nanoMuse: An Open-Source Personal Agent for Every Device You Own
cs.AI
Submitted: 2026-10-06
Updated: 2026-10-06
Comments: 12 pages, 6 figures, 2 tables. Project page: https://nanomuse.cn ; Code: https://github.com/nano-muse/nanoMuse
Code: https://github.com/nano-muse/nanoMuse
Project page: https://nanomuse.cn
License: http://creativecommons.org/licenses/by/4.0/
The gist: Assistants from 2011 answered and waited, and agents from 2023 did a task and stopped.
Abstract
Assistants from 2011 answered and waited, and agents from 2023 did a task and stopped. In September 2026 Meta's Muse showed an agent for one person, with accounts, devices, memory and a conversation that lasts, closed, in a vendor's cloud, in one country. Such an agent is expected to act on a person's accounts and devices, remember them across weeks, speak first when it is worth it, and answer for what it did. It is a kind of software, not a model, and until now had no open counterpart. This report defines the personal agent in five questions and three horizons. It reads how Muse is built from Meta's public record and a copy of its production prompt, each statement marked by its source. It then presents nanoMuse, the open-source counterpart under the GPL-3.0, one agent on every device a person owns, with hands on the phone's screen and the computer's. They share one conversation over a relay anyone can run; every action goes through a Sentinel, memory is files the person can read, and the model is their choice. Its size and cost are given as estimates. What is open, memory with provenance, an evaluation suite for the hands and an open model for them, is set out as a roadmap.
Sources
- Mind2Web: Towards a Generalist Agent for the Web
- CogAgent: A Visual Language Model for GUI Agents
- OS-Harm: A Benchmark for Measuring Safety of Computer Use Agents
- ScreenSpot-Pro: GUI Grounding for Professional High-Resolution Computer Use
- LLM-Powered GUI Agents in Phone Automation: Surveying Progress and Prospects
- LearnAct: Few-Shot Mobile GUI Agent with a Unified Demonstration Benchmark
- MemGUI-Agent: An End-to-End Long-Horizon Mobile GUI Agent with Proactive Context Management
- MemGUI-Bench: Benchmarking Memory of Mobile GUI Agents in Dynamic Environments
- MobileForge: Annotation-Free Adaptation for Mobile GUI Agents with Hierarchical Feedback-Guided Policy Optimization
- AutoGLM: Autonomous Foundation Agents for GUIs
- UI-TARS: Pioneering Automated GUI Interaction with Native Agents
- AndroidWorld: A Dynamic Benchmarking Environment for Autonomous Agents
- Mobile-Agent: Autonomous Multi-Modal Mobile Device Agent with Visual Perception
- OpenCUA: Open Foundations for Computer-Use Agents
- OS-ATLAS: A Foundation Action Model for Generalist GUI Agents
- OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments
- Aguvis: Unified Pure Vision Agents for Autonomous GUI Interaction
- Mobile-Agent-v3: Fundamental Agents for GUI Automation
- AppAgent: Multimodal Agents as Smartphone Users
- MAS-Bench: A Unified Benchmark for Shortcut-Augmented Hybrid Mobile GUI Agents
Related papers
- MAVEN-T: Reinforced Heterogeneous Distillation for Real-Time Multi-Agent Trajectory Prediction
- Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models
- Agents' Last Exam
- The Clinician's Veto: Navigating Trust, Liability, and Uncertainty in Autonomous AI Prescribing
- MindHelper: Closed-Loop Embodied Mental-State Reasoning for Precision Intervention
- Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systems