Publications (6)
Governing Equation Discovery from Data Based on Differential Invariants
Lexiang Hu, Yikang Li, Zhouchen Lin
The explicit governing equation is one of the simplest and most intuitive forms for characterizing physical laws. However, directly discovering partial differential equations (PDEs…
Symmetry Discovery for Different Data Types
Lexiang Hu, Yikang Li, Zhouchen Lin
Equivariant neural networks incorporate symmetries into their architecture, achieving higher generalization performance. However, constructing equivariant neural networks typically…
Adaptive Global and Fine-Grained Perceptual Fusion for