6 citations · 21 across the 38 of their papers we have counts for
13 papers · 1 filter
Learning GUI Grounding with Spatial Reasoning from Visual Feedback
Yu Zhao, Wei-Ning Chen, Huseyin Atahan Inan +8
Graphical User Interface (GUI) grounding is commonly framed as a coordinate prediction task -- given a natural language instruction, generate on-screen coordinates for actions such…
Learning to Refine: Self-Refinement of Parallel Reasoning in LLMs
Qibin Wang, Pu Zhao, Shaohan Huang +6
Test-time scaling (TTS) has gained widespread attention for enhancing LLM reasoning. Existing approaches such as Best-of-N and majority voting are limited as their performance depe…
AdaptFlow: Adaptive Workflow Optimization via Meta-Learning
Runchuan Zhu, Bowen Jiang, Lingrui Mei +8
Recent advances in large language models (LLMs) have sparked growing interest in agentic workflows, which are structured sequences of LLM invocations intended to solve complex task…
WarriorMath: Enhancing the Mathematical Ability of Large Language Models with a Defect-aware Framework
Yue Chen, Minghua He, Fangkai Yang +9
Large Language Models (LLMs) excel in solving mathematical problems, yet their performance is often limited by the availability of high-quality, diverse training data. Existing met…
LettinGo: Explore User Profile Generation for Recommendation System
Lu Wang, Di Zhang, Fangkai Yang +9
User profiling is pivotal for recommendation systems, as it transforms raw user interaction data into concise and structured representations that drive personalized recommendations…
RePrompt: Reasoning-Augmented Reprompting for Text-to-Image Generation via Reinforcement Learning
Mingrui Wu, Lu Wang, Pu Zhao +14
Despite recent progress in text-to-image (T2I) generation, existing models often struggle to faithfully capture user intentions from short and under-specified prompts. While prior…
UFO2: The Desktop AgentOS
Chaoyun Zhang, He Huang, Chiming Ni +18
Recent Computer-Using Agents (CUAs), powered by multimodal large language models (LLMs), offer a promising direction for automating complex desktop workflows through natural langua…
VEM: Environment-Free Exploration for Training GUI Agent with Value Environment Model
Jiani Zheng, Lu Wang, Fangkai Yang +7
Training Vision-Language Models (VLMs) for Graphical User Interfaces (GUI) agents via Reinforcement Learning (RL) faces critical challenges: environment-based RL requires costly in…
Pretrain Value, Not Reward: Decoupled Value Policy Optimization
Chenghua Huang, Lu Wang, Fangkai Yang +6
In this paper, we explore how directly pretraining a value model simplifies and stabilizes reinforcement learning from human feedback (RLHF). In reinforcement learning, value estim…
Enabling Autonomic Microservice Management through Self-Learning Agents
Fenglin Yu, Fangkai Yang, Xiaoting Qin +8
The increasing complexity of modern software systems necessitates robust autonomic self-management capabilities. While Large Language Models (LLMs) demonstrate potential in this do…
Skeleton-Guided-Translation: A Benchmarking Framework for Code Repository Translation with Fine-Grained Quality Evaluation
Xing Zhang, Jiaheng Wen, Fangkai Yang +11
The advancement of large language models has intensified the need to modernize enterprise applications and migrate legacy systems to secure, versatile languages. However, existing…
Large Action Models: From Inception to Implementation
Lu Wang, Fangkai Yang, Chaoyun Zhang +15
As AI continues to advance, there is a growing demand for systems that go beyond language-based assistance and move toward intelligent agents capable of performing real-world actio…
ExeCoder: Empowering Large Language Models with Executability Representation for Code Translation
Minghua He, Yue Chen, Fangkai Yang +6
Code translation is a crucial activity in the software development and maintenance process, and researchers have recently begun to focus on using pre-trained large language models…