Building an always-on, contextual AI assistant that proactively supports humans remains a central goal in Embodied AI—yet cloud-based pipelines struggle to meet due to delay, bandwidth, and privacy constraints. This demo presents EdgeMind-OS, a fully on-device intelligence system designed for embodied agents operating in real-world scenarios. Edge-Mind-OS features a hierarchical architecture combining a real-time StreamBrain, modular skill experts, and a dynamic scene-episode memory. Achieving up to 7.3× faster local processing, it enables low-latency, privacy-preserving, and plug-and-play deployment across tasks such as semantic navigation, spatial memory recall, and multimodal interaction. We demonstrate how EdgeMind-OS empowers a mobile robot with only basic locomotion capabilities to perform realtime, free-form user-robot interaction through autonomous perception, reasoning and action —without reliance on external cloud infrastructure.

Current models for multi-intent detection and slot-filling have made some progress, but often overlook the potential of rich syntactic information and rely on window mechanisms that capture only local slot interactions. In this paper, we propose a Multi-grained Syntactic Graph Interaction Model (MSGIM) for joint multi-intent detection and slot filling. The model has two main core components: (1) Multi-grained syntax module. This module utilizes syntactic dependency types and syntactic dependency distances between words, enabling the model to learn syntactic structure and semantic information more comprehensively. (2) Syntactic graph interaction layer. Specifically, we construct a syntactic slot-aware layer and a syntactic intent-slot interaction layer based on dependency tree. These layers can capture more distant slot dependencies and the interactions between intents and slots. Experimental results show that our model achieves a significant performance improvement, with an overall accuracy improvement of 4.4% on the MixATIS dataset compared to the previous best model.