StackPilot: Autonomous Function Agents for Scalable and Environment-Free Code Execution
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866917197660553216 |
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| author | Zhao, Xinkui Zhang, Yifan Zhou, Zhengyi Xu, Yueshen |
| author_facet | Zhao, Xinkui Zhang, Yifan Zhou, Zhengyi Xu, Yueshen |
| contents | Recent advances in large language models (LLMs) have substantially enhanced automated code generation across a wide range of programming languages. Nonetheless, verifying the correctness and executability of LLM-generated code remains a significant challenge, as traditional methods rely on language-specific compilers and environment-dependent runtimes. To overcome these limitations, we introduce StackPilot, an LLM-native, multi-agent framework designed for language-agnostic code verification and execution, which operates independently of conventional toolchains. StackPilot offers three principal innovations: (1) a Function-as-Agents paradigm, in which each function is modeled as an autonomous agent capable of fine-grained reasoning and collaborative verification; (2) an LLM-as-Executor strategy, which enables scalable verification via stack-based scheduling; and (3) a novel snapshot mechanism that preserves complete execution contexts, facilitating deterministic and lossless context switching during verification. Empirical evaluations demonstrate that StackPilot achieves framework reliability rates between 89% and 97%, substantially outperforming baseline approaches. These results indicate that StackPilot can reliably verify and execute a significantly larger proportion of LLM-generated code across diverse programming tasks compared to existing methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_11665 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | StackPilot: Autonomous Function Agents for Scalable and Environment-Free Code Execution Zhao, Xinkui Zhang, Yifan Zhou, Zhengyi Xu, Yueshen Programming Languages Multiagent Systems Recent advances in large language models (LLMs) have substantially enhanced automated code generation across a wide range of programming languages. Nonetheless, verifying the correctness and executability of LLM-generated code remains a significant challenge, as traditional methods rely on language-specific compilers and environment-dependent runtimes. To overcome these limitations, we introduce StackPilot, an LLM-native, multi-agent framework designed for language-agnostic code verification and execution, which operates independently of conventional toolchains. StackPilot offers three principal innovations: (1) a Function-as-Agents paradigm, in which each function is modeled as an autonomous agent capable of fine-grained reasoning and collaborative verification; (2) an LLM-as-Executor strategy, which enables scalable verification via stack-based scheduling; and (3) a novel snapshot mechanism that preserves complete execution contexts, facilitating deterministic and lossless context switching during verification. Empirical evaluations demonstrate that StackPilot achieves framework reliability rates between 89% and 97%, substantially outperforming baseline approaches. These results indicate that StackPilot can reliably verify and execute a significantly larger proportion of LLM-generated code across diverse programming tasks compared to existing methods. |
| title | StackPilot: Autonomous Function Agents for Scalable and Environment-Free Code Execution |
| topic | Programming Languages Multiagent Systems |
| url | https://arxiv.org/abs/2508.11665 |