Yep, however rather than using one large autoregressive model over the entire image, the fractal generative model takes a divide-and-conquer approach. In other words, the model recursively divides the image into patches, and embeds them to form the input
sequence for a
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Fractal Generative Model Uses Divide-and-Conquer Approach
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Fractal Generative Models: Recursive Architecture for Image Generation
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What if generative models could build themselves recursively like fractals? Presenting Fractal Generative Models High quality pixel-by-pixel image generation Efficient modeling of high-dimensional data
Scalable fractal-like architecture Check out the images below -
VideoJAM: Joint Appearance-Motion Representations for Video Generation
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JAM: Joint Appearance-Motion Representations for Enhanced Motion Generation in Models VideoJAM introduces a framework that improves motion coherence in video generation by learning a joint appearance-motion representation. This framework uses a modified training
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Radiant Foam: Real-Time Differentiable Ray Tracing Technique
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Radiant Foam: Real-Time Differentiable Ray Tracing Radiant Foam introduces a real-time differentiable ray tracing technique using a volumetric mesh to model light transport, offering speed and quality similar to 3D Gaussian Splatting (3DGS) without requiring special hardware.
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Flow Q-Learning: Offline RL with Flow-Matching Policies
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Flow Q-Learning Flow Q-Learning (FQL) is an offline reinforcement learning (RL) method that uses flow-matching policies to model complex action distributions. It avoids the challenges of iterative action generation by training an expressive one-step policy with RL, which
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LIMO: Mathematical Reasoning Emerges From Few Examples
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LIMO: Less is More for Reasoning LIMO challenges the belief that complex reasoning requires vast datasets, showing that mathematical reasoning abilities can emerge with just a few examples. It achieves high accuracy on benchmarks with only 817 training samples. Problem:
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Long Chain-of-Thought Reasoning Emergence in LLMs
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Demystifying Long Chain-of-Thought Reasoning in LLMs This paper investigates the emergence of long chain-of-thought (CoT) reasoning in LLMs, focusing on factors that enable structured reasoning strategies like backtracking and error correction. It analyzes the role of supervised
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Inference-Time Compute Improves Adversarial Robustness
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Trading Inference-Time Compute for Adversarial Robustness This paper explores how increasing inference-time compute improves the adversarial robustness of reasoning models (OpenAI o1-preview and o1-mini) without adversarial training. Problem: Adversarial attacks remain a major
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DeepRAG: Adaptive Retrieval for Large Language Models
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DeepRAG: Thinking to Retrieval Step by Step for Large Language Models DeepRAG enhances retrieval-augmented generation (RAG) by modeling reasoning as a Markov Decision Process (MDP), enabling adaptive retrieval and improving answer accuracy. Problem: LLMs struggle with factual
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AlphaGeometry2 Surpasses IMO Gold Medalists in Geometry
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Gold-medalist Performance in Solving Olympiad Geometry with AlphaGeometry2 AlphaGeometry2 (AG2) surpasses IMO gold medalists in solving Olympiad geometry problems by expanding its problem domain, enhancing symbolic reasoning, and integrating a stronger language model. Problem: