Recent advances in Large Language Models (LLMs) have propelled intelligent agents from reactive responses to proactive support. While promising, existing proactive agents either rely exclusively on observations from enclosed environments (e.g., desktop UIs) with direct LLM inference or employ rule-based proactive notifications, leading to suboptimal user intent understanding and limited functionality for proactive service. In this paper, we introduce ContextAgent, the first context-aware proactive agent that incorporates extensive sensory contexts surrounding humans to enhance the proactivity of LLM agents. ContextAgent first extracts multi-dimensional contexts from massive sensory perceptions on wearables (e.g., video and audio) to understand user intentions. ContextAgent then leverages the sensory contexts and personas from historical data to predict the necessity for proactive services. When proactive assistance is needed, ContextAgent further automatically calls the necessary tools to assist users unobtrusively. To evaluate this new task, we curate ContextAgentBench, the first benchmark for evaluating context-aware proactive LLM agents, covering 1,000 samples across nine daily scenarios and twenty tools. Experiments on ContextAgentBench show that ContextAgent outperforms baselines by achieving up to 8.5% and 6.0% higher accuracy in proactive predictions and tool calling, respectively. We hope our research can inspire the development of more advanced, human-centric, proactive AI assistants. The code and dataset are publicly available at https://github.com/openaiotlab/ContextAgent.

An increasing number of environments, such as smart homes and factories, are being equipped with multiple sensor systems to enable diverse intelligent applications. However, most existing sensor coordination systems require manually predefined rules, limiting their ability to handle flexible and complex tasks. While recent approaches leverage large language models (LLMs) to interact with external APIs, they struggle to fully understand the capabilities and data dependencies of practical sensor systems. This paper introduces TaskSense, a novel system that coordinates multiple sensor systems in response to users' complex queries. TaskSense introduces a sensor language that automatically translates the capabilities and data dependencies of sensor systems into vocabularies and grammar rules that can be understood by LLMs. It then interprets user intentions into executable task plans for sensor systems using this sensor language in combination with LLMs. Meanwhile, TaskSense checks the solvability of user queries and verifies the correctness of task plan dependencies. To further enhance robustness, TaskSense incorporates a dynamic plan execution mechanism that adjusts plans based on real-time feedback from sensor data availability, data quality and execution results. TaskSense is deployed on real-world smart home systems, utilizing six popular LLMs. The system is evaluated across 4 scenarios involving 9 types of sensor systems, over 60 APIs, 170 tasks and 5 types of data modalities. Results show that TaskSense achieves up to 2× higher planning accuracy and a 75% increase in answer accuracy using the similar amount of tokens compared with baseline approaches.

The demand for fully on-device health monitoring is huge and urgent. However, deploying Large Foundation Models conventionally relies on cloud-based computing services, which poses privacy concerns. Driven by the belief of delivering personalised healthcare to family members, this study presents the development of an innovative on-device machine learning system, CaringFM. This family caring system utilizes privacy-protecting sensors and an edge-deployed Foundation Model(FM) to offer a convenient and low-cost solution for chronic disease prediction and health condition monitoring at home. In particular, CaringFM provides general health suggestions and personalized medical information while ensuring high privacy by processing and preserving all data locally.

Despite the extensive use of sensors enabling intelligent applications, the complementary potential of co-existing sensor systems is often not fully utilized, limiting more advanced applications. This paper introduces a novel solution using Large Language Models (LLMs) to coordinate sensor systems for handling complex user queries. It defines a sensor language for sensor systems, including vocabulary set and grammar rules, analogous to natural language components, enabling LLMs to translate user intentions into sensor coordination plans. Preliminary results show that our approach significantly outperforms the existing solution at plan generation, execution and response generation stages.

We present TensorBind, a novel approach aimed at unifying different hardware architectures for compilation optimization. Our proposed framework establishes an embedding space to seamlessly bind diverse hardware platforms together. By leveraging this unified representation, TensorBind enables efficient tensor program optimization techniques across a wide range of hardware platforms. We provide experimental results demonstrating the essentiality and adaptability of TensorBind in translating tensor program optimization records across multiple hardware architectures, thus revolutionizing compilation optimization strategies and facilitating the development of high-performance compilation systems over heterogeneous devices.