Single-Cell Foundation Models

Follow this topic Ask about this topic

Momentum

3 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Jul 6Week of Sep 21

Latest papers 33

All topics

On the Information-Theoretic Limits of Latent-Space Watermarking Through Pretrained Generators
Sep 21, 2026Jinwan Jeon, Minju Lee, Sung Hoon LimWatermarks Watermarking
Hallym University, CHUNCHEON-SI, GANGWON-DO, South Korea
FRPSS: Feature Rearrangement in Pre-Shape Space for Single-Image Generation
Sep 15, 2026Yuexing Han, Haoxuan Zhang, Bing WangMulti-Reference Image Generation Single-Cell Foundation Models
School of Computer Engineering and Science, Shanghai University, 99 Shangda Road, Shanghai 200444, People’s Republic of China · Key Laboratory of Silicate Cultural Relics Conservation (Shanghai University), Ministry of Education
Isotropic Embedding Perturbations for Robust Vision Language Encoders
Sep 9, 2026Hyesong Choi, Daeun Kim, Song Park +5Data Augmentation Single-Cell Foundation Models
Soongsil Univ. · NAVER AI Lab · Ewha W. Univ.
Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models
Aug 6, 2026Haodong Yan, Junfeng Li, Junjie He +12Recent World-Action Models Faster-Wam
The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China · Beihang University, Beijing, China · Huawei Foundation Model Department
Self-Supervised Representation-Guided Generative Dataset Distillation
Aug 4, 2026Mingzhuo Li, Guang Li, Linfeng Ye +4Diffusion-Based Dataset Distillation Dataset Distillation
Hokkaido University, Japan · University of Toronto, Canada · University of Tokyo, Japan
Beyond Gene Reconstruction: Learning Cell Representations through Complementary Transcriptomic Views
Aug 2, 2026Jiaqi Xiong, Yuntao hu, Yu Zheng +3Single-Cell Rna Transcriptomics
University of Oxford, Oxford, United Kingdom · Tongji University, Shanghai, China · Hunan University, Changsha, China +2
Test-Time Registers as Global Priors for Tokenized Image Generation
Jul 18, 2026Cheng-Yao Hong, Yifan Wang, Yuewei Lin +1Visual Tokenizers Single-Cell Foundation Models
Stony Brook University · Brookhaven National Laboratory
Read It Back: Pretrained MLLMs Are Zero-Shot Reward Models for Text-to-Image Generation
Jul 13, 2026Runhui Huang, Qihui Zhang, Zhe Liu +3Single-Cell Foundation Models Multimodal Large Language Models
The University of Hong Kong · Peking University · ByteDance Seed
Slot-RAE: Streamlining Object-Centric Learning via Direct Representation Auto-Encoders
Jul 13, 2026Alexandre Chapin, Emmanuel Dellandrea, Liming ChenObject-Centric Representations Identity-Conditioned Latent Diffusion Models
LIRIS, imagine
From RGB Generation to Dense Field Readout: Pixel-Space Dense Prediction with Text-to-Image Models
Jul 7, 2026Zanyi Wang, Xin Lin, Haodong Li +2Dense Prediction Single-Cell Foundation Models
1UCSD · 2HKUST
UniCoder: Unified Visual-to-Code Generation via Symbolic Rewards and Reference-Guided Code Optimization
Jun 30, 2026Yaozhi Zheng, Yilei Jiang, Manyuan Zhang +5Image-To-Code Generation Visual Generation
The Chinese University of Hong Kong · Shanghai Artificial Intelligence Laboratory
DiT-Reward: Generative Representations for Text-to-Image Reward Modeling
Jun 22, 2026Yuanming Yang, Guoqing Ma, Bo Wang +5Text-To-Image Diffusion Models Diffusion Transformers
JD Explore Academy, JD.com, Beijing, China · Tsinghua University, Beijing, China · Beijing Institute of Technology, Beijing, China +1
Adversarial Domain Prompt Tuning and Generation for Single Domain Generalization
Jun 19, 2026Zhipeng Xu, De Cheng, Xinyang Jiang +3Multimodal Domain Generalization Single-Cell Foundation Models
Xidian University · Microsoft Research Asia · Chongqing University of Posts and Telecommunications
Breaking the Lock-in: Diversifying Text-to-Image Generation via Representation Modulation
Jun 5, 2026Dahee Kwon, Haeun Lee, Jaesik ChoiModern Text-To-Image Models Text-To-Image
Korea Advanced Institute of Science and Technology (KAIST) · INEEJI
Balancing Image Compression and Generation with Bootstrapped Tokenization
Jun 4, 2026Haozhe Chi, Jinghan Li, Hao Jiang +4Visual Tokenizers Autoregressive Image Generation
1Peking University · 2Central Media Technology Institute, Huawei
GAP3D: Generative Alignment of VLM Latents to Patch-Level Embeddings for 3D Generation
May 27, 2026Polytimi Anna Gkotsi, Andrii Zadaianchuk, Mohammad Mahdi Derakhshani3D Generative Models 3D Assets
University of Amsterdam, The Netherlands
ERNIE-Image Technical Report
May 25, 2026Jiaxiang Liu, Zhida Feng, Pengyu Zou +46Modern Text-To-Image Models Single-Cell Foundation Models
Generating Pretraining Tokens from Organic Data for Data-Bound Scaling
May 18, 2026Zichun Yu, Chenyan XiongSynthetic Data Single-Cell Foundation Models
Language Technologies Institute, Carnegie Mellon University · Xlue
ITGPT: Generative Pretraining on Irregular Timeseries
May 15, 2026Antoine Honoré, Ming XiaoTime Series Single-Cell Foundation Models
⋆Division of Information Science and Engineering, KTH Royal Institute of Technology, Stockholm, Sweden
HiDream-O1-Image: A Natively Unified Image Generative Foundation Model with Pixel-level Unified Transformer
May 11, 2026Qi Cai, Jingwen Chen, Chengmin Gao +22Single-Cell Foundation Models Generative Models
Uni-Synergy: Bridging Understanding and Generation for Personalized Reasoning via Co-operative Reinforcement Learning
May 11, 2026Zijun Shen, Sihan Yang, Ruichuan An +5Multimodal Generation Single-Cell Foundation Models
1Peking University · 3CUHK · 4Zhongguancun Academy
Prototype Guided Post-pretraining for Single-Cell Representation Learning
May 8, 2026Sachini Weerasekara, Natasha Darras, Sagar Kamarthi +2Transcriptomics Single-Cell Foundation Models
Sachini Weerasekara∗ · Natasha Darras · Sagar Kamarthi +2
Let ViT Speak: Generative Language-Image Pre-training
May 1, 2026Yan Fang, Mengcheng Lan, Zilong Huang +7Recent Vision-Language Models Vision Encoders
1Beijing Jiaotong University · 2ByteDance · 3Nanyang Technological University
Towards Universal Gene Regulatory Network Inference: Unlocking Generalizable Regulatory Knowledge in Single-cell Foundation Models
May 1, 2026Jiaxin Qi, Hang Li, Yan Cui +2Regulatory Networks Graph Foundation Model
Computer Network Information Center, Chinese Academy of Sciences, Beijing, China · Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, China · University of Chinese Academy of Sciences, Beijing, China
Alethia: A Foundational Encoder for Voice Deepfakes
Apr 30, 2026Yi Zhu, Brahmi Dwivedi, Jayaram Raghuram +1Deepfake Detection Opencode
Reality Defender Inc.
ViPO: Visual Preference Optimization at Scale
Apr 27, 2026Ming Li, Jie Wu, Justin Cui +3Preference Datasets Single-Cell Foundation Models
University of Central Florida · 2ByteDance Seed · ‡ This work was done during the first author’s internship at USA +1
CheXmix: Unified Generative Pretraining for Vision Language Models in Medical Imaging
Apr 24, 2026Ashwin Kumar, Robbie Holland, Corey Barrett +8Chest X-Ray Medical Vision-Language Models
Stanford AIMI, Stanford University · Oracle Health AI · Department of Radiology, Stanford University
Image Generators are Generalist Vision Learners
Apr 22, 2026Valentin Gabeur, Shangbang Long, Songyou Peng +22Single-Cell Foundation Models Image Generation
★project leads and equal contributions · ∇core contributors · †project advisors +1
Mask World Model: Predicting What Matters for Robust Robot Policy Learning
Apr 21, 2026Yunfan Lou, Xiaowei Chi, Xiaojie Zhang +9Robot Policies World Models
National University of Singapore, Singapore · Beijing Academy of Artificial Intelligence, Beijing, China · The Hong Kong University of Science and Technology, Hong Kong, China +3
Generative Texture Filtering
Apr 21, 2026Rongjia Zheng, Shangwei Huang, Lei Zhu +2Texture Single-Cell Foundation Models
Sun Yat-sen University, China · Hong Kong University of Science and Technology (Guangzhou), China
Intermediate Layers Encode Optimal Biological Representations in Single-Cell Foundation Models
Apr 16, 2026Vincenzo Yuto Civale, Roberto Semeraro, Andrew David Bagdanov +1Cells Layer-Wise
University of Siena · University of di Firenze · University of Firenze
Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer
Nov 27, 2025Z-Image Team, Huanqia Cai, Sihan Cao +21Multi-Reference Image Generation Image Generation
Z-Image Team, Alibaba Group
Verbalized Sampling: How to Mitigate Mode Collapse and Unlock LLM Diversity
Oct 1, 2025Jiayi Zhang, Simon Yu, Derek Chong +4Diversity Large Language Model Alignment
Northeastern University · Stanford University · West Virginia University

