# ContextAgent: Context-Aware Proactive LLM Agents with Open-World Sensory Perceptions

arXiv: https://arxiv.org/abs/2505.14668 · HTML: https://mlsanity.com/paper/2505.14668/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use.
- Builds on 47 of its 61 references that are in the map.
- First posted on arXiv 20 May 2025.

## Placed in

- LLM agents and tool use / [Multimodal and embodied agents](https://mlsanity.com/agents/multimodal-and-embodied-agents/)

## Builds on (most cited first, 30 of 47)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [Qwen2.5-VL Technical Report](https://mlsanity.com/paper/2502.13923/) (2025)
- [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](https://mlsanity.com/paper/2204.01691/) (2022)
- [ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs](https://mlsanity.com/paper/2307.16789/) (2023)
- [SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering](https://mlsanity.com/paper/2405.15793/) (2024)
- [Mind2Web: Towards a Generalist Agent for the Web](https://mlsanity.com/paper/2306.06070/) (2023)
- [GAIA: a benchmark for General AI Assistants](https://mlsanity.com/paper/2311.12983/) (2023)
- [ExpeL: LLM Agents Are Experiential Learners](https://mlsanity.com/paper/2308.10144/) (2023)
- [SeeClick: Harnessing GUI Grounding for Advanced Visual GUI Agents](https://mlsanity.com/paper/2401.10935/) (2024)
- [GPT-4V(ision) is a Generalist Web Agent, if Grounded](https://mlsanity.com/paper/2401.01614/) (2024)
- [Scaling Synthetic Data Creation with 1,000,000,000 Personas](https://mlsanity.com/paper/2406.20094/) (2024)
- [AppAgent: Multimodal Agents as Smartphone Users](https://mlsanity.com/paper/2312.13771/) (2023)
- [CodeAgent: Enhancing Code Generation with Tool-Integrated Agent Systems for Real-World Repo-level Coding Challenges](https://mlsanity.com/paper/2401.07339/) (2024)
- [SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution](https://mlsanity.com/paper/2502.18449/) (2025)
- [A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Models](https://mlsanity.com/paper/2303.10420/) (2023)
- [ToolACE: Winning the Points of LLM Function Calling](https://mlsanity.com/paper/2409.00920/) (2024)
- [Chain of Agents: Large Language Models Collaborating on Long-Context Tasks](https://mlsanity.com/paper/2406.02818/) (2024)
- [ART: Automatic multi-step reasoning and tool-use for large language models](https://mlsanity.com/paper/2303.09014/) (2023)
- [AutoDroid: LLM-powered Task Automation in Android](https://mlsanity.com/paper/2308.15272/) (2023)
- [ProAgent: Building Proactive Cooperative Agents with Large Language Models](https://mlsanity.com/paper/2308.11339/) (2023)
- [Conversational Health Agents: A Personalized LLM-Powered Agent Framework](https://mlsanity.com/paper/2310.02374/) (2023)
- [AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML](https://mlsanity.com/paper/2410.02958/) (2024)

