40 citations · 59 across the 66 of their papers we have counts for
25 papers · 1 filter
PerturbBridge: Direct Phenotypic Supervision for Cellular Perturbation Generation
Yucheng Yang, Xiaoxiao Sun, Huangqingbo Sun +3
Virtual cell models should capture phenotypes induced by specific perturbations. However, image-based models are typically trained and evaluated to match real-image distributions,…
ScreenHaystack: Finding Blind Zones in GUI Grounding
Chenyue Li, Xiaoxiao Sun, Yubo Deng +3
We introduce ScreenHaystack, a dynamic needle-in-a-haystack benchmark for evaluating spatial reliability in GUI grounding. Instead of testing each target at a fixed position, Scree…
Solve the Missing First Step: Can VLMs Standardize Raw Heterogeneous Medical Data?
Xin Chen, Dongliang Xu, Cunhao Zhu +5
As vision-language models (VLMs) are increasingly applied to medical AI, existing benchmarks mainly focus on evaluating their diagnostic ability over given medical images and texts…
AutoMem: Automated Learning of Memory as a Cognitive Skill
Shengguang Wu, Hao Zhu, Yuhui Zhang +2
Memory expertise is a learned skill: knowing what to encode, when to retrieve, and how to organize knowledge--a capacity known in cognitive science as metamemory. We bring this per…
Learning from Failure: Inference-Time Self-Improvement for Computer-Use Agents
Xueqiao Sun, Xiaohan Wang, Ludwig Schmidt +2
Computer-use agents, which leverage multimodal large language models (MLLMs) to operate computers and complete tasks, have attracted significant attention for their utility and ver…
PerturbCellRL: Aligning Distributions and Grounding Biology via Post-Training Perturbation Generators
Dongxia Wu, Mingyu Li, Yuhui Zhang +4
Single-cell perturbation models can reduce costly wet-lab screening by predicting how cells respond transcriptionally to interventions. Recent advances in flow-matching have enable…
SalArt-VQA: Diagnosing Whether VLMs Understand Salient Artifacts in Generated Images
Xiaoxiao Sun, Ruotian Zhang, Junzhe Huang +2
Vision-language models (VLMs) are increasingly used to detect whether AI-generated images contain visible artifacts, yet their ability to analyze such artifacts remains poorly unde…
MMBU: A Massive Multi-modal Biomedical Understanding Benchmark to Probe the Perception Capabilities of Vision-Language Models
Ryan D'Cunha, Alejandro Lozano, Xiaoxiao Sun +21
Vision and language models (VLMs) hold immense promise to transform biomedical imaging workflows, from detecting lesions in chest X-rays to profiling cellular features in microscop…
Ten Headache Specialists versus Artificial Intelligence for Clinical Literature Summarization: A Critical Evaluation and Comparison
Alejandro Lozano, Keiko Ihara, Ping-Hao Yang +13
Summarizing the latest medical literature to guide clinical decision-making is essential for evidence-based medicine and high-quality patient care. Yet clinicians face increasing c…
DataComp-VLM: Improved Open Datasets for Vision-Language Models
Matteo Farina, Vishaal Udandarao, Thao Nguyen +33
Building performant Vision-Language Models (VLMs) requires carefully curating large-scale training datasets, yet the community lacks systematic benchmarks for evaluating such curat…
WildTableBench: Benchmarking Multimodal Foundation Models on Table Understanding In the Wild
Junzhe Huang, Xiaoxiao Sun, Yan Yang +6
Using multimodal foundation models to analyze table images is a high-value yet challenging application in consumer and enterprise scenarios. Despite its importance, current evaluat…
Anny-Fit: All-Age Human Mesh Recovery
Laura Bravo-Sánchez, Matthieu Armando, Romain Brégier +3
Recovering 3D human pose and shape from a single image remains a cornerstone of human-centric vision, yet most methods assume adult subjects and optimize each person independently.…
V-GRPO: Online Reinforcement Learning for Denoising Generative Models Is Easier than You Think
Bingda Tang, Yuhui Zhang, Xiaohan Wang +3
Aligning denoising generative models with human preferences or verifiable rewards remains a key challenge. While policy-gradient online reinforcement learning (RL) offers a princip…
Project Imaging-X: A Survey of 1000+ Open-Access Medical Imaging Datasets for Foundation Model Development
Zhongying Deng, Cheng Tang, Ziyan Huang +124
Foundation models have demonstrated remarkable success across diverse domains and tasks, primarily due to the thrive of large-scale, diverse, and high-quality datasets. However, in…
Fine-tuning MLLMs Without Forgetting Is Easier Than You Think
He Li, Yuhui Zhang, Xiaohan Wang +2
The paper demonstrate that simple adjustments of the fine-tuning recipes of multimodal large language models (MLLM) are sufficient to mitigate catastrophic forgetting. On visual qu…
CellFluxRL: Biologically-Constrained Virtual Cell Modeling via Reinforcement Learning
Dongxia Wu, Shiye Su, Yuhui Zhang +4
Building virtual cells with generative models to simulate cellular behavior in silico is emerging as a promising paradigm for accelerating drug discovery. However, prior image-base…
Uncertainty-Aware Distribution-to-Distribution Flow Matching for Scientific Imaging
Dongxia Wu, Yuhui Zhang, Serena Yeung-Levy +2
Distribution-to-distribution generative models support scientific imaging tasks ranging from modeling cellular perturbation responses to translating medical images across condition…
Tool Verification for Test-Time Reinforcement Learning
Ruotong Liao, Nikolai Röhrich, Xiaohan Wang +4
Test-time reinforcement learning (TTRL) has emerged as a promising paradigm for Recursive Self-Improving AI (RSI) by adapting Large Reasoning Models (LRMs) on unlabeled test inputs…
ArtifactLens: Hundreds of Labels Are Enough for Artifact Detection with VLMs
James Burgess, Rameen Abdal, Dan Stoddart +3
Modern image generators produce strikingly realistic images, where only artifacts like distorted hands or warped objects reveal their synthetic origin. Detecting these artifacts is…
iSight: Towards expert-AI co-assessment for improved immunohistochemistry staining interpretation
Jacob S. Leiby, Jialu Yao, Pan Lu +17
Immunohistochemistry (IHC) provides information on protein expression in tissue sections and is commonly used to support pathology diagnosis and disease triage. While AI models for…
Seeing Is Believing? A Benchmark for Multimodal Large Language Models on Visual Illusions and Anomalies
Wenjin Hou, Wei Liu, Han Hu +3
Multimodal Large Language Models (MLLMs) have shown remarkable proficiency on general-purpose vision-language benchmarks, reaching or even exceeding human-level performance. Howeve…
UniT: Unified Multimodal Chain-of-Thought Test-time Scaling
Leon Liangyu Chen, Haoyu Ma, Zhipeng Fan +11
Unified models can handle both multimodal understanding and generation within a single architecture, yet they typically operate in a single pass without iteratively refining their…
PaperSearchQA: Learning to Search and Reason over Scientific Papers with RLVR
James Burgess, Jan N. Hansen, Duo Peng +5
Search agents are language models (LMs) that reason and search knowledge bases (or the web) to answer questions; recent methods supervise only the final answer accuracy using reinf…
RadDiff: Describing Differences in Radiology Image Sets with Natural Language
Xiaoxian Shen, Yuhui Zhang, Sahithi Ankireddy +5
Understanding how two radiology image sets differ is critical for generating clinical insights and for interpreting medical AI systems. We introduce RadDiff, a multimodal agentic s…