mlsanityData to 14 Oct 2026

OSVBench: Benchmarking LLMs on Specification Generation Tasks for Operating System Verification

arXiv 2504.20964 ↗First posted 29 Apr 2025cs.CL cs.AI cs.OScited by 8

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Abstract (arXiv)

We introduce OSVBench, a new benchmark for evaluating Large Language Models (LLMs) on the task of generating complete formal specifications for verifying the functional correctness of operating system kernels. This benchmark is built upon a real-world operating system kernel, Hyperkernel, and consists of 245 complex specification generation tasks in total, each of which is a long-context task of about 20k-30k tokens. The benchmark formulates the specification generation task as a program synthesis problem confined to a domain for specifying states and transitions. This formulation is provided to LLMs through a programming model. The LLMs must be able to understand the programming model and verification assumptions before delineating the correct search space for syntax and semantics and generating formal specifications. Guided by the operating system's high-level functional description, the LLMs are asked to generate a specification that fully describes all correct states and transitions for a potentially buggy code implementation of the operating system. Experimental results with 12 state-of-the-art LLMs indicate limited performance of existing LLMs on the specification generation task for operating system verification. Significant disparities in their performance highlight differences in their ability to handle long-context code generation tasks. The code are available at https://github.com/lishangyu-hkust/OSVBench

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 23 of its 60 references that are in the map. First posted on arXiv 29 Apr 2025.

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23 of 60 references.

References · 23

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  3. 2021Program Synthesis with Large Language Modelscited by 3,637
  4. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,373
  5. 2023WizardCoder: Empowering Code Large Language Models with Evol-Instructcited by 733
  6. 2022A Survey on In-context Learningcited by 721
  7. 2024A Survey on Large Language Models for Code Generationcited by 658
  8. 2023Rethinking Benchmark and Contamination for Language Models with Rephrased Samplescited by 195
  9. 2023CrossCodeEval: A Diverse and Multilingual Benchmark for Cross-File Code Completioncited by 183
  10. 2022Multi-lingual Evaluation of Code Generation Modelscited by 159
  11. 2022Large Language Models Meet NL2Code: A Surveycited by 138
  12. 2023InferFix: End-to-End Program Repair with LLMscited by 114
11 more
  1. 2024IRIS: LLM-Assisted Static Analysis for Detecting Security Vulnerabilitiescited by 103
  2. 2023Automatic Semantic Augmentation of Language Model Prompts (for Code Summarization)cited by 72
  3. 2023Clover: Closed-Loop Verifiable Code Generationcited by 67
  4. 2024Towards AI-Assisted Synthesis of Verified Dafny Methodscited by 58
  5. 2024DafnyBench: A Benchmark for Formal Software Verificationcited by 57
  6. 2024Automated Proof Generation for Rust Code via Self-Evolutioncited by 33
  7. 2025EquiBench: Benchmarking Large Language Models' Reasoning about Program Semantics via Equivalence Checkingcited by 24
  8. 2024ComplexCodeEval: A Benchmark for Evaluating Large Code Models on More Complex Codecited by 19
  9. 2025CRUST-Bench: A Comprehensive Benchmark for C-to-safe-Rust Transpilationcited by 19
  10. 2025From Informal to Formal -- Incorporating and Evaluating LLMs on Natural Language Requirements to Verifiable Formal Proofscited by 18
  11. 2024Selene: Pioneering Automated Proof in Software Verificationcited by 12

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 6.

8 papers.