Can Discrete Tokens Serve as Semantically Lossless Representations of Visual Signals? Paper • 2610.37662 • Published 19 days ago • 37
PDRVLA: A Vision-Language-Action Model with Physical Dynamics Representations Paper • 2610.37381 • Published 19 days ago • 158
On the Limits of Scale: Inverse Scaling in Vision-Language Models Paper • 2610.27360 • Published 27 days ago • 14
Can Visual Generation Expand What Unified Multimodal Models Know? Paper • 2610.21459 • Published about 1 month ago • 28
AutoSFT: An Agentic System for Automated Data Research in Supervised Fine-Tuning Paper • 2610.22194 • Published about 1 month ago • 41
MC-Sparse: Deconstructing and Closing the Dense-Sparse Attention Gap in Diffusion Transformers Paper • 2610.06801 • Published Oct 5 • 37
DC-SAE: Deep Compression Semantic Autoencoder for Faster Diffusion Convergence Paper • 2609.39222 • Published Sep 30 • 24
How Far Are We from Removing the Visual Encoder? Scaling Laws for Encoder-Free Multimodal Pretraining Paper • 2609.35457 • Published Sep 28 • 61
Learning Native Reflection in Unified Models with Interleaved Reinforcement Learning Paper • 2609.35767 • Published Sep 28 • 42
Just MLPs: Efficient Visual State Reconstruction for Multimodal Language Models Paper • 2609.34972 • Published Sep 28 • 24
Rethinking Training-Inference Mismatch in LLM Reinforcement Learning: Where It Arises and How to Correct It Paper • 2609.32444 • Published Sep 26 • 23
SenseNova-U1.5: Towards Native Unified Visual Intelligence Paper • 2609.11929 • Published Sep 10 • 139
SpatialBlock: Enhancing Spatial Intelligence in LVLMs via Synthetic Block-Stacking Problem Paper • 2609.07064 • Published Sep 7 • 61
Don't Drop Dropout: Optimizing Layer Sparsity for Efficient LLM Training and Inference Paper • 2609.05275 • Published Sep 4 • 23
Granite 4.2 Language Models Collection Efficient reasoning and thinking language models for multilingual generation, coding, and AI assistant workflows. • 27 items • Updated 26 days ago • 55