Personalizing Embodied Multimodal Large Language Model Agents over Long-term User Interactions
arXiv:2605.26256v1 Announce Type: new Abstract: Multimodal large language model (MLLM)-based embodied agents have shown strong potential for solving complex tasks in physical environments. However, personalized assistance requires more than following generic instruction or recognizing object categories. In real-world scenarios, the intended target is often specified only implicitly through prior interactions, requiring agents to leverage personalized context accumulated over time. In this work, we propose POLAR, a multiomodal memory-augmented framework for personalized embodied agents over lon
The increased sophistication of MLLMs and the growing demand for more nuanced and adaptable AI in real-world scenarios are driving this development.
This work directly addresses a critical hurdle for AI agents: the ability to personalize interactions and leverage long-term context, moving beyond generic instruction.
AI agents will become significantly more effective and context-aware in dynamic environments, enabling deeper integration into personalized user workflows.
- · AI platform developers
- · Robotics companies
- · Personal assistant service providers
- · Generic instructional AI
- · Task-specific rigid automation
Embodied MLLM agents will be able to perform complex tasks with implicit instructions based on user history.
This personalization will accelerate the adoption of AI agents in both consumer and enterprise settings, reducing the need for explicit user intervention.
The increased autonomy and personalization of agents could lead to new forms of human-AI collaboration and redefine workflows across various industries.
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Read at arXiv cs.AI