Trending Research
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).
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.
Multimodal Chain-of-Thought Reasoning: A Comprehensive Survey
By extending the advantage of chain-of-thought (CoT) reasoning in human-like step-by-step processes to multimodal contexts, multimodal CoT (MCoT) reasoning has recently garnered significant research attention, especially in the integration with multimodal large language models (MLLMs).
Niagara: Normal-Integrated Geometric Affine Field for Scene Reconstruction from a Single View
xianzuwu/Niagara
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Recent advances in single-view 3D scene reconstruction have highlighted the challenges in capturing fine geometric details and ensuring structural consistency, particularly in high-fidelity outdoor scene modeling.
Light-R1: Curriculum SFT, DPO and RL for Long COT from Scratch and Beyond
The Light-R1 series of work validates training long-COT models from scratch, showcases the art in SFT data and releases SOTA models from RL.
HybridFlow: A Flexible and Efficient RLHF Framework
volcengine/verl
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Traditional RL can be modeled as a dataflow, where each node represents computation of a neural network (NN) and each edge denotes data dependencies between the NNs.
YOLOE: Real-Time Seeing Anything
THU-MIG/yoloe
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Object detection and segmentation are widely employed in computer vision applications, yet conventional models like YOLO series, while efficient and accurate, are limited by predefined categories, hindering adaptability in open scenarios.
Zero-shot Voice Conversion with Diffusion Transformers
Plachtaa/seed-vc
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Zero-shot voice conversion aims to transform a source speech utterance to match the timbre of a reference speech from an unseen speaker.
Seeing the Future, Perceiving the Future: A Unified Driving World Model for Future Generation and Perception
Extensive experiments on the nuScenes dataset demonstrate that UniFuture outperforms specialized models on future generation and perception tasks, highlighting the advantages of a unified, structurally-aware world model.
