3 papers
cs.CL2026
PEARL: Plan Exploration and Adaptive Reinforcement Learning for Multihop Tool Use
Qihao Wang, Mingzhe Lu, Jiayue Wu +2
Large Language Models show great potential with external tools, but face significant challenges in complex, multi-turn tool invocation. They often exhibit weak planning, tool hallu…
cs.CL2026
Beyond Accuracy: A Cognitive Load Framework for Mapping the Capability Boundaries of Tool-use Agents
Qihao Wang, Yue Hu, Mingzhe Lu +3
The ability of Large Language Models (LLMs) to use external tools unlocks powerful real-world interactions, making rigorous evaluation essential. However, current benchmarks primar…
cs.SE2026
MemGovern: Enhancing Code Agents through Learning from Governed Human Experiences
Qihao Wang, Ziming Cheng, Shuo Zhang +12
While autonomous software engineering (SWE) agents are reshaping programming paradigms, they currently suffer from a "closed-world" limitation: they attempt to fix bugs from scratc…