Trending Research
SOAP: Style-Omniscient Animatable Portraits
Creating animatable 3D avatars from a single image remains challenging due to style limitations (realistic, cartoon, anime) and difficulties in handling accessories or hairstyles.
Paper2Code: Automating Code Generation from Scientific Papers in Machine Learning
Despite the rapid growth of machine learning research, corresponding code implementations are often unavailable, making it slow and labor-intensive for researchers to reproduce results and build upon prior work.
Continuous Thought Machines
SakanaAI/continuous-thought-machines
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The CTM has two core innovations: (1) neuron-level temporal processing, where each neuron uses unique weight parameters to process a history of incoming signals; and (2) neural synchronization employed as a latent representation.
HealthBench: Evaluating Large Language Models Towards Improved Human Health
We present HealthBench, an open-source benchmark measuring the performance and safety of large language models in healthcare.
AlphaNet: Scaling Up Local-frame-based Atomistic Interatomic Potential
zmyybc/alphanet
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Molecular dynamics simulations demand an unprecedented combination of accuracy and scalability to tackle grand challenges in catalysis and materials design.
Spherical Channels for Modeling Atomic Interactions
We propose the Spherical Channel Network (SCN) to model atomic energies and forces.
Absolute Zero: Reinforced Self-play Reasoning with Zero Data
LeapLabTHU/Absolute-Zero-Reasoner
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Reinforcement learning with verifiable rewards (RLVR) has shown promise in enhancing the reasoning capabilities of large language models by learning directly from outcome-based rewards.
SkyReels-V2: Infinite-length Film Generative Model
Recent advances in video generation have been driven by diffusion models and autoregressive frameworks, yet critical challenges persist in harmonizing prompt adherence, visual quality, motion dynamics, and duration: compromises in motion dynamics to enhance temporal visual quality, constrained video duration (5-10 seconds) to prioritize resolution, and inadequate shot-aware generation stemming from general-purpose MLLMs' inability to interpret cinematic grammar, such as shot composition, actor expressions, and camera motions.
MTVCrafter: 4D Motion Tokenization for Open-World Human Image Animation
To tackle this problem, we propose MTVCrafter (Motion Tokenization Video Crafter), the first framework that directly models raw 3D motion sequences (i. e., 4D motion) for human image animation.
Finetune-RAG: Fine-Tuning Language Models to Resist Hallucination in Retrieval-Augmented Generation
Pints-AI/Finetune-Bench-RAG
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Retrieval-Augmented Generation (RAG) has emerged as a powerful framework to improve factuality in large language models (LLMs) by grounding their outputs in retrieved documents.
