ESearch-R1: Advancing Interactive Embodied Search with Cost-Aware MLLM Agents

Research#Agent, Search🔬 Research|Analyzed: Jan 10, 2026 09:03•
Published: Dec 21, 2025 02:45
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1 min read
•ArXiv

Analysis

This research explores a novel application of Reinforcement Learning for developing cost-aware agents in the domain of embodied search. The focus on cost-efficiency within this context is a significant contribution, potentially leading to more practical and resource-efficient AI systems.
Reference / Citation
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"The research focuses on learning cost-aware MLLM agents."
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ArXivDec 21, 2025 02:45
* Cited for critical analysis under Article 32.
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Source: ArXiv