LLM-Driven Kernel Evolution: Automating Driver Updates in Linux
- URL: http://arxiv.org/abs/2511.18924v1
- Date: Mon, 24 Nov 2025 09:31:52 GMT
- Title: LLM-Driven Kernel Evolution: Automating Driver Updates in Linux
- Abstract summary: Linux kernel evolution breaks drivers through API/ABI changes, semantic shifts, and security-hardening updates.<n>We introduce DRIVEBENCH, an executable corpus of kernel$rightarrow$driver co-evolution cases, and AUTODRIVER, a closed-loop, LLM-driven system for automating driver maintenance.
- Score: 19.111608041629008
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Linux kernel evolution breaks drivers through API/ABI changes, semantic shifts, and security-hardening updates. We introduce DRIVEBENCH, an executable corpus of kernel$\rightarrow$driver co-evolution cases, and AUTODRIVER, a closed-loop, LLM-driven system for automating driver maintenance. The system integrates prompt engineering, multi-agent collaboration, static analysis, and iterative validation to ensure that generated patches are not only syntactically correct but also functionally and semantically consistent with kernel conventions. The corpus spans v5.10-v6.10 with 235 validated cases drawn from 612 candidates. In evaluation across 55 cases, AUTODRIVER achieves 56.4% compilation success; QEMU-based boot verification indicates that compiled patches preserve driver initialization in most instances. By releasing DRIVEBENCH and tooling, we enable reproducible research and a practical route to continuous, safe co-evolution of drivers with the Linux kernel.
Related papers
- Harness Engineering for LLM-Driven GPU Kernel Generation [1.375573703671189]
Large language models (LLMs) can assist GPU kernel generation, but their practical effectiveness depends on whether generated code can be reliably constrained, validated, profiled, and selected.<n>This paper presents a harness-centered system for LLM-driven GPU kernel optimization in the MLSys 2026 FlashInfer AI Kernel Generation Contest.
arXiv Detail & Related papers (2026-07-20T14:14:14Z) - Automatic Detection of Reference Counting Bugs in Linux Kernel Drivers [1.3075880857448061]
We introduce DrvHorn, a novel automated tool to detect reference counting bugs in Linux kernel drivers.<n>DrvHorn discovered 545 bugs, of which 424 were previously unknown, across all platform drivers in v6.6 Linux kernel.<n>To address the root causes of these newly discovered bugs, we submitted patches to the Linux kernel, and 45 of them were merged.
arXiv Detail & Related papers (2026-05-13T09:31:08Z) - NeuroClaw Technical Report [54.14941057366186]
NeuroClaw is a domain-specialized multi-agent research assistant for neuroimaging research.<n>NeuroClaw operates directly on raw neuroimaging data across formats and modalities.<n>A three-tier skill/agent hierarchy separates user-facing interaction, high-level orchestration, and low-level tool skills.
arXiv Detail & Related papers (2026-04-27T16:57:14Z) - KISS Sorcar: A Stupidly-Simple General-Purpose and Software Engineering AI Assistant [8.273738980475393]
KISS Sorcar is a general-purpose assistant and integrated development environment built on top of the KISS Agent Framework.<n> KISS Sorcar is a free, open-source Visual Studio Code extension that runs locally and effectively for long-horizon tasks.<n>The entire system was built using itself in 4.5 months, providing a continuous stress test in which any agent-introduced bug immediately impairs its own ability to work.
arXiv Detail & Related papers (2026-04-26T17:59:06Z) - OneDrive: Unified Multi-Paradigm Driving with Vision-Language-Action Models [69.2503510410147]
We present a unified autonomous driving framework built upon a pretrained VLM.<n>We demonstrate that pretrained VLM attention exhibits strong transferability beyond pure language modeling.<n>Experiments on end-to-end autonomous driving benchmarks demonstrate state-of-the-art performance.
arXiv Detail & Related papers (2026-04-20T07:50:00Z) - Beyond Crash-to-Patch: Patch Evolution for Linux Kernel Repair [2.267311896805228]
Linux kernel fixes undergo iterative revision on mailing lists before acceptance, with reviewer feedback shaping correctness, handling, and API compliance.<n>We present a large-scale study of kernel patch evolution, reconstructing 6946 syzbot-linked bug-fix lifecycles.<n>We develop PatchAdvisor, a repair framework that integrates retrieval-based memory with a fine-tuned diagnostic advisor to guide a coding agent toward reviewer-aligned patches.
arXiv Detail & Related papers (2026-04-04T20:23:32Z) - Bench2Drive-VL: Benchmarks for Closed-Loop Autonomous Driving with Vision-Language Models [50.22099309218635]
In autonomous driving, closed-loop evaluation has become widely recognized as a more reliable validation method than open-loop evaluation.<n>We present Bench2Drive-VL, an extension of Bench2Drive that brings closed-loop evaluation to VLM-based driving.
