Collections
Discover the best community collections!
Collections including paper arxiv:2602.04804
-
Linear representations in language models can change dramatically over a conversation
Paper • 2601.20834 • Published • 21 -
daVinci-Agency: Unlocking Long-Horizon Agency Data-Efficiently
Paper • 2602.02619 • Published • 52 -
OmniSIFT: Modality-Asymmetric Token Compression for Efficient Omni-modal Large Language Models
Paper • 2602.04804 • Published • 48
-
DocLLM: A layout-aware generative language model for multimodal document understanding
Paper • 2401.00908 • Published • 189 -
COSMO: COntrastive Streamlined MultimOdal Model with Interleaved Pre-Training
Paper • 2401.00849 • Published • 17 -
LLaVA-Plus: Learning to Use Tools for Creating Multimodal Agents
Paper • 2311.05437 • Published • 51 -
LLaVA-Interactive: An All-in-One Demo for Image Chat, Segmentation, Generation and Editing
Paper • 2311.00571 • Published • 42
-
Good SFT Optimizes for SFT, Better SFT Prepares for Reinforcement Learning
Paper • 2602.01058 • Published • 42 -
PixelGen: Pixel Diffusion Beats Latent Diffusion with Perceptual Loss
Paper • 2602.02493 • Published • 46 -
Scaling Embeddings Outperforms Scaling Experts in Language Models
Paper • 2601.21204 • Published • 102 -
OmniSIFT: Modality-Asymmetric Token Compression for Efficient Omni-modal Large Language Models
Paper • 2602.04804 • Published • 48
-
Qwen2.5-Omni Technical Report
Paper • 2503.20215 • Published • 172 -
Unsupervised Post-Training for Multi-Modal LLM Reasoning via GRPO
Paper • 2505.22453 • Published • 46 -
UniRL: Self-Improving Unified Multimodal Models via Supervised and Reinforcement Learning
Paper • 2505.23380 • Published • 22 -
More Thinking, Less Seeing? Assessing Amplified Hallucination in Multimodal Reasoning Models
Paper • 2505.21523 • Published • 13
-
Good SFT Optimizes for SFT, Better SFT Prepares for Reinforcement Learning
Paper • 2602.01058 • Published • 42 -
PixelGen: Pixel Diffusion Beats Latent Diffusion with Perceptual Loss
Paper • 2602.02493 • Published • 46 -
Scaling Embeddings Outperforms Scaling Experts in Language Models
Paper • 2601.21204 • Published • 102 -
OmniSIFT: Modality-Asymmetric Token Compression for Efficient Omni-modal Large Language Models
Paper • 2602.04804 • Published • 48
-
Linear representations in language models can change dramatically over a conversation
Paper • 2601.20834 • Published • 21 -
daVinci-Agency: Unlocking Long-Horizon Agency Data-Efficiently
Paper • 2602.02619 • Published • 52 -
OmniSIFT: Modality-Asymmetric Token Compression for Efficient Omni-modal Large Language Models
Paper • 2602.04804 • Published • 48
-
Qwen2.5-Omni Technical Report
Paper • 2503.20215 • Published • 172 -
Unsupervised Post-Training for Multi-Modal LLM Reasoning via GRPO
Paper • 2505.22453 • Published • 46 -
UniRL: Self-Improving Unified Multimodal Models via Supervised and Reinforcement Learning
Paper • 2505.23380 • Published • 22 -
More Thinking, Less Seeing? Assessing Amplified Hallucination in Multimodal Reasoning Models
Paper • 2505.21523 • Published • 13
-
DocLLM: A layout-aware generative language model for multimodal document understanding
Paper • 2401.00908 • Published • 189 -
COSMO: COntrastive Streamlined MultimOdal Model with Interleaved Pre-Training
Paper • 2401.00849 • Published • 17 -
LLaVA-Plus: Learning to Use Tools for Creating Multimodal Agents
Paper • 2311.05437 • Published • 51 -
LLaVA-Interactive: An All-in-One Demo for Image Chat, Segmentation, Generation and Editing
Paper • 2311.00571 • Published • 42