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20242026
most citedUnderstanding the planning of LLM agents: A survey

34 citations · 44 across the 23 of their papers we have counts for

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6 papers · 1 filter

cs.AI2026

What Makes Good Agentic Data? An ACE Lens on Data Generation for LLM Agents

Xingshan Zeng, Zishan Xu, Boju Zhang +11

LLM agents increasingly rely on generated interaction data to learn how to interact with external environments. Agentic data generation must maintain consistency among environments…

cs.AI2024★ 2 cited

GUI Agents with Foundation Models: A Comprehensive Survey

Shuai Wang, Weiwen Liu, Jingxuan Chen +12

Recent advances in foundation models, particularly Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs), have facilitated the development of intelligent agents…

cs.AI2024

SPA-Bench: A Comprehensive Benchmark for SmartPhone Agent Evaluation

Jingxuan Chen, Derek Yuen, Bin Xie +14

Smartphone agents are increasingly important for helping users control devices efficiently, with (Multimodal) Large Language Model (MLLM)-based approaches emerging as key contender…

cs.AI2024★ 1 cited

Proactive Agent: Shifting LLM Agents from Reactive Responses to Active Assistance

Yaxi Lu, Shenzhi Yang, Cheng Qian +12

Agents powered by large language models have shown remarkable abilities in solving complex tasks. However, most agent systems remain reactive, limiting their effectiveness in scena…

cs.AI2024★ 2 cited

WESE: Weak Exploration to Strong Exploitation for LLM Agents

Xu Huang, Weiwen Liu, Xiaolong Chen +5

Recently, large language models (LLMs) have demonstrated remarkable potential as an intelligent agent. However, existing researches mainly focus on enhancing the agent's reasoning…

cs.AI2024★ 34 cited

Understanding the planning of LLM agents: A survey

Xu Huang, Weiwen Liu, Xiaolong Chen +6

As Large Language Models (LLMs) have shown significant intelligence, the progress to leverage LLMs as planning modules of autonomous agents has attracted more attention. This surve…