AI RESEARCH
Seeing Isn't Believing: Mitigating Belief Inertia via Active Intervention in Embodied Agents
arXiv CS.CL
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ArXi:2604.17252v1 Announce Type: new Recent advancements in large language models (LLMs) have enabled agents to tackle complex embodied tasks through environmental interaction. However, these agents still make suboptimal decisions and perform ineffective actions, as they often overlook critical environmental feedback that differs from their internal beliefs. Through a formal probing analysis, we characterize this as belief inertia, a phenomenon where agents stubbornly adhere to prior beliefs despite explicit observations.