OmniWeaving: Towards Unified Video Generation with Free-form Composition and Reasoning Paper β’ 2603.24458 β’ Published 3 days ago β’ 4
4DGS360: 360Β° Gaussian Reconstruction of Dynamic Objects from a Single Video Paper β’ 2603.21618 β’ Published 5 days ago β’ 11
Tri-Prompting: Video Diffusion with Unified Control over Scene, Subject, and Motion Paper β’ 2603.15614 β’ Published 12 days ago β’ 6
V-JEPA 2.1: Unlocking Dense Features in Video Self-Supervised Learning Paper β’ 2603.14482 β’ Published 13 days ago β’ 23
Running on Zero Featured 753 FLUX.2 [dev] π» 753 Generate or edit images from text and optional photos
LumosX: Relate Any Identities with Their Attributes for Personalized Video Generation Paper β’ 2603.20192 β’ Published 8 days ago β’ 22
Astrolabe: Steering Forward-Process Reinforcement Learning for Distilled Autoregressive Video Models Paper β’ 2603.17051 β’ Published 11 days ago β’ 105
SAMA: Factorized Semantic Anchoring and Motion Alignment for Instruction-Guided Video Editing Paper β’ 2603.19228 β’ Published 9 days ago β’ 66
3DreamBooth: High-Fidelity 3D Subject-Driven Video Generation Model Paper β’ 2603.18524 β’ Published 9 days ago β’ 58
view post Post 3822 Can small models program?Although even if they are reasoning AIs, small AIs cannot create extensive and high-quality code, at least that's what is commonly thought.We present OrionLLM/NanoCoder-0.6b, an AI with just 600 million parameters based on qwen3-0.6b and trained with the dataset nvidia/OpenCodeReasoning.While not good at complex code, we observed a significant improvement in code generation (especially in Python code), demonstrating that, when trained correctly, small AIs can, in fact, program. See translation 2 replies Β· π€ 9 9 π 1 1 π₯ 1 1 + Reply