Personalizing Embodied Multimodal Large Language Model Agents over Long-term User Interactions 事件

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 inter