31 citations · 50 across the 51 of their papers we have counts for
58 papers
AdaStep: Adaptive Step Credit Weighting for Agentic Reinforcement Learning
Xin Wang, Wenhao Wu, Menghao Zhang +3
Long-horizon LLM agents are typically trained with sparse outcome rewards, making trajectory-level objectives too coarse to distinguish the contribution of individual decisions. St…
Learning Reliable GUI Agents under Imperfect Priors
Bo Han, Qianyi Wang, Shuai Liu +9
GUI agents built on large language and vision-language models still struggle on unseen applications and complex multi-step tasks, as completing real GUI tasks depends on app-specif…
HarnessPAI: An Evolving Harness for Physical AI
Xin Wang, Wenhao Wu, Menghao Zhang +20
Physical AI aims to build embodied agents that perceive the world, understand and reason about it, and decide how to act. Yet the field has focused primarily on the last component:…
Where to Look and What to Use: Retrieve-Localize-Generate for Long-Term Conversational Memory Question Answering
Yifan Wang, Xinkui Lin, Yongxiu Xu +9
Retrieval-augmented generation (RAG) enables large language models (LLMs) to answer questions by accessing external knowledge and has been widely adopted for long-term conversation…
TRACE: A Self-Evolving Skill Bank for Consistent, Limit-Aware LLM Agents
Wenhao Wu, Menghao Zhang, Xin Wang +3
Reliable deployment of LLM agents in user-facing products depends not on raw task-solving ability but on consistency and limit-awareness: behaving the same way across repeated tria…
Task-Adaptive Rubrics for GUI Reward Modeling
Tao Xiong, Xavier Hu, Wenkai Wang +6
Recent studies on GUI agents have increasingly focused on outcome reward modeling, which assigns outcome rewards by judging whether an executed trajectory satisfies the success cri…
GSAR: Goal-State-Anchor Rewards for Mobile GUI Agents with Self-Evolving Data Synthesis
Long Zhang, Yuhan Chen, Chaoran Zhang +7
Vision-Language Models (VLMs) based GUI agents stand to benefit significantly from online reinforcement learning (RL). However, their training is bottlenecked by two fundamental is…
G-ReAct: Graph-Guided Deep Search via Structure-State Co-Evolution
Shaoxiong Yang, Mengyuan Zhang, Shaojun Lin +4
Deep search has become a fundamental capability of large language models (LLMs) for solving open-domain complex tasks. However, existing approaches typically rely on linear sequent…
SEE: Structure-aware Exploring & Exploiting for Long-horizon GUI Agent Trajectory Synthesis
Zhuohang Fan, Beichen Zhang, Yuanfa Li +4
Graphical User Interface (GUI) agents powered by vision-language models hold promise for automating real-world mobile tasks. However, progress is limited by the lack of high-covera…
Topological collapse of higher-order interactions bottlenecks collective intelligence in AI agent societies
Shuo Lu, Weicheng Meng, Aijing Yu +4
Current paradigms in artificial intelligence concentrate on scaling the capabilities of individual models, yet the collective behaviour of interacting agents is shaped by the topol…
Reference-Free Post-Training of Open Large Language Models for Multilingual Machine Translation
Chris Han, Pengzhi Gao, Pei Fu +1
We study reference-free post-training for multilingual machine translation with open large language models. Starting from the supervised-finetuned MiLMMT-46-v0.1 models, we apply G…
UI-MOPD: Multi-Platform On-Policy Distillation for Unified GUI Agents
Niu Lian, Tongbo Chen, Zhehao Yu +8
Recent advances in multimodal foundation models and agent systems have driven GUI agents from single-platform task execution toward cross-platform interaction. However, unified mul…
HarnessX: A Composable, Adaptive, and Evolvable Agent Harness Foundry
Tingyang Chen, Shuo Lu, Kang Zhao +11
AI agent performance depends critically on the runtime harness, comprising the prompts, tools, memory, and control flow that mediate how a model observes, reasons, and acts. Yet to…
Mi-Memory: A Lifecycle Memory Framework for Personal AI
Xule Liu, Hanlin Teng, Chao Li +15
Personal AI is moving beyond chat-only interaction toward continuous services that span phones, cars, homes, wearables, cameras, and tools. In this setting, memory cannot remain a…
Xiaomi-GUI-0 Technical Report
Wanxia Cao, Chengzhen Duan, Pei Fu +29
Graphical user interface (GUI) agents build on vision-language models to complete user tasks end-to-end in real applications through interface actions such as tapping, swiping, tex…
ScaleWoB: Guiding GUI Agents with Coding Agents via Large-Scale Environmental Synthesis
Guohong Liu, Jialei Ye, Pengzhi Gao +4
GUI agents powered by large language models are advancing rapidly, creating urgent needs for evaluation and training based on realistic environments. However, directly doing so in…
Scaling, Benchmarking, and Reasoning of Vision-Language Agents for Mobile GUI Navigation
Heng Qu, Yike Liu, Renren Jin +4
Vision-Language Models (VLMs) have shown rapid progress in mobile GUI navigation. This paper presents a systematic study of data scaling, benchmarking, and reasoning for VLM-based…
PixelWizard: Towards Efficient High-Fidelity Video Generation at Ultra-Large Spatial Resolution
Wenxue Li, Jingjing Ren, Peng Zhang +6
High-resolution video generation faces a coupled bottleneck of optimization instability and prohibitive computational costs. The massive expansion of the token sequence not only bi…
ExPosST: Explicit Positioning with Adaptive Masking for LLM-Based Simultaneous Machine Translation
Yuzhe Shang, Pengzhi Gao, Yazheng Yang +4
Large language models (LLMs) have recently demonstrated promising performance in simultaneous machine translation (SimulMT). However, applying decoder-only LLMs to SimulMT introduc…
C-Cite: Contextual-Aware Citation Generation for Attributed Large Language Models
Yue Yu, Ting Bai, HengZhi Lan +6
The attribution technique enhances the credibility of LLMs by adding citations to the generated sentences, enabling users to trace back to the original sources and verify the relia…
Scaling Model and Data for Multilingual Machine Translation with Open Large Language Models
Yuzhe Shang, Pengzhi Gao, Wei Liu +2
Open large language models (LLMs) have demonstrated improving multilingual capabilities in recent years. In this paper, we present a study of open LLMs for multilingual machine tra…
FutureMind: Equipping Small Language Models with Strategic Thinking-Pattern Priors via Adaptive Knowledge Distillation
Shaoxiong Yang, Junting Li, Mengyuan Zhang +3
Small Language Models (SLMs) are attractive for cost-sensitive and resource-limited settings due to their efficient, low-latency inference. However, they often struggle with comple…
MobileBench-OL: A Comprehensive Chinese Benchmark for Evaluating Mobile GUI Agents in Real-World Environment
Qinzhuo Wu, Zhizhuo Yang, Hanhao Li +3
Recent advances in mobile Graphical User Interface (GUI) agents highlight the growing need for comprehensive evaluation benchmarks. While new online benchmarks offer more realistic…
R^3: Replay, Reflection, and Ranking Rewards for LLM Reinforcement Learning
Zhizheng Jiang, Kang Zhao, Weikai Xu +5
Large reasoning models (LRMs) aim to solve diverse and complex problems through structured reasoning. Recent advances in group-based policy optimization methods have shown promise…