Trending Research
Human-like Episodic Memory for Infinite Context LLMs
Large language models (LLMs) have shown remarkable capabilities, but still struggle with processing extensive contexts, limiting their ability to maintain coherence and accuracy over long sequences.
OpenThinkIMG: Learning to Think with Images via Visual Tool Reinforcement Learning
We hope OpenThinkIMG can serve as a foundational framework for advancing dynamic, tool-augmented visual reasoning, helping the community develop AI agents that can genuinely "think with images".
Fully Open Source Moxin-7B Technical Report
Recently, Large Language Models (LLMs) have undergone a significant transformation, marked by a rapid rise in both their popularity and capabilities.
Flow-GRPO: Training Flow Matching Models via Online RL
We propose Flow-GRPO, the first method integrating online reinforcement learning (RL) into flow matching models.
Ranked #1 on
Text-to-Image Generation
on GenEval
UniVLA: Learning to Act Anywhere with Task-centric Latent Actions
Learned from internet-scale videos, the generalist policy can be deployed to various robots through efficient latent action decoding.
Generating Physically Stable and Buildable LEGO Designs from Text
Our experiments show that LegoGPT produces stable, diverse, and aesthetically pleasing LEGO designs that align closely with the input text prompts.
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.
Attentive Reasoning Queries: A Systematic Method for Optimizing Instruction-Following in Large Language Models
We present Attentive Reasoning Queries (ARQs), a novel structured reasoning approach that significantly improves instruction-following in Large Language Models through domain-specialized reasoning blueprints.
Aligning Anime Video Generation with Human Feedback
Existing reward models, designed primarily for real-world videos, fail to capture the unique appearance and consistency requirements of anime.
LTX-Video: Realtime Video Latent Diffusion
To address this, our VAE decoder is tasked with both latent-to-pixel conversion and the final denoising step, producing the clean result directly in pixel space.