## Built on by (most cited first, 30 of 45)

- [SimpleMem: Efficient Lifelong Memory for LLM Agents](https://mlsanity.com/paper/2601.02553/) (2026)
- [PersonalAlign: Hierarchical Implicit Intent Alignment for Personalized GUI Agent with Long-Term User-Centric Records](https://mlsanity.com/paper/2601.09636/) (2026)
- [ProAgentBench: Evaluating LLM Agents for Proactive Assistance with Real-World Data](https://mlsanity.com/paper/2602.04482/) (2026)
- [Proactive Agent Research Environment: Simulating Active Users to Evaluate Proactive Assistants](https://mlsanity.com/paper/2604.00842/) (2026)
- [EgoForge: Goal-Directed Egocentric World Simulator](https://mlsanity.com/paper/2603.20169/) (2026)
- [Beyond Reactivity: Measuring Proactive Problem Solving in LLM Agents](https://mlsanity.com/paper/2510.19771/) (2025)
- [ProAgent: Harnessing On-Demand Sensory Contexts for Proactive LLM Agent Systems in the Wild](https://mlsanity.com/paper/2512.06721/) (2025)
- [PASK: Toward Intent-Aware Proactive Agents with Long-Term Memory](https://mlsanity.com/paper/2604.08000/) (2026)
- [ProactiveMobile: A Comprehensive Benchmark for Boosting Proactive Intelligence on Mobile Devices](https://mlsanity.com/paper/2602.21858/) (2026)
- [A Survey of Foundation Models for IoT: Taxonomy and Criteria-Based Analysis](https://mlsanity.com/paper/2506.12263/) (2025)
- [TriggerBench: Investigating Prospective Memory for Large Language Models](https://mlsanity.com/paper/2606.23459/) (2026)
- [Vinci2: Providing Proactive Assistance in Continuous Egocentric Videos](https://mlsanity.com/paper/2607.11523/) (2026)
- [ShadowNPU: System and Algorithm Co-design for NPU-Centric On-Device LLM Inference](https://mlsanity.com/paper/2508.16703/) (2025)
- [DuetUI: A Bidirectional Context Loop for Human-Agent Co-Generation of Task-Oriented Interfaces](https://mlsanity.com/paper/2509.13444/) (2025)
- [GUIDE: A Benchmark for Understanding and Assisting Users in Open-Ended GUI Tasks](https://mlsanity.com/paper/2603.25864/) (2026)
- [From Reactive to Proactive: Assessing the Proactivity of Voice Agents via ProVoice-Bench](https://mlsanity.com/paper/2604.15037/) (2026)
- [SpeechLess: Micro-utterance with Personalized Spatial Memory-aware Assistant in Everyday Augmented Reality](https://mlsanity.com/paper/2602.00793/) (2026)
- [ProAct: Harnessing Streaming Motion Generation and Agentic Reasoning for Real-Time Embodied Social Interaction](https://mlsanity.com/paper/2602.14048/) (2026)
- [Wearable Foundation Models Should Go Beyond Static Encoders](https://mlsanity.com/paper/2603.19564/) (2026)
- [Pro2Assist: Continuous Step-aware Proactive Assistance with Multi-modal Egocentric Perception for Long-horizon Procedural Tasks](https://mlsanity.com/paper/2605.04227/) (2026)
- [Agentic Coding Needs Proactivity, Not Just Autonomy](https://mlsanity.com/paper/2605.06717/) (2026)
- [Organizational Control Layer: Governance Infrastructure at the Execution Boundary of LLM Agent Systems](https://mlsanity.com/paper/2606.04306/) (2026)
- [Long-term Task-oriented Agent: Proactive Long-term Intent Maintenance in Dynamic Environments](https://mlsanity.com/paper/2601.09382/) (2026)
- [Do Proactive Agents Really Need an LLM to Decide When to Wake and What to Anchor?](https://mlsanity.com/paper/2605.30152/) (2026)
- [ProEvent: An Event-centric Benchmark for Proactive Agents](https://mlsanity.com/paper/2607.17701/) (2026)
- [Proactive Service Agents: A Unified Decision Framework, Methods, and Evaluation](https://mlsanity.com/paper/2609.03727/) (2026)
- [Redefining CX with Agentic AI: Minerva CQ Case Study](https://mlsanity.com/paper/2509.12589/) (2025)
- [Mixture of Lookup Key-Value Experts](https://mlsanity.com/paper/2512.09723/) (2025)
- [SensorPersona: An LLM-Empowered System for Continual Persona Extraction from Longitudinal Mobile Sensor Streams](https://mlsanity.com/paper/2604.06204/) (2026)
- [Help Without Being Asked: A Deployed Proactive Agent System for On-Call Support with Continuous Self-Improvement](https://mlsanity.com/paper/2604.09579/) (2026)

## Abstract (arXiv)

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.