AI RESEARCH
Think Twice, Act Once: Verifier-Guided Action Selection For Embodied Agents
arXiv CS.AI
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ArXi:2605.12620v1 Announce Type: new Building generalist embodied agents capable of solving complex real-world tasks remains a fundamental challenge in AI. Multimodal Large Language Models (MLLMs) have significantly advanced the reasoning capabilities of such agents through strong vision-language knowledge and chain-of-thought (CoT) reasoning, yet remain brittle when faced with challenging out-of-distribution scenarios.