagonal pattern indicates a strong style bias. issue, increasing Ws to 64.93 for GPT-5, which demonstrates that explicitly reasoning improves process fidelity. Regarding the reflection-based RHDA framework, implementation invariance is consistently enhanced, whereas gains in … view at source ↗

**Figure 6.** Figure 6: Exposure trend across increasing test cases. view at source ↗

**Figure 7.** Figure 7: The interaction of intermediate probes view at source ↗

read the original abstract

Despite strong performance on code generation tasks, it remains unclear whether large language models (LLMs) genuinely reason about code execution. Existing code reasoning benchmarks primarily evaluate final output correctness under a single canonical implementation, leaving two critical aspects underexplored: (1) whether LLMs can maintain consistency to functionally equivalent implementations, and (2) whether LLMs can accurately reason about intermediate execution states. We introduce \textbf{CoRE}, a \textbf{Co}de \textbf{Re}asoning benchmark that evaluates code reasoning through \textbf{implementation invariance} and \textbf{process transparency}. Extensive evaluations on eight frontier LLMs reveal two fundamental limitations. First, models exhibit a substantial \textbf{robustness gap}, with performance varying significantly across equivalent implementations. Second, we observe \textbf{superficial execution}, where models arrive at correct final outputs without correctly reasoning about intermediate execution states. Together, these findings demonstrate that output-only evaluations are insufficient for assessing code reasoning and position CoRE as a necessary benchmark for evaluating robust and faithful code reasoning.\footnote{Data and code are available at https://github.com/ZJUSig/CoRE.}

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

CoRE gives a practical new benchmark for code reasoning that checks invariance across equivalent implementations and tracks intermediate execution steps, but the robustness and superficial-execution claims rest on equivalence that the abstract does not show was verified for the process questions.

read the letter

CoRE pushes evaluation of code LLMs past single-output checks. It adds tasks that test whether models stay consistent when the same logic appears in different but equivalent code, and whether they can correctly answer questions about intermediate states during execution rather than just landing on the right final result. The authors ran eight frontier models and report a noticeable robustness gap plus cases of superficial execution where the output is correct but the step-by-step reasoning is not. Releasing the data and code at the GitHub link is a clear plus for anyone who wants to inspect or extend the tasks.

Referee Report

2 major / 2 minor

Summary. The paper introduces CoRE, a benchmark for assessing LLMs' code reasoning via implementation invariance (consistency across functionally equivalent code variants) and process transparency (accuracy on intermediate execution states). Evaluations across eight frontier LLMs demonstrate a substantial robustness gap, with performance varying across equivalent implementations, and superficial execution, where models produce correct final outputs without accurate intermediate reasoning. The work argues that output-only evaluations are insufficient and releases data/code at the cited GitHub link.

Significance. If the empirical claims hold after verification, CoRE would be a valuable addition to code reasoning benchmarks by exposing gaps not captured by final-output metrics alone. The public release of data and code strengthens reproducibility and enables follow-on work. The findings align with broader concerns about LLM faithfulness in reasoning tasks but would benefit from tighter controls on task construction to isolate model limitations from benchmark artifacts.

major comments (2)

[§3] §3 (Benchmark Construction), implementation pairs: The central robustness-gap claim rests on the premise that selected functionally equivalent implementations produce identical answers to all intermediate-state questions. No explicit cross-verification (e.g., execution traces or static analysis confirming invariance of control flow, variable lifetimes, or short-circuit behavior) is described; without it, observed performance drops could reflect legitimate differences in expected intermediate states rather than model failure.
[§4] §4 (Experiments), data selection and metrics: The abstract and evaluation summary report results on eight models and two limitations, yet the manuscript provides insufficient detail on how code pairs were sampled, how equivalence was operationalized beyond final output, and the precise definition/scoring of 'superficial execution' (e.g., how many intermediate questions must be answered incorrectly to classify a case). This makes it difficult to assess whether the reported gaps are robust or sensitive to task design choices.

minor comments (2)

[§4] Clarify the exact prompt templates and few-shot examples used for each sub-task (implementation invariance vs. process transparency) to support replication.
[§3] Add a table summarizing the number of code pairs, languages covered, and average number of intermediate questions per example.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed comments. We agree that greater transparency in benchmark construction and evaluation details will strengthen the paper. Below we respond to each major comment and indicate the revisions we will make.

read point-by-point responses

Referee: [§3] §3 (Benchmark Construction), implementation pairs: The central robustness-gap claim rests on the premise that selected functionally equivalent implementations produce identical answers to all intermediate-state questions. No explicit cross-verification (e.g., execution traces or static analysis confirming invariance of control flow, variable lifetimes, or short-circuit behavior) is described; without it, observed performance drops could reflect legitimate differences in expected intermediate states rather than model failure.

