mlsanityData to 7 Oct 2026

Prompt-to-OS (P2OS): Revolutionizing Operating Systems and Human-Computer Interaction with Integrated AI Generative Models

arXiv 2310.04875 ↗First posted 7 Oct 2023cs.LG cs.CL cs.CYcited by 3

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Abstract (arXiv)

In this paper, we present a groundbreaking paradigm for human-computer interaction that revolutionizes the traditional notion of an operating system. Within this innovative framework, user requests issued to the machine are handled by an interconnected ecosystem of generative AI models that seamlessly integrate with or even replace traditional software applications. At the core of this paradigm shift are large generative models, such as language and diffusion models, which serve as the central interface between users and computers. This pioneering approach leverages the abilities of advanced language models, empowering users to engage in natural language conversations with their computing devices. Users can articulate their intentions, tasks, and inquiries directly to the system, eliminating the need for explicit commands or complex navigation. The language model comprehends and interprets the user's prompts, generating and displaying contextual and meaningful responses that facilitate seamless and intuitive interactions. This paradigm shift not only streamlines user interactions but also opens up new possibilities for personalized experiences. Generative models can adapt to individual preferences, learning from user input and continuously improving their understanding and response generation. Furthermore, it enables enhanced accessibility, as users can interact with the system using speech or text, accommodating diverse communication preferences. However, this visionary concept raises significant challenges, including privacy, security, trustability, and the ethical use of generative models. Robust safeguards must be in place to protect user data and prevent potential misuse or manipulation of the language model. While the full realization of this paradigm is still far from being achieved, this paper serves as a starting point for envisioning this transformative potential.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 24 of its 42 references that are in the map. First posted on arXiv 7 Oct 2023.

Builds on

24 of 42 references.

References · 24

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2020Language Models are Few-Shot Learnerscited by 32,199
  4. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  5. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  6. 2018GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understandingcited by 4,854
  7. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
  8. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,383
  9. 2023Gorilla: Large Language Model Connected with Massive APIscited by 1,302
  10. 2021Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivitycited by 1,122
  11. 2022AudioLM: a Language Modeling Approach to Audio Generationcited by 611
  12. 2020Jukebox: A Generative Model for Musiccited by 535
12 more
  1. 2021MViTv2: Improved Multiscale Vision Transformers for Classification and Detectioncited by 516
  2. 2023Augmented Language Models: a Surveycited by 398
  3. 2023VideoFusion: Decomposed Diffusion Models for High-Quality Video Generationcited by 330
  4. 2023How Language Model Hallucinations Can Snowballcited by 310
  5. 2018Adversarial Attacks and Defences: A Surveycited by 259
  6. 2023SceneDreamer: Unbounded 3D Scene Generation from 2D Image Collectionscited by 72
  7. 2021Shape registration in the time of transformerscited by 51
  8. 2021Database Reasoning Over Textcited by 20
  9. 2023Boosting GUI Prototyping with Diffusion Modelscited by 10
  10. 2023Multimodal Neural Databasescited by 7
  11. 2022Sparse Vicious Attacks on Graph Neural Networkscited by 5
  12. 2021CycleDRUMS: Automatic Drum Arrangement For Bass Lines Using CycleGANcited by 4

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  2. 2025Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy
  3. 2025Design and Evaluation of Generative Agent-based Platform for Human-Assistant Interaction Research: A Tale of 10 User Studies
  4. 2024Building Living Software Systems with Generative & Agentic AI
  5. 2026Synthetic Interaction Data for Scalable Personalization in Large Language Models
  6. 2023Machine Psychology
  7. 2024OSCAR: Operating System Control via State-Aware Reasoning and Re-Planning
  8. 2024Agent S: An Open Agentic Framework that Uses Computers Like a Human