most citedMemento: Fine-tuning LLM Agents without Fine-tuning LLMs

2 citations · 2 across the 3 of their papers we have counts for

collaborators

5 papers

cs.AI2025

See-Control: A Multimodal Agent Framework for Smartphone Interaction with a Robotic Arm

Haoyu Zhao, Weizhong Ding, Yuhao Yang +4

Recent advances in Multimodal Large Language Models (MLLMs) have enabled their use as intelligent agents for smartphone operation. However, existing methods depend on the Android D…

cs.AI2025

Probing the "Psyche'' of Large Reasoning Models: Understanding Through a Human Lens

Yuxiang Chen, Zuohan Wu, Ziwei Wang +6

Large reasoning models (LRMs) have garnered significant attention from researchers owing to their exceptional capability in addressing complex tasks. Motivated by the observed huma…

cs.LG20252 cited

Memento: Fine-tuning LLM Agents without Fine-tuning LLMs

Huichi Zhou, Yihang Chen, Siyuan Guo +8

In this paper, we introduce a novel learning paradigm for Adaptive Large Language Model (LLM) agents that eliminates the need for fine-tuning the underlying LLMs. Existing approach…

cs.AI2025

Deep Research Agents: A Systematic Examination And Roadmap

Yuxuan Huang, Yihang Chen, Haozheng Zhang +10

The rapid progress of Large Language Models (LLMs) has given rise to a new category of autonomous AI systems, referred to as Deep Research (DR) agents. These agents are designed to…

cs.CL2025

Causal Sufficiency and Necessity Improves Chain-of-Thought Reasoning

Xiangning Yu, Zhuohan Wang, Linyi Yang +5

Chain-of-Thought (CoT) prompting plays an indispensable role in endowing large language models (LLMs) with complex reasoning capabilities. However, CoT currently faces two fundamen…