most citedSPATIA: Multimodal Generation and Prediction of Spatial Cell Phenotypes

1 citations · 1 across the 3 of their papers we have counts for

collaborators

7 papers

q-bio.QM20261 cited

SPATIA: Multimodal Generation and Prediction of Spatial Cell Phenotypes

Zhenglun Kong, Mufan Qiu, John Boesen +5

Understanding how cellular morphology, gene expression, and spatial context jointly shape tissue function is a central challenge in biology. Image-based spatial transcriptomics tec…

cs.LG2026

Chreode: A Cell World Model for One-Step Temporal Dynamics and Perturbation Prediction

Mufan Qiu, Genhui Zheng, Yinuo Xu +4

Predicting how a cell will change its transcriptional state under a developmental signal or a genetic perturbation is the computational core of in-silico biology and the AI Virtual…

cs.LG2026

Understanding the Role of Hallucination in Reinforcement Post-Training of Multimodal Reasoning Models

Gengwei Zhang, Jie Peng, Zhen Tan +6

The recent success of reinforcement learning (RL) in large reasoning models has inspired the growing adoption of RL for post-training Multimodal Large Language Models (MLLMs) to en…

cs.CV2025

SparseC-AFM: a deep learning method for fast and accurate characterization of MoS with C-AFM

Levi Harris, Md Jayed Hossain, Mufan Qiu +6

The increasing use of two-dimensional (2D) materials in nanoelectronics demands robust metrology techniques for electrical characterization, especially for large-scale production.…

cs.LG2025

Advancing MoE Efficiency: A Collaboration-Constrained Routing (C2R) Strategy for Better Expert Parallelism Design

Mohan Zhang, Pingzhi Li, Jie Peng +2

Mixture-of-Experts (MoE) has successfully scaled up models while maintaining nearly constant computing costs. By employing a gating network to route input tokens, it selectively ac…

cs.MA2025

Symbiotic Cooperation for Web Agents: Harnessing Complementary Strengths of Large and Small LLMs

Ruichen Zhang, Mufan Qiu, Zhen Tan +7

Web browsing agents powered by large language models (LLMs) have shown tremendous potential in automating complex web-based tasks. Existing approaches typically rely on large LLMs…