Seeing Isn't Believing: Mitigating Belief Inertia via Active Intervention in Embodied Agents

2026-04-19

AI Summary

This work addresses the suboptimal decision-making of embodied agents caused by “belief inertia”—the tendency to cling to internal beliefs that contradict environmental feedback. To mitigate this issue, the authors propose an Estimate-Verify-Update (EVU) mechanism that explicitly externalizes belief states into textual form, establishing a closed-loop intervention process for prediction, verification, and dynamic belief updating. This enables agents to proactively correct erroneous beliefs through explicit reasoning. The EVU framework is designed to integrate seamlessly into both prompting-based and training-based agent architectures, leveraging large language models for explicit belief modeling and reasoning. Evaluated across three embodied task benchmarks, EVU significantly improves task success rates, effectively alleviates belief inertia, and enhances decision robustness.

Technology Category

• Knowledge Representation and Reasoning: Reasoning with Beliefs
• Multiagent Systems: Adversarial Agents
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search

Abstract

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. To address this, we advocate active belief intervention, moving from passive understanding to active management. We introduce the Estimate-Verify-Update (EVU) mechanism, which empowers agents to predict expected outcomes, verify them against observations through explicit reasoning, and actively update prior beliefs based on the verification evidence. EVU is designed as a unified intervention mechanism that generates textual belief states explicitly, and can be integrated into both prompting-based and training-based agent reasoning methods. Extensive experiments across three embodied benchmarks demonstrate that EVU consistently yields substantial gains in task success rates. Further analyses validate that our approach effectively mitigates belief inertia, advancing the development of more robust embodied agents. Our code is available at https://github.com/WangHanLinHenry/EVU.

Problem

• belief inertia
• embodied agents
• environmental feedback
• suboptimal decisions
• prior beliefs

Innovation

• belief inertia
• active belief intervention
• Estimate-Verify-Update
• embodied agents
• explicit reasoning