arXiv Detail & Related papers (2026-04-01T11:38:46Z) - Towards Cold-Start Drafting and Continual Refining: A Value-Driven Memory Approach with Application to NPU Kernel Synthesis [68.7701048879757]
EvoKernel is a self-evolving agentic framework that automates the lifecycle of kernel synthesis.<n>It learns stage-specific Q-values that prioritize experiences based on their contribution to the current objective.<n>It improves models' correctness from 11.0% to 83.0% and a median speedup of 3.60x over initial drafts.
arXiv Detail & Related papers (2026-03-11T14:57:06Z) - Outrunning LLM Cutoffs: A Live Kernel Crash Resolution Benchmark for All [57.23434868678603]
Live-kBench is an evaluation framework for self-evolving benchmarks that scrapes and evaluates agents on freshly discovered kernel bugs.<n> kEnv is an agent-agnostic crash-resolution environment for kernel compilation, execution, and feedback.<n>Using kEnv, we benchmark three state-of-the-art agents, showing that they resolve 74% of crashes on the first attempt.
arXiv Detail & Related papers (2026-02-02T19:06:15Z) - AgentDrive: An Open Benchmark Dataset for Agentic AI Reasoning with LLM-Generated Scenarios in Autonomous Systems [3.099103925863002]
This paper introduces AgentDrive, an open benchmark dataset containing 300,000 driving scenarios.<n>AgentDrive formalizes a factorized scenario space across seven axes: scenario type, driver behavior, environment, road layout, objective, difficulty, and traffic density.<n>To complement simulation-based evaluation, we introduce AgentDrive-MCQ, a 100,000-question multiple-choice benchmark spanning five reasoning dimensions.
arXiv Detail & Related papers (2026-01-23T18:33:41Z) - VibeTensor: System Software for Deep Learning, Fully Generated by AI Agents [42.56489784841984]
"fully generated" refers to code provenance: implementation changes were produced and applied as agent-proposed diffs.<n>We describe the architecture, summarize the workflow used to produce and validate the system, and evaluate the artifact.
arXiv Detail & Related papers (2026-01-21T19:29:00Z) - ProofWright: Towards Agentic Formal Verification of CUDA [1.8946520856889382]
Large Language Models (LLMs) are increasingly used to automatically generate optimized kernels.<n>Despite rapid generation, these kernels often contain subtle correctness bugs and lack formal safety guarantees.<n>We present ProofWright, an agentic verification framework that bridges this gap by integrating automated formal verification with LLM-based code generation.
arXiv Detail & Related papers (2025-11-15T17:06:50Z) - dParallel: Learnable Parallel Decoding for dLLMs [77.24184219948337]
Diffusion large language models (dLLMs) offer parallel token prediction and lower inference latency.<n>Existing open-source models still require nearly token-length decoding steps to ensure performance.<n>We introduce dParallel, a simple and effective method that unlocks the inherent parallelism of dLLMs for fast sampling.
arXiv Detail & Related papers (2025-09-30T16:32:52Z) - CrashFixer: A crash resolution agent for the Linux kernel [58.152358195983155]
This work builds upon kGym, which shares a benchmark for system-level Linux kernel bugs and a platform to run experiments on the Linux kernel.<n>This paper introduces CrashFixer, the first LLM-based software repair agent that is applicable to Linux kernel bugs.
arXiv Detail & Related papers (2025-04-29T04:18:51Z) - MigGPT: Harnessing Large Language Models for Automated Migration of Out-of-Tree Linux Kernel Patches Across Versions [53.811953357289866]
Large language models (LLMs) have shown remarkable progress across various domains.<n>LLMs struggle with incomplete code context understanding and inaccurate migration point identification.<n>MigGPT is a framework that employs a novel code fingerprint structure to retain code snippet information.
arXiv Detail & Related papers (2025-04-13T08:08:37Z) - KNighter: Transforming Static Analysis with LLM-Synthesized Checkers [20.368621145118468]
KNighter generates high-precision checkers capable of detecting diverse bug patterns.<n>To date, KNighter-synthesized checkers have discovered 92 new, critical, long-latent bugs in the Linux kernel.
arXiv Detail & Related papers (2025-03-12T02:30:19Z) - KGym: A Platform and Dataset to Benchmark Large Language Models on Linux Kernel Crash Resolution [59.20933707301566]
Large Language Models (LLMs) are consistently improving at increasingly realistic software engineering (SE) tasks.
In real-world software stacks, significant SE effort is spent developing foundational system software like the Linux kernel.
To evaluate if ML models are useful while developing such large-scale systems-level software, we introduce kGym and kBench.
arXiv Detail & Related papers (2024-07-02T21:44:22Z) - Kernel Identification Through Transformers [54.3795894579111]
Kernel selection plays a central role in determining the performance of Gaussian Process (GP) models.
This work addresses the challenge of constructing custom kernel functions for high-dimensional GP regression models.
We introduce a novel approach named KITT: Kernel Identification Through Transformers.
arXiv Detail & Related papers (2021-06-15T14:32:38Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.