A Journey of Modern OS Construction From boot to DOOM

• URL: http://arxiv.org/abs/2504.17984v1
• Date: Thu, 24 Apr 2025 23:46:28 GMT
• Title: A Journey of Modern OS Construction From boot to DOOM
• Authors: Wonkyo Choe, Rongxiang Wang, Afsara Benazir, Felix Xiaozhu Lin,
• Abstract summary: VOS is a first-of-its-kind instructional OS that runs on commodity, portable hardware.<n>Our method, which we call inverse engineering, breaks down a full-featured OS into a set of incremental, self-contained prototypes.
• Score: 1.0399614883374284
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: VOS is a first-of-its-kind instructional OS that: (1) Runs on commodity, portable hardware. (2) Showcases modern features, including per-app address spaces, threading, commodity filesystems, USB, DMA, multicore, self-hosted debugging, and a window manager. (3) Supports rich applications such as 2D/3D games, music and video players, and a blockchain miner. Unlike traditional instructional systems, VOS emphasizes strong motivation for building systems-supporting engaging, media-rich apps that go beyond basic terminal programs. To achieve this, we design VOS to strike a careful balance between essential OS complexity and overall simplicity. Our method, which we call inverse engineering, breaks down a full-featured OS into a set of incremental, self-contained prototypes. Each prototype introduces a minimal set of OS mechanisms, driven by the needs of specific apps. The construction process (i.e., forward engineering) then progressively enables these apps by bringing up one mechanism at a time. VOS makes it accessible for a wider audience to experience building a software system that is self-contained and usable in everyday scenarios.

Related papers

• UFO2: The Desktop AgentOS [60.317812905300336]
  UFO2 is a multiagent AgentOS for Windows desktops that elevates into practical, system-level automation. We evaluate UFO2 across over 20 real-world Windows applications, demonstrating substantial improvements in robustness and execution accuracy over prior CUAs. Our results show that deep OS integration unlocks a scalable path toward reliable, user-aligned desktop automation.
  arXiv  Detail & Related papers  (2025-04-20T13:04:43Z)
• OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments [87.41051677852231]
  We introduce OSWorld, the first-of-its-kind scalable, real computer environment for multimodal agents. OSWorld can serve as a unified, integrated computer environment for assessing open-ended computer tasks. We create a benchmark of 369 computer tasks involving real web and desktop apps in open domains, OS file I/O, and spanning multiple applications.
  arXiv  Detail & Related papers  (2024-04-11T17:56:05Z)
• CARLOS: An Open, Modular, and Scalable Simulation Framework for the Development and Testing of Software for C-ITS [0.0]
  We propose CARLOS - an open, modular, and scalable simulation framework for the development and testing of software in C-ITS. We provide core building blocks for this framework and explain how it can be used and extended by the community. In our paper, we motivate the architecture by describing important design principles and showcasing three major use cases.
  arXiv  Detail & Related papers  (2024-04-02T10:48:36Z)
• PerOS: Personalized Self-Adapting Operating Systems in the Cloud [0.0]
  This work proposes PerOS, a personalized OS with large language models (LLMs) capabilities. PerOS aims to provide tailored user experiences while safeguarding privacy and personal data through declarative interfaces, self-adaptive kernels, and secure data management in a scalable cloud-centric architecture.
  arXiv  Detail & Related papers  (2024-03-26T20:10:31Z)
• OS-Copilot: Towards Generalist Computer Agents with Self-Improvement [48.29860831901484]
  We introduce OS-Copilot, a framework to build generalist agents capable of interfacing with comprehensive elements in an operating system (OS) We use OS-Copilot to create FRIDAY, a self-improving embodied agent for automating general computer tasks. On GAIA, a general AI assistants benchmark, FRIDAY outperforms previous methods by 35%, showcasing strong generalization to unseen applications via accumulated skills from previous tasks.
  arXiv  Detail & Related papers  (2024-02-12T07:29:22Z)
• LLM as OS, Agents as Apps: Envisioning AIOS, Agents and the AIOS-Agent Ecosystem [48.81136793994758]
  Large Language Model (LLM) serves as the (Artificial) Intelligent Operating System (IOS), or AIOS--an operating system "with soul" We envision that LLM's impact will not be limited to the AI application level, instead, it will in turn revolutionize the design and implementation of computer system, architecture, software, and programming language.
  arXiv  Detail & Related papers  (2023-12-06T18:50:26Z)
• Harnessing Deep Learning and HPC Kernels via High-Level Loop and Tensor Abstractions on CPU Architectures [67.47328776279204]
  This work introduces a framework to develop efficient, portable Deep Learning and High Performance Computing kernels. We decompose the kernel development in two steps: 1) Expressing the computational core using Processing Primitives (TPPs) and 2) Expressing the logical loops around TPPs in a high-level, declarative fashion. We demonstrate the efficacy of our approach using standalone kernels and end-to-end workloads that outperform state-of-the-art implementations on diverse CPU platforms.
  arXiv  Detail & Related papers  (2023-04-25T05:04:44Z)
• MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge [70.47759528596711]
  We introduce MineDojo, a new framework built on the popular Minecraft game. We propose a novel agent learning algorithm that leverages large pre-trained video-language models as a learned reward function. Our agent is able to solve a variety of open-ended tasks specified in free-form language without any manually designed dense shaping reward.
  arXiv  Detail & Related papers  (2022-06-17T15:53:05Z)
• A Scalable Approach to Modeling on Accelerated Neuromorphic Hardware [0.0]
  This work presents the software aspects of the BrainScaleS-2 system, a hybrid accelerated neuromorphic hardware architecture based on physical modeling. We introduce key aspects of the BrainScaleS-2 Operating System: experiment workflow, API layering, software design, and platform operation. The focus lies on novel system and software features such as multi-compartmental neurons, fast re-configuration for hardware-in-the-loop training, applications for the embedded processors, the non-spiking operation mode, interactive platform access, and sustainable hardware/software co-development.
  arXiv  Detail & Related papers  (2022-03-21T16:30:18Z)
• Artificial Intelligence in the Low-Level Realm -- A Survey [0.0]
  We seek methods and efforts that exploit AI approaches, specifically machine learning, in the OSes' primary responsibilities. In other words, the main question to be answered is how AI has played/can play a role directly in improving the traditional OS kernel main tasks.
  arXiv  Detail & Related papers  (2021-09-19T19:36:54Z)

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.