9 citations · 12 across the 8 of their papers we have counts for
8 papers
Deep Adaptive Dimension Reduction for Bayesian Inference in Inverse Problems
Yueyang Wang, Xili Wang, Kejun Tang +3
Solving high-dimensional PDE-governed inverse problems is often challenging due to complex non-Gaussian posterior distributions, expensive forward model evaluations, and misspecifi…
How Should LLMs Consume High-Quality Data? Optimal Data Scheduling via Quality-Aware Functional Scaling Laws
Zhitao Zhu, Xili Wang, Shizhe Wu +2
High-quality data is scarce in large language model (LLM) training, yet how to schedule its use with optimization dynamics lacks theoretical guidance. We extend functional scaling…
HE-SNR: Uncovering Latent Logic via Entropy for Guiding Mid-Training on SWE-bench
Yueyang Wang, Jiawei Fu, Baolong Bi +2
SWE-bench has emerged as the premier benchmark for evaluating Large Language Models on complex software engineering tasks. While these capabilities are fundamentally acquired durin…
LongCat-Flash Technical Report
Meituan LongCat Team, Bayan, Bei Li +179
We introduce LongCat-Flash, a 560-billion-parameter Mixture-of-Experts (MoE) language model designed for both computational efficiency and advanced agentic capabilities. Stemming f…
MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use
Weikang Zhao, Xili Wang, Chengdi Ma +6
With the recent rapid advancement of Agentic Intelligence, agentic tool use in LLMs has become increasingly important. During multi-turn interactions between agents and users, the…
Estimating Committor Functions via Deep Adaptive Sampling on Rare Transition Paths
Yueyang Wang, Kejun Tang, Xili Wang +3
The committor functions are central to investigating rare but important events in molecular simulations. It is known that computing the committor function suffers from the curse of…
Deep adaptive sampling for surrogate modeling without labeled data
Xili Wang, Kejun Tang, Jiayu Zhai +2
Surrogate modeling is of great practical significance for parametric differential equation systems. In contrast to classical numerical methods, using physics-informed deep learning…
AONN-2: An adjoint-oriented neural network method for PDE-constrained shape optimization
Xili Wang, Pengfei Yin, Bo Zhang +1
Shape optimization has been playing an important role in a large variety of engineering applications. Existing shape optimization methods are generally mesh-dependent and therefore…