6 citations · 29 across the 40 of their papers we have counts for
41 papers
One Prompt Does Not Fit All: Self-Meta-Evolve for Personalized Information Extraction
Hongliang Li, Lu Wang, Yong Xu +6
Large language models (LLMs) are increasingly deployed for enterprise information extraction (IE), where the same document must be reorganized differently for each user. Existing p…
Beyond Fail-to-Pass: Iterative Hardening of Co-Generated Bug Reproduction Tests and Fixes
Yuhao Tan, Zhibang Yang, Fangkai Yang +9
Large language models (LLMs) have made automated program repair (APR) increasingly practical for real-world bugs, but repairing directly from bug reports remains underconstrained.…
Zipage: Maintain High Request Concurrency for LLM Reasoning through Compressed PagedAttention
Mengqi Liao, Lu Wang, Chaoyun Zhang +6
With reasoning becoming the generative paradigm for large language models (LLMs), the memory bottleneck caused by KV cache during the decoding phase has become a critical factor li…
AdNanny: One Reasoning LLM for All Offline Ads Recommendation Tasks
Nan Hu, Han Li, Jimeng Sun +16
Large Language Models (LLMs) have shown strong capabilities in Natural Language Understanding and Generation, but deploying them directly in online advertising systems is often imp…
RepoGenesis: Benchmarking End-to-End Microservice Generation from Readme to Repository
Zhiyuan Peng, Xin Yin, Pu Zhao +7
Large language models and agents have achieved remarkable progress in code generation. However, existing benchmarks focus on isolated function/class-level generation (e.g., ClassEv…
G-KV: Decoding-Time KV Cache Eviction with Global Attention
Mengqi Liao, Lu Wang, Chaoyun Zhang +7
Recent reasoning large language models (LLMs) excel in complex tasks but encounter significant computational and memory challenges due to long sequence lengths. KV cache compressio…
GUI-360: A Comprehensive Dataset and Benchmark for Computer-Using Agents
Jian Mu, Chaoyun Zhang, Chiming Ni +14
We introduce GUI-360, a large-scale, comprehensive dataset and benchmark suite designed to advance computer-using agents (CUAs). CUAs present unique challenges and is const…
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…
Text2Grad: Reinforcement Learning from Natural Language Feedback
Hanyang Wang, Lu Wang, Chaoyun Zhang +5
Traditional RLHF optimizes language models with coarse, scalar rewards that mask the fine-grained reasons behind success or failure, leading to slow and opaque learning. Recent wor…
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…
Distill Not Only Data but Also Rewards: Can Smaller Language Models Surpass Larger Ones?
Yudi Zhang, Lu Wang, Meng Fang +8
Distilling large language models (LLMs) typically involves transferring the teacher model's responses through supervised fine-tuning (SFT). However, this approach neglects the pote…
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…
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…
WarriorCoder: Learning from Expert Battles to Augment Code Large Language Models
Huawen Feng, Pu Zhao, Qingfeng Sun +8
Despite recent progress achieved by code large language models (LLMs), their remarkable abilities are largely dependent on fine-tuning on the high-quality data, posing challenges f…
RuAG: Learned-rule-augmented Generation for Large Language Models
Yudi Zhang, Pei Xiao, Lu Wang +11
In-context learning (ICL) and Retrieval-Augmented Generation (RAG) have gained attention for their ability to enhance LLMs' reasoning by incorporating external knowledge but suffer…
Token-level Proximal Policy Optimization for Query Generation
Yichen Ouyang, Lu Wang, Fangkai Yang +13
Query generation is a critical task for web search engines (e.g. Google, Bing) and recommendation systems. Recently, state-of-the-art query generation methods leverage Large Langua…
Self-Evolved Reward Learning for LLMs
Chenghua Huang, Zhizhen Fan, Lu Wang +7
Reinforcement Learning from Human Feedback (RLHF) is a crucial technique for aligning language models with human preferences, playing a pivotal role in the success of conversationa…
AXIS: Efficient Human-Agent-Computer Interaction with API-First LLM-Based Agents
Junting Lu, Zhiyang Zhang, Fangkai Yang +7
Multimodal large language models (MLLMs) have enabled LLM-based agents to directly interact with application user interfaces (UIs), enhancing agents' performance in complex tasks.…