Trending Research
Cube: A Roblox View of 3D Intelligence
We show how our tokenization scheme can be used in applications for text-to-shape generation, shape-to-text generation and text-to-scene generation.
Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning
Efficiently acquiring external knowledge and up-to-date information is essential for effective reasoning and text generation in large language models (LLMs).
Reinforcement Learning for Reasoning in Small LLMs: What Works and What Doesn't
Enhancing the reasoning capabilities of large language models (LLMs) typically relies on massive computational resources and extensive datasets, limiting accessibility for resource-constrained settings.
Neural Fields with Thermal Activations for Arbitrary-Scale Super-Resolution
prs-eth/thera
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We present a novel way to design neural fields such that points can be queried with an adaptive Gaussian PSF, so as to guarantee correct anti-aliasing at any desired output resolution.
Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation
This system includes two foundation components: a large-scale shape generation model -- Hunyuan3D-DiT, and a large-scale texture synthesis model -- Hunyuan3D-Paint.
Cosmos-Reason1: From Physical Common Sense To Embodied Reasoning
We begin by defining key capabilities for Physical AI reasoning, with a focus on physical common sense and embodied reasoning.
Data Formulator 2: Iterative Creation of Data Visualizations, with AI Transforming Data Along the Way
Data analysts often need to iterate between data transformations and chart designs to create rich visualizations for exploratory data analysis.
Sonata: Self-Supervised Learning of Reliable Point Representations
In this paper, we question whether we have a reliable self-supervised point cloud model that can be used for diverse 3D tasks via simple linear probing, even with limited data and minimal computation.
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Semantic Segmentation
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The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery
This approach signifies the beginning of a new era in scientific discovery in machine learning: bringing the transformative benefits of AI agents to the entire research process of AI itself, and taking us closer to a world where endless affordable creativity and innovation can be unleashed on the world's most challenging problems.
Tokenize Image as a Set
This paper proposes a fundamentally new paradigm for image generation through set-based tokenization and distribution modeling.
