108 citations · 206 across the 18 of their papers we have counts for
21 papers
Collaborative Personalized Preference Alignment for LLMs under Data Deficiency
Liyan Yang, Yige Yuan, Zhiqin Yang
Real-world users often exhibit highly heterogeneous preferences over multiple objectives for LLM responses. A lightweight aligner can tailor these responses to individual preferenc…
An AI4AI Framework for Visual Token Pruning
Zhen Liu, Wenli Huang, Wei Song +3
Visual-token pruning can substantially reduce the inference cost of multimodal large language models (MLLMs), yet existing methods largely rely on fixed, handcrafted heuristics and…
Scaling Large Reasoning Models beyond Human Supervision: A Path toward Superintelligence
Zhiqin Yang, Jingwen Fu, Yuhan Liu +16
Recent advances in large reasoning models (LRMs) have shown that reinforcement learning with verifiable rewards (RLVR) can substantially improve reasoning in mathematics and code,…
JarvisHub: An Open Harness for Canvas-Native Multimodal Creative Agents
Yunlong Lin, Zixu Lin, Zhaohu Xing +23
Creative AI is moving from single-step asset generation toward long-horizon multimodal production. Although recent generative models can synthesize high-quality images, videos, aud…
Zero2Skill: Bootstrapping Robot Skills through Autonomous Data Collection, Training, and Deployment
Boyuan Wang, Zhenyuan Zhang, Zhiqin Yang +16
Autonomous data collection governs the volume and quality of real-world trajectories for manipulation policy learning. Existing pipelines reduce human effort via self-resetting, VL…
DRNOISE: Benchmarking Deep Research Agents in Misleading Evidence Environments
Jun Nie, Zhiqin Yang, Zhenheng Tang +4
Deep research agents increasingly operate over the open web, where relevant records coexist with redundant summaries, outdated reports, and misleading documents. Existing evaluatio…
A Control Theory of Predictability in Latent World Models
Hanzhe You, Yonggang Zhang, Maohao Ran +6
Latent world models are trained to predict future states in a learned representation and are then deployed inside a planner that selects actions by simulating them forward. Current…
Shaping Schema via Language Representation as the Next Frontier for LLM Intelligence Expanding
Zhiqin Yang, Yuhan Liu, Jingwen Fu +4
Although natural language is the default medium for Large Language Models (LLMs), its limited expressive capacity creates a profound bottleneck for complex problem-solving. While r…
ClawNet: Human-Symbiotic Agent Network for Cross-User Autonomous Cooperation
Zhiqin Yang, Zhenyuan Zhang, Xianzhang Jia +4
Current AI agent frameworks have made remarkable progress in automating individual tasks, yet all existing systems serve a single user. Human productivity rests on the social and o…
FedRG: Unleashing the Representation Geometry for Federated Learning with Noisy Clients
Tian Wen, Zhiqin Yang, Yonggang Zhang +4
Federated learning (FL) suffers from performance degradation due to the inevitable presence of noisy annotations in distributed scenarios. Existing approaches have advanced in dist…
MemCoRe: Recovering Evidence from Progressively Compressed Factual Knowledge for Agent Memory
Zhenyuan Zhang, Xianzhang Jia, Zhiqin Yang +4
Memory systems enable LLM agents to consolidate and retrieve relevant evidence from the factual knowledge accumulated through growing interaction histories for downstream reasoning…
FedGPS: Statistical Rectification Against Data Heterogeneity in Federated Learning
Zhiqin Yang, Yonggang Zhang, Chenxin Li +3
Federated Learning (FL) confronts a significant challenge known as data heterogeneity, which impairs model performance and convergence. Existing methods have made notable progress…
MimicDreamer: Aligning Human and Robot Demonstrations for Scalable VLA Training
Haoyun Li, Ivan Zhang, Runqi Ouyang +12
Vision Language Action (VLA) models derive their generalization capability from diverse training data, yet collecting embodied robot interaction data remains prohibitively expensiv…
EmbodieDreamer: Advancing Real2Sim2Real Transfer for Policy Training via Embodied World Modeling
Boyuan Wang, Xinpan Meng, Xiaofeng Wang +7
The rapid advancement of Embodied AI has led to an increasing demand for large-scale, high-quality real-world data. However, collecting such embodied data remains costly and ineffi…
IR3D-Bench: Evaluating Vision-Language Model Scene Understanding as Agentic Inverse Rendering
Parker Liu, Chenxin Li, Zhengxin Li +7
Vision-language models (VLMs) excel at descriptive tasks, but whether they truly understand scenes from visual observations remains uncertain. We introduce IR3D-Bench, a benchmark…
Robust Federated Learning against Noisy Clients via Masked Optimization
Xuefeng Jiang, Tian Wen, Zhiqin Yang +5
In recent years, federated learning (FL) has made significant advance in privacy-sensitive applications. However, it can be hard to ensure that FL participants provide well-annotat…
HumanDreamer: Generating Controllable Human-Motion Videos via Decoupled Generation
Boyuan Wang, Xiaofeng Wang, Chaojun Ni +9
Human-motion video generation has been a challenging task, primarily due to the difficulty inherent in learning human body movements. While some approaches have attempted to drive…
Robust Training of Federated Models with Extremely Label Deficiency
Yonggang Zhang, Zhiqin Yang, Xinmei Tian +3
Federated semi-supervised learning (FSSL) has emerged as a powerful paradigm for collaboratively training machine learning models using distributed data with label deficiency. Adva…
FedFed: Feature Distillation against Data Heterogeneity in Federated Learning
Zhiqin Yang, Yonggang Zhang, Yu Zheng +4
Federated learning (FL) typically faces data heterogeneity, i.e., distribution shifting among clients. Sharing clients' information has shown great potentiality in mitigating data…
RoSGAS: Adaptive Social Bot Detection with Reinforced Self-Supervised GNN Architecture Search
Yingguang Yang, Renyu Yang, Yangyang Li +5
Social bots are referred to as the automated accounts on social networks that make attempts to behave like human. While Graph Neural Networks (GNNs) has been massively applied to t…
Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning
Shaoxiong Ji, Yue Tan, Teemu Saravirta +6
Federated learning is a new learning paradigm that decouples data collection and model training via multi-party computation and model aggregation. As a flexible learning setting, f…