1 citations · 2 across the 6 of their papers we have counts for
8 papers
PhysCodeGym: Benchmarking Coding Agents for Physically Verifiable 4D Reconstruction
Shuai Shao, Jinchen He, Cunxin Fan +10
Building physical simulations from video requires coding agents to translate observations into executable models and refine them through tool use. We introduce PhysCodeGym, a bench…
Harness-R1: Learning to Edit Executable Runtime Harnesses from Agent Failure Trajectories
Shuai Shao, Kangning Zhang, Qingyao Li +7
Agents built around large language models continually accumulate interaction trajectories during deployment, yet their behavior typically remains fixed. Beyond updating model weigh…
SOLAR-RL: Semi-Online Long-horizon Assignment Reinforcement Learning
Jichao Wang, Liuyang Bian, Yufeng Zhou +9
As Multimodal Large Language Models (MLLMs) mature, GUI agents are evolving from static interactions to complex navigation. While Reinforcement Learning (RL) has emerged as a promi…
UI-Mem: Self-Evolving Experience Memory for Online Reinforcement Learning in Mobile GUI Agents
Han Xiao, Guozhi Wang, Hao Wang +7
Online Reinforcement Learning (RL) offers a promising paradigm for enhancing GUI agents through direct environment interaction. However, its effectiveness is severely hindered by i…
MemGUI-Bench: Benchmarking Memory of Mobile GUI Agents in Dynamic Environments
Guangyi Liu, Pengxiang Zhao, Yaozhen Liang +12
Reliable mobile GUI agents must retain and reuse information across actions, applications, and repeated interactions. However, current benchmarks systematically underrepresent thes…
MAS-Bench: A Unified Benchmark for Shortcut-Augmented Hybrid Mobile GUI Agents
Pengxiang Zhao, Guangyi Liu, YaoZhen Liang +11
Shortcuts such as APIs and deep-links have emerged as efficient complements to flexible GUI operations, fostering a promising hybrid paradigm for MLLM-based mobile automation. Howe…
LearnAct: Few-Shot Mobile GUI Agent with a Unified Demonstration Benchmark
Guangyi Liu, Pengxiang Zhao, Liang Liu +6
Mobile GUI agents show promise in automating tasks but face generalization challenges in diverse real-world scenarios. Traditional approaches using pre-training or fine-tuning with…
UI-R1: Enhancing Efficient Action Prediction of GUI Agents by Reinforcement Learning
Zhengxi Lu, Yuxiang Chai, Yaxuan Guo +7
The recent DeepSeek-R1 has showcased the emergence of reasoning capabilities in LLMs through reinforcement learning (RL) with rule-based rewards. Despite its success in language mo…