OSVBench: Benchmarking LLMs on Specification Generation Tasks for Operating System Verification
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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.
Builds on
23 of 60 references.
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11 more
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Citing papers per year: 2025: 2; 2026: 6.
8 papers.
- 2026ReflexiCoder: Teaching Large Language Models to Self-Reflect on Generated Code and Self-Correct It via Reinforcement Learningcited by 7
- 2025Agentic Specification Generator for Move Programscited by 3
- 2025SysMoBench: Evaluating AI on Formally Modeling Complex Real-World Systemscited by 2
- 2026WebGen-R1: Incentivizing Large Language Models to Generate Functional and Aesthetic Websites with Reinforcement Learningcited by 2
- 2026TAROT: Test-driven and Capability-adaptive Curriculum Reinforcement Fine-tuning for Code Generation with Large Language Modelscited by 1
- 2026CodePivot: Bootstrapping Multilingual Transpilation in LLMs via Reinforcement Learning without Parallel Corporacited by 1
- 2026BODHI: Precise OS Kernel Specification Inferencecited by 0
- 2026OSFoundry: Building and Evolving Operating Systems with Specification-Guided Agentscited by 0
- 2026OSFoundry: Building and Evolving Operating Systems with Specification-Guided Agents8 Aug 2026
- 2026BODHI: Precise OS Kernel Specification Inference22 Apr 2026
- 2026WebGen-R1: Incentivizing Large Language Models to Generate Functional and Aesthetic Websites with Reinforcement Learning22 Apr 2026
- 2026CodePivot: Bootstrapping Multilingual Transpilation in LLMs via Reinforcement Learning without Parallel Corpora20 Apr 2026
- 2026ReflexiCoder: Teaching Large Language Models to Self-Reflect on Generated Code and Self-Correct It via Reinforcement Learning6 Mar 2026
- 2026TAROT: Test-driven and Capability-adaptive Curriculum Reinforcement Fine-tuning for Code Generation with Large Language Models17 Feb 2026
- 2025Agentic Specification Generator for Move Programs29 Sep 2025
- 2025SysMoBench: Evaluating AI on Formally Modeling Complex Real-World Systems27 Sep 2025