# CRUXEval-X: A Benchmark for Multilingual Code Reasoning, Understanding and Execution

arXiv: https://arxiv.org/abs/2408.13001 · HTML: https://mlsanity.com/paper/2408.13001/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning.
- Builds on 33 of its 44 references that are in the map.
- First posted on arXiv 23 Aug 2024.

## Placed in

- LLM reasoning / [Reasoning for agents, planning and code](https://mlsanity.com/lr/reasoning-for-agents-planning-and-code/)

## Builds on (most cited first, 30 of 33)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Program Synthesis with Large Language Models](https://mlsanity.com/paper/2108.07732/) (2021)
- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [Qwen Technical Report](https://mlsanity.com/paper/2309.16609/) (2023)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [Qwen2 Technical Report](https://mlsanity.com/paper/2407.10671/) (2024)
- [Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone](https://mlsanity.com/paper/2404.14219/) (2024)
- [DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence](https://mlsanity.com/paper/2401.14196/) (2024)
- [Teaching Large Language Models to Self-Debug](https://mlsanity.com/paper/2304.05128/) (2023)
- [CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis](https://mlsanity.com/paper/2203.13474/) (2022)
- [StarCoder: may the source be with you!](https://mlsanity.com/paper/2305.06161/) (2023)
- [WizardCoder: Empowering Code Large Language Models with Evol-Instruct](https://mlsanity.com/paper/2306.08568/) (2023)
- [Textbooks Are All You Need](https://mlsanity.com/paper/2306.11644/) (2023)
- [Textbooks Are All You Need II: phi-1.5 technical report](https://mlsanity.com/paper/2309.05463/) (2023)
- [StarCoder 2 and The Stack v2: The Next Generation](https://mlsanity.com/paper/2402.19173/) (2024)
- [DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence](https://mlsanity.com/paper/2406.11931/) (2024)
- [CRUXEval: A Benchmark for Code Reasoning, Understanding and Execution](https://mlsanity.com/paper/2401.03065/) (2024)
- [Multi-lingual Evaluation of Code Generation Models](https://mlsanity.com/paper/2210.14868/) (2022)
- [Debug like a Human: A Large Language Model Debugger via Verifying Runtime Execution Step-by-step](https://mlsanity.com/paper/2402.16906/) (2024)
- [Keep the Conversation Going: Fixing 162 out of 337 bugs for 0.42 each using ChatGPT](https://mlsanity.com/paper/2304.00385/) (2023)
- [Leveraging Automated Unit Tests for Unsupervised Code Translation](https://mlsanity.com/paper/2110.06773/) (2021)
- [DebugBench: Evaluating Debugging Capability of Large Language Models](https://mlsanity.com/paper/2401.04621/) (2024)
- [AVATAR: A Parallel Corpus for Java-Python Program Translation](https://mlsanity.com/paper/2108.11590/) (2021)
- [CodeTransOcean: A Comprehensive Multilingual Benchmark for Code Translation](https://mlsanity.com/paper/2310.04951/) (2023)
- [XLCoST: A Benchmark Dataset for Cross-lingual Code Intelligence](https://mlsanity.com/paper/2206.08474/) (2022)
- [Reasoning Runtime Behavior of a Program with LLM: How Far Are We?](https://mlsanity.com/paper/2403.16437/) (2024)
- [McEval: Massively Multilingual Code Evaluation](https://mlsanity.com/paper/2406.07436/) (2024)
- [CodeTrans: Towards Cracking the Language of Silicon's Code Through Self-Supervised Deep Learning and High Performance Computing](https://mlsanity.com/paper/2104.02443/) (2021)
- [Rectifier: Code Translation with Corrector via LLMs](https://mlsanity.com/paper/2407.07472/) (2024)

