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Research#Agent🔬 ResearchAnalyzed: Jan 10, 2026 11:25

Benchmarking Mobile GUI Agents: A Modular and Multi-Path Approach

Published:Dec 14, 2025 10:41
1 min read
ArXiv

Analysis

This research focuses on improving the evaluation of mobile GUI agents, crucial for advancing AI's interaction with mobile devices. The modular and multi-path approach likely addresses limitations of existing benchmarking methods, paving the way for more robust and reliable agent performance assessments.
Reference

“The article is sourced from ArXiv, indicating it's a pre-print of a research paper.”

PermalinkArXiv
Research#LLM Agents🔬 ResearchAnalyzed: Jan 10, 2026 13:34

Benchmarking LLM Agents in Wealth Management: A Performance Analysis

Published:Dec 1, 2025 21:56
1 min read
ArXiv

Analysis

This research from ArXiv likely investigates the performance of Large Language Model (LLM) agents in automating or assisting wealth management tasks. The study's focus on benchmarking suggests an attempt to quantify and compare the effectiveness of different LLM agent implementations within this domain.
Reference

“The study focuses on wealth-management workflows.”

PermalinkArXiv
Research#agent🔬 ResearchAnalyzed: Jan 10, 2026 14:17

Evo-Memory: Benchmarking LLM Agent Test-time Learning

Published:Nov 25, 2025 21:08
1 min read
ArXiv

Analysis

This article from ArXiv introduces Evo-Memory, a new benchmark for evaluating Large Language Model (LLM) agents' ability to learn during the testing phase. The focus on self-evolving memory offers potential advancements in agent adaptability and performance.
Reference

“Evo-Memory is a benchmarking framework.”

PermalinkArXiv