Sep 21, 2026, cs.ITJ/K move · Enter open · S save

On the Information-Theoretic Limits of Latent-Space Watermarking Through Pretrained Generators

Jinwan Jeon, Minju Lee, Sung Hoon Lim

Hallym University, CHUNCHEON-SI, GANGWON-DO, South Korea

Watermarks Watermarking Information-Theoretic Limits Single-Cell Foundation Models Latent Space Encoders

Open paper AskPDF arXiv

We study latent-space watermarking through a pretrained generator using a prescribed latent-to-output stochastic mapping, called the renderer. A watermark encoder selects the latent input using a message and secret key. For every message and semantic context, the released output must have exactly the desired conditional output distribution. For finite alphabets, we derive rate--key inner and outer bounds and characterize the coding and coordination requirements for realizing watermark communication through the prescribed latent interface. When the target output distribution of the generator uniquely determines the corresponding latent input distribution through the renderer, a strengthened converse yields the capacity region; the same region governs explicit preservation of the pretrained latent distribution. We extend the analysis to general jointly Gaussian models and identify a sufficient statistic of the latent that captures both the watermark-bearing information available at the generated output and the latent coordination required to preserve its target distribution. For the vector Gaussian model, we further characterize the optimal allocation of the secret-key resource across the resulting modes. Finally, we turn to an emerging robustness threat that is particularly natural in generative watermarking: an adversary can regenerate the released sample to obtain a fresh realization of the same underlying content while attenuating or destroying the embedded watermark. We incorporate this robustness axis into our framework and characterize the one-pass compound capacity of the scalar Gaussian model when the semantic context is known to the encoder but hidden from the detector, while the regeneration attack may depend on that context. Extending the analysis to multiple rounds of repeated canonical regeneration, we characterize the resulting watermark-capacity decay.

Momentum

Related topics

Latest papers 33