activity
20222025
most citedInvestigate-Consolidate-Exploit: A General Strategy for Inter-Task Agent Self-Evolution

7 citations · 25 across the 15 of their papers we have counts for

collaborators
Showing cs.AIShow all

5 papers · 1 filter

cs.AI20253 cited

A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence

Huan-ang Gao, Jiayi Geng, Wenyue Hua +24

Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse tasks but remain fundamentally static, unable to adapt their internal parameters to novel task…

cs.AI20251 cited

Acting Less is Reasoning More! Teaching Model to Act Efficiently

Hongru Wang, Cheng Qian, Wanjun Zhong +7

Tool-integrated reasoning (TIR) augments large language models (LLMs) with the ability to invoke external tools during long-form reasoning, such as search engines and code interpre…

cs.AI2025

SMART: Self-Aware Agent for Tool Overuse Mitigation

Cheng Qian, Emre Can Acikgoz, Hongru Wang +5

Current Large Language Model (LLM) agents demonstrate strong reasoning and tool use capabilities, but often lack self-awareness, failing to balance these approaches effectively. Th…

cs.AI20251 cited

EmbodiedBench: Comprehensive Benchmarking Multi-modal Large Language Models for Vision-Driven Embodied Agents

Rui Yang, Hanyang Chen, Junyu Zhang +10

Leveraging Multi-modal Large Language Models (MLLMs) to create embodied agents offers a promising avenue for tackling real-world tasks. While language-centric embodied agents have…

cs.AI20241 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…