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EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters
Paper • 2402.04252 • Published • 30 -
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models
Paper • 2402.03749 • Published • 15 -
ScreenAI: A Vision-Language Model for UI and Infographics Understanding
Paper • 2402.04615 • Published • 44 -
EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss
Paper • 2402.05008 • Published • 23
Collections
Discover the best community collections!
Collections including paper arxiv:2511.09611
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MMaDA-Parallel: Multimodal Large Diffusion Language Models for Thinking-Aware Editing and Generation
Paper • 2511.09611 • Published • 71 -
In-Video Instructions: Visual Signals as Generative Control
Paper • 2511.19401 • Published • 32 -
Both Semantics and Reconstruction Matter: Making Representation Encoders Ready for Text-to-Image Generation and Editing
Paper • 2512.17909 • Published • 37
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Describe What You See with Multimodal Large Language Models to Enhance Video Recommendations
Paper • 2508.09789 • Published • 5 -
MM-BrowseComp: A Comprehensive Benchmark for Multimodal Browsing Agents
Paper • 2508.13186 • Published • 20 -
ZARA: Zero-shot Motion Time-Series Analysis via Knowledge and Retrieval Driven LLM Agents
Paper • 2508.04038 • Published • 1 -
Prompt Orchestration Markup Language
Paper • 2508.13948 • Published • 48
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Test-Time Scaling with Reflective Generative Model
Paper • 2507.01951 • Published • 108 -
Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach
Paper • 2502.05171 • Published • 155 -
Autoregressive Diffusion Models
Paper • 2110.02037 • Published -
EQ-VAE: Equivariance Regularized Latent Space for Improved Generative Image Modeling
Paper • 2502.09509 • Published • 9
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TiDAR: Think in Diffusion, Talk in Autoregression
Paper • 2511.08923 • Published • 128 -
Diffusion Language Models are Super Data Learners
Paper • 2511.03276 • Published • 132 -
What Makes Diffusion Language Models Super Data Learners?
Paper • 2510.04071 • Published -
LLaDA2.0: Scaling Up Diffusion Language Models to 100B
Paper • 2512.15745 • Published • 88
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Arbitrary-steps Image Super-resolution via Diffusion Inversion
Paper • 2412.09013 • Published • 13 -
Deep Researcher with Test-Time Diffusion
Paper • 2507.16075 • Published • 68 -
nablaNABLA: Neighborhood Adaptive Block-Level Attention
Paper • 2507.13546 • Published • 126 -
Yume: An Interactive World Generation Model
Paper • 2507.17744 • Published • 92
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Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters
Paper • 2408.03314 • Published • 67 -
TAG: A Decentralized Framework for Multi-Agent Hierarchical Reinforcement Learning
Paper • 2502.15425 • Published • 9 -
EgoLife: Towards Egocentric Life Assistant
Paper • 2503.03803 • Published • 46 -
Visual-RFT: Visual Reinforcement Fine-Tuning
Paper • 2503.01785 • Published • 86
-
EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters
Paper • 2402.04252 • Published • 30 -
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models
Paper • 2402.03749 • Published • 15 -
ScreenAI: A Vision-Language Model for UI and Infographics Understanding
Paper • 2402.04615 • Published • 44 -
EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss
Paper • 2402.05008 • Published • 23
-
MMaDA-Parallel: Multimodal Large Diffusion Language Models for Thinking-Aware Editing and Generation
Paper • 2511.09611 • Published • 71 -
In-Video Instructions: Visual Signals as Generative Control
Paper • 2511.19401 • Published • 32 -
Both Semantics and Reconstruction Matter: Making Representation Encoders Ready for Text-to-Image Generation and Editing
Paper • 2512.17909 • Published • 37
-
TiDAR: Think in Diffusion, Talk in Autoregression
Paper • 2511.08923 • Published • 128 -
Diffusion Language Models are Super Data Learners
Paper • 2511.03276 • Published • 132 -
What Makes Diffusion Language Models Super Data Learners?
Paper • 2510.04071 • Published -
LLaDA2.0: Scaling Up Diffusion Language Models to 100B
Paper • 2512.15745 • Published • 88
-
Describe What You See with Multimodal Large Language Models to Enhance Video Recommendations
Paper • 2508.09789 • Published • 5 -
MM-BrowseComp: A Comprehensive Benchmark for Multimodal Browsing Agents
Paper • 2508.13186 • Published • 20 -
ZARA: Zero-shot Motion Time-Series Analysis via Knowledge and Retrieval Driven LLM Agents
Paper • 2508.04038 • Published • 1 -
Prompt Orchestration Markup Language
Paper • 2508.13948 • Published • 48
-
Arbitrary-steps Image Super-resolution via Diffusion Inversion
Paper • 2412.09013 • Published • 13 -
Deep Researcher with Test-Time Diffusion
Paper • 2507.16075 • Published • 68 -
nablaNABLA: Neighborhood Adaptive Block-Level Attention
Paper • 2507.13546 • Published • 126 -
Yume: An Interactive World Generation Model
Paper • 2507.17744 • Published • 92
-
Test-Time Scaling with Reflective Generative Model
Paper • 2507.01951 • Published • 108 -
Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach
Paper • 2502.05171 • Published • 155 -
Autoregressive Diffusion Models
Paper • 2110.02037 • Published -
EQ-VAE: Equivariance Regularized Latent Space for Improved Generative Image Modeling
Paper • 2502.09509 • Published • 9
-
Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters
Paper • 2408.03314 • Published • 67 -
TAG: A Decentralized Framework for Multi-Agent Hierarchical Reinforcement Learning
Paper • 2502.15425 • Published • 9 -
EgoLife: Towards Egocentric Life Assistant
Paper • 2503.03803 • Published • 46 -
Visual-RFT: Visual Reinforcement Fine-Tuning
Paper • 2503.01785 • Published • 86