activity
20232026
most citedFedFed: Feature Distillation against Data Heterogeneity in Federated Learning

28 citations · 33 across the 16 of their papers we have counts for

collaborators

18 papers

cs.LG2026

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…

cs.AI2026

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,…

cs.CV2026

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…

cs.RO2026

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…

cs.LG2026

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Deep research agents increasingly operate over the open web, where relevant records coexist with redundant summaries, outdated reports, and misleading documents.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…

cs.LG2026

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…