Mitigating Belief Inertia via Active Intervention in Embodied Agents
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.
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Computer Science > Computation and Language arXiv:2604.17252 (cs) [Submitted on 19 Apr 2026] Title:Seeing Isn't Believing: Mitigating Belief Inertia via Active Intervention in Embodied Agents Authors:Hanlin Wang, Chak Tou Leong, Jian Wang, Wenjie Li View a PDF of the paper titled Seeing Isn't Believing: Mitigating Belief Inertia via Active Intervention in Embodied Agents, by Hanlin Wang and 3 other authors View PDF HTML (experimental) 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 this https URL. Comments: Accepted by ACL2026 Fingdings Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Robotics (cs.RO) Cite as: arXiv:2604.17252 [cs.CL] (or arXiv:2604.17252v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2604.17252 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Hanlin Wang [view email] [v1] Sun, 19 Apr 2026 04:36:33 UTC (627 KB) Full-text links: Access Paper: View a PDF of the paper titled Seeing Isn't Believing: Mitigating Belief Inertia via Active Intervention in Embodied Agents, by Hanlin Wang and 3 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: cs.CL < prev | next > new | recent | 2026-04 Change to browse by: cs cs.AI cs.RO References & Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face…
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