works on

From the 3 of 8 linked papers with an AI index.

most citedCUDA-L1: Improving CUDA Optimization via Contrastive Reinforcement Learning

1 citations · 1 across the 4 of their papers we have counts for

collaborators

8 papers

cs.AI2026

GrandCode: Achieving Grandmaster Level in Competitive Programming via Agentic Reinforcement Learning

DeepReinforce Team, Ornith Team, Xiaoya Li +4

The paper presents GrandCode, a multi‑agent reinforcement learning system that integrates hypothesis generation, solving, test creation, and summarization modules, and uses a new A…

cs.LG2026

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

Songqiao Su, Xiaoya Li, Albert Wang +3

In this paper, we propose CUDA-L2, a system that combines large language models (LLMs) and reinforcement learning (RL) to automatically optimize Half-precision General Matrix Multi…

cs.LG2026

CRINN: Contrastive Reinforcement Learning for Approximate Nearest Neighbor Search

Xiaoya Li, Albert Wang, Guoyin Wang +2

The paper introduces CRINN, a contrastive reinforcement learning framework that automatically designs faster approximate nearest‑neighbor search algorithms while respecting accurac…

cs.AI20261 cited

CUDA-L1: Improving CUDA Optimization via Contrastive Reinforcement Learning

Xiaoya Li, Albert Wang, Guoyin Wang +2

The paper presents CUDA-L1, a reinforcement learning system that automatically optimizes CUDA code using a contrastive RL algorithm, achieving large speedups on GPU kernels without…

cs.CV2026

LongCat-Next: Lexicalizing Modalities as Discrete Tokens

Meituan LongCat Team, Bin Xiao, Chao Wang +86

The prevailing Next-Token Prediction (NTP) paradigm has driven the success of large language models through discrete autoregressive modeling. However, contemporary multimodal syste…

cs.CV2025

FaceID-6M: A Large-Scale, Open-Source FaceID Customization Dataset

Shuhe Wang, Xiaoya Li, Jiwei Li +8

Due to the data-driven nature of current face identity (FaceID) customization methods, all state-of-the-art models rely on large-scale datasets containing millions of high-quality…