## Built on by (most cited first, 30 of 31)

- [Lingma SWE-GPT: An Open Development-Process-Centric Language Model for Automated Software Improvement](https://mlsanity.com/paper/2411.00622/) (2024)
- [A Survey of Multilingual Reasoning in Language Models](https://mlsanity.com/paper/2502.09457/) (2025)
- [Thinking Longer, Not Larger: Enhancing Software Engineering Agents via Scaling Test-Time Compute](https://mlsanity.com/paper/2503.23803/) (2025)
- [A Comprehensive Survey on Benchmarks and Solutions in Software Engineering of LLM-Empowered Agentic System](https://mlsanity.com/paper/2510.09721/) (2025)
- [From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligence](https://mlsanity.com/paper/2511.18538/) (2025)
- [CodeSense: a Real-World Benchmark and Dataset for Code Semantic Reasoning](https://mlsanity.com/paper/2506.00750/) (2025)
- [Software Development Life Cycle Perspective: A Survey of Benchmarks for Code Large Language Models and Agents](https://mlsanity.com/paper/2505.05283/) (2025)
- [LLMs as Continuous Learners: Improving the Reproduction of Defective Code in Software Issues](https://mlsanity.com/paper/2411.13941/) (2024)
- [Helping LLMs Improve Code Generation Using Feedback from Testing and Static Analysis](https://mlsanity.com/paper/2412.14841/) (2024)
- [LLMs Lean on Priors, Not Programming Language Semantics](https://mlsanity.com/paper/2510.03415/) (2025)
- [MirrorCode: AI can rebuild entire programs from behavior alone](https://mlsanity.com/paper/2606.30182/) (2026)
- [EmbedAgent: Benchmarking Large Language Models in Embedded System Development](https://mlsanity.com/paper/2506.11003/) (2025)
- [Demystifying Errors in LLM Reasoning Traces: An Empirical Study of Code Execution Simulation](https://mlsanity.com/paper/2512.00215/) (2025)
- [Benchmarking AI Models in Software Engineering: A Review, Search Tool, and Unified Approach for Elevating Benchmark Quality](https://mlsanity.com/paper/2503.05860/) (2025)
- [CodeReasoner: Enhancing the Code Reasoning Ability with Reinforcement Learning](https://mlsanity.com/paper/2507.17548/) (2025)
- [Can LLMs Compress (and Decompress)? Evaluating Code Understanding and Execution via Invertibility](https://mlsanity.com/paper/2601.13398/) (2026)
- [Across Programming Language Silos: A Study on Cross-Lingual Retrieval-augmented Code Generation](https://mlsanity.com/paper/2506.03535/) (2025)
- [Self-Execution Simulation Improves Coding Models](https://mlsanity.com/paper/2604.03253/) (2026)
- [Sense and Sensitivity: Examining the Influence of Semantic Recall on Long Context Code Understanding](https://mlsanity.com/paper/2505.13353/) (2025)
- [Beyond Language Barriers: Multi-Agent Coordination for Multi-Language Code Generation](https://mlsanity.com/paper/2509.19918/) (2025)
- [Spec-Harness: Assessing and Guiding LLM-Synthesized Formal Specifications Beyond Verifiability](https://mlsanity.com/paper/2604.00280/) (2026)
- [Parallel-SFT: Improving Zero-Shot Cross-Programming-Language Transfer for Code RL](https://mlsanity.com/paper/2604.20835/) (2026)
- [StepCodeReasoner: Aligning Code Reasoning with Stepwise Execution Traces via Reinforcement Learning](https://mlsanity.com/paper/2605.11922/) (2026)
- [STEPWISE-CODEX-Bench: Evaluating Complex Multi-Function Comprehension and Fine-Grained Execution Reasoning](https://mlsanity.com/paper/2508.05193/) (2025)
- [Enhancing the Code Reasoning Capabilities of LLMs via Consistency-based Reinforcement Learning](https://mlsanity.com/paper/2605.17958/) (2026)
- [ITPEval: Benchmarking Formal Translation Across Interactive Theorem Provers](https://mlsanity.com/paper/2607.19407/) (2026)
- [CrossPL: Evaluating Large Language Models on Cross Programming Language Code Generation](https://mlsanity.com/paper/2507.19904/) (2025)
- [EvoCodeBench: A Human-Performance Benchmark for Self-Evolving LLM-Driven Coding Systems](https://mlsanity.com/paper/2602.10171/) (2026)
- [Where Larger Models Excel: The Primacy of Constraint-Guided Reasoning](https://mlsanity.com/paper/2606.26108/) (2026)
- [Don't Let Me Ask for It: LLMs Show Deficiencies in Active Multi-Turn Information Acquisition for Abductive Inference](https://mlsanity.com/paper/2608.03388/) (2026)

## Abstract (arXiv)

Code benchmarks such as HumanEval are widely adopted to evaluate Large Language Models' (LLMs) coding capabilities. However, there is an unignorable programming language bias in existing code benchmarks -- over 95% code generation benchmarks are dominated by Python, leaving the LLMs' capabilities in other programming languages such as Java and C/C++ unknown. Moreover, coding task bias is also crucial. Most benchmarks focus on code generation capability, while benchmarks for code reasoning (given input, reasoning output; and given output, reasoning input), an essential coding capability, are insufficient. Yet, constructing multi-lingual benchmarks can be expensive and labor-intensive, and codes in contest websites such as Leetcode suffer from data contamination during training. To fill this gap, we propose CRUXEVAL-X, a multi-lingual code reasoning benchmark that contains 19 programming languages. It comprises at least 600 subjects for each language, along with 19K content-consistent tests in total. In particular, the construction pipeline of CRUXEVAL-X works in a fully automated and test-guided manner, which iteratively generates and repairs based on execution feedback. Also, to cross language barriers (e.g., dynamic/static type systems in Python/C++), we formulated various transition rules between language pairs to facilitate translation. Our intensive evaluation of 24 representative LLMs reveals the correlation between language pairs. For example, TypeScript and JavaScript show a significant positive correlation, while Racket has less correlation with other languages. More interestingly, even a model trained solely on Python can achieve at most 34.4% Pass@1 in other languages, revealing the cross-language generalization of LLMs.