This paper introduces KernelEvolve, a framework designed to automate the generation and optimization of kernels for deep learning recommendation models across various hardware platforms. It addresses challenges related to model and kernel diversity by using a graph-based search method for efficient kernel optimization. The framework has been validated on multiple NVIDIA and AMD GPUs and Meta's AI accelerators, achieving high correctness and significantly reducing development time.

WebThinker is a deep research framework that enhances large reasoning models (LRMs) by enabling them to autonomously search the web, navigate pages, and draft research reports. It integrates various features such as a Deep Web Explorer and an Autonomous Think-Search-and-Draft strategy, significantly improving the efficiency of information gathering for researchers. The framework has been recognized in academic circles, with its paper accepted at NeurIPS 2025, and is now available for deployment on platforms like Hugging Face.