mlsanityData to 7 Oct 2026

X-OmniClaw Technical Report: A Unified Mobile Agent for Multimodal Understanding and Interaction

arXiv 2605.05765 ↗First posted 7 May 2026cs.CVcited by 1

On the map: LLM agents and tool use → GUI and mobile agents

Abstract (arXiv)

Inspired by the development of OpenClaw, there is a growing demand for mobile-based personal agents capable of handling complex and intuitive interactions. In this technical report, we introduce X-OmniClaw, a unified mobile agent designed for multimodal understanding and interaction in the Android ecosystem. This unified architecture of perception, memory, and action enables the agent to handle complex mobile tasks with high contextual awareness. Specifically, Omni Perception provides a unified multimodal ingress pipeline that integrates UI states, real-world visual contexts, and speech inputs, leveraging a temporal alignment module to decompose raw data into structured multimodal intent representations. Omni Memory leverages multimodal memory optimization to enhance personalized intelligence by integrating runtime working memory for task continuity with long-term personal memory distilled from local data, enabling highly context-aware and personalized interactions. Finally, Omni Action employs a hybrid grounding strategy that combines structural XML metadata with visual perception for robust interaction. Through Behavior Cloning and Trajectory Replay, the system captures user navigation as reusable skills, enabling precise direct-access execution. Demonstrations across diverse scenarios show that X-OmniClaw effectively enhances interaction efficiency and task reliability, providing a practical architectural blueprint for the next generation of mobile-native personal assistants.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in GUI and mobile agents, one of 16 directions in LLM agents and tool use. Builds on 11 of its 20 references that are in the map. First posted on arXiv 7 May 2026.

Builds on

11 of 20 references.

Closest ancestors · 6

Same direction, newest first

  1. 2025UI-TARS: Pioneering Automated GUI Interaction with Native Agents21 Jan
  2. 2024OS-ATLAS: A Foundation Action Model for Generalist GUI Agents30 Oct
  3. 2024DigiRL: Training In-The-Wild Device-Control Agents with Autonomous Reinforcement Learning14 Jun
  4. 2024OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments11 Apr
  5. 2024Mobile-Agent: Autonomous Multi-Modal Mobile Device Agent with Visual Perception29 Jan
  6. 2023AppAgent: Multimodal Agents as Smartphone Users21 Dec

Other references · 5

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  2. 2023Voyager: An Open-Ended Embodied Agent with Large Language Modelscited by 2,081
  3. 2023WebArena: A Realistic Web Environment for Building Autonomous Agentscited by 1,908
  4. 2024AndroidWorld: A Dynamic Benchmarking Environment for Autonomous Agentscited by 450
  5. 2023Android in the Wild: A Large-Scale Dataset for Android Device Controlcited by 314

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

Similar, not cited

Close in wording, no citation either way

  1. 2026OmniGUI: Benchmarking GUI Agents in Omni-Modal Smartphone Environments
  2. 2026Qwen3.8-Omni: Towards Native Omni-Modal Agents
  3. 2025Mobile-Agent-E: Self-Evolving Mobile Assistant for Complex Tasks
  4. 2026OmniGAIA: Towards Native Omni-Modal AI Agents
  5. 2025Omni-Captioner: Data Pipeline, Models, and Benchmark for Omni Detailed Perception
  6. 2025Agent-ScanKit: Unraveling Memory and Reasoning of Multimodal Agents via Sensitivity Perturbations
  7. 2025Agent-Omni: Test-Time Multimodal Reasoning via Model Coordination for Understanding Anything
  8. 2025See-Control: A Multimodal Agent Framework for Smartphone Interaction with a Robotic Arm