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

2026-05-07

AI Summary

This work addresses the limitations of existing mobile agents in complex multimodal interactions, which often lack integrated perception, memory, and action capabilities necessary for context-aware, personalized task execution. To bridge this gap, the paper introduces OmniAgent, a unified agent architecture tailored for the Android ecosystem. OmniAgent features a tripartite design—Omni Perception, Omni Memory, and Omni Action—that enables, for the first time on mobile devices, structured multimodal intent representation, fusion of runtime and long-term memory, and a hybrid action grounding mechanism leveraging both XML semantics and visual inputs. By incorporating multimodal temporal alignment, on-device data distillation, and behavior cloning, the proposed architecture significantly enhances task success rates and interaction efficiency across diverse scenarios, offering a practical and scalable paradigm for next-generation native mobile personal assistants.

Abstract

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.

Problem

• mobile agent
• multimodal understanding
• context-aware interaction
• personalized intelligence
• Android ecosystem

Innovation

• multimodal understanding
• unified mobile agent
• context-aware interaction
• behavior cloning
• personalized memory