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Research#Agent, Search🔬 ResearchAnalyzed: Jan 10, 2026 09:03

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

Published:Dec 21, 2025 02:45
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

“The research focuses on learning cost-aware MLLM agents.”

PermalinkArXiv
Research#Search🔬 ResearchAnalyzed: Jan 10, 2026 13:17

GRPO Collapse: A Deep Dive into Search-R1's Failure Mode

Published:Dec 3, 2025 19:41
1 min read
ArXiv

Analysis

This article, sourced from ArXiv, likely details the failure of a specific AI model or technique (GRPO) within the context of search and ranking (Search-R1). The title's use of 'death spiral' suggests a critical vulnerability and potentially significant implications for system performance and reliability.
Reference

“The article's focus is on the failure of GRPO within the Search-R1 system.”

PermalinkArXiv