LATIDIA · Investigación

Hacia una IA física autoadaptativa: ¿Pueden los agentes de LLM gestionar tareas físicas a largo plazo?

arXiv: 2609.13436v1Tipo de anuncio: nuevo Resumen: los agentes del Modelo de Lenguaje Grande (LLM) ofrecen un camino prometedor hacia la gestión autónoma de tareas físicas a largo plazo sin intervención humana. Sin embargo, las tareas físicas requieren una

La noticia

arXiv:2609.13436v1 Announce Type: new Abstract: Large Language Model (LLM) agents offer a promising path toward autonomously managing long-term physical tasks without human intervention. However, physical tasks require agents to continuously observe the environment, make consequential actions, and remain effective as the environment changes. Existing approaches either require substantial data and retraining, or primarily focus on agents operating in the virtual world. In this work, we explore the feasibility of building a self-adaptive physical AI agent that manages long-term physical tasks in a zero-shot manner and adapts to environmental changes without human intervention. We design a multi-agent framework that integrates planning, tool calling, observation, and verification, and evaluate it on agricultural