Authors: We acknowledge the validity of this concern. The implementation pairs were selected such that they produce identical outputs on the same test suites, and intermediate questions were derived from a canonical execution trace; however, the manuscript does not describe a systematic verification step confirming that all intermediate states are invariant across pairs. In the revised manuscript we will add an appendix containing execution-trace comparisons (via dynamic instrumentation) for the full set of pairs, along with a brief static analysis confirming control-flow and variable-lifetime equivalence. This will directly address the possibility of benchmark artifacts. revision: yes
Referee: [§4] §4 (Experiments), data selection and metrics: The abstract and evaluation summary report results on eight models and two limitations, yet the manuscript provides insufficient detail on how code pairs were sampled, how equivalence was operationalized beyond final output, and the precise definition/scoring of 'superficial execution' (e.g., how many intermediate questions must be answered incorrectly to classify a case). This makes it difficult to assess whether the reported gaps are robust or sensitive to task design choices.

Authors: We agree that additional methodological detail is warranted. The original text in §3 and §4 is brief on these points. In the revision we will expand §4 with: (i) the exact sampling procedure (stratified random selection from a curated pool of 200 problems, each with 2–4 implementations), (ii) a formal definition of functional equivalence (identical input–output behavior on a held-out test suite of at least 10 cases), and (iii) the precise scoring rule for superficial execution (a trial is labeled superficial when the final output is correct yet at least one of the intermediate-state questions is answered incorrectly). We will also include a sensitivity table showing that the reported robustness gap and superficial-execution rates remain stable under modest changes to these thresholds. revision: yes

Circularity Check

0 steps flagged

No circularity: new benchmark with independent empirical claims

full rationale

The paper introduces CoRE as an external benchmark evaluating LLMs on implementation invariance and process transparency via new tasks. Central claims about robustness gaps and superficial execution rest on direct empirical results from eight models rather than any derivation, fitted parameters, or self-referential definitions. No equations, ansatzes, or load-bearing self-citations appear in the provided text; the benchmark is presented as falsifiable with released data and code. The derivation chain is therefore self-contained and does not reduce to its inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The benchmark construction assumes that selected code variants are functionally equivalent and that intermediate-state probes measure genuine reasoning; these are domain assumptions rather than derived results.

axioms (2)

domain assumption Functionally equivalent implementations produce identical execution semantics for the purposes of the benchmark tasks
Invoked when defining implementation invariance as a core evaluation axis.
domain assumption Correct answers on intermediate execution state questions indicate faithful process reasoning rather than pattern matching
Central to the claim of superficial execution.

pith-pipeline@v0.9.0 · 5523 in / 1228 out tokens · 34352 ms · 2026-05-07T15:57:43.654659+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

9 extracted references · 4 canonical work pages · 3 internal anchors

[1]

Islem Bouzenia, Premkumar Devanbu, and Michael Pradel

IEEE. Islem Bouzenia, Premkumar Devanbu, and Michael Pradel. 2024. Repairagent: An autonomous, llm- based agent for program repair.arXiv preprint arXiv:2403.17134. Junkai Chen, Zhiyuan Pan, Xing Hu, Zhenhao Li, Ge Li, and Xin Xia. 2025. Reasoning runtime behavior of a program with llm: How far are we? In2025 IEEE/ACM 47th International Conference on Soft-...

work page arXiv 2024
[2]

CRUXEval: A Benchmark for Code Reasoning, Understanding and Execution

Cruxeval: A benchmark for code reason- ing, understanding and execution.arXiv preprint arXiv:2401.03065. Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shi- rong Ma, Peiyi Wang, Xiao Bi, and 1 others. 2025. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning.arXiv preprint arXiv:2501.12...

work page internal anchor Pith review arXiv 2025
[3]

DeepSeek-V3 Technical Report

Let’s verify step by step. InThe Twelfth Inter- national Conference on Learning Representations. Aixin Liu, Bei Feng, Bing Xue, Bingxuan Wang, Bochao Wu, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, and 1 others. 2024a. Deepseek-v3 technical report.arXiv preprint arXiv:2412.19437. Aixin Liu, Aoxue Mei, Bangcai Lin, Bing Xue, Bingx- ...

work page internal anchor Pith review arXiv 2025
[4]

Qwen3 Technical Report

Qwen3 technical report.arXiv preprint arXiv:2505.09388. John Yang, Carlos E Jimenez, Alexander Wettig, Kilian Lieret, Shunyu Yao, Karthik Narasimhan, and Ofir Press. 2024. Swe-agent: Agent-computer interfaces enable automated software engineering.Advances in Neural Information Processing Systems, 37:50528– 50652. Yuze Zhao, Tianyun Ji, Wenjun Feng, Zhenya...

work page internal anchor Pith review arXiv 2024
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Complex Arithmetic:Lines with mul- tiple operators (e.g., res = (a + b) * c % d) or list indexing with math
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Loop Boundaries:The last iteration of a loop, or the state immediately after a loop finishes
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Nested Logic:Steps inside a nested loop or a nested if block where context is deep
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Compound Conditions:Boolean eval- uations involvingand, or, not
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At the end of the 3rd iteration

State Accumulation:A variable modi- fied multiple times (e.g.,total after the 5th iteration). ## Rules: • Answer Integrity:The ‘ground_truth‘ must be extracted EXACTLY from the provided trace. Do not compute it your- self. • Precision:The question must specify the exact context (e.g., "At the end of the 3rd iteration...", "In the evaluation of the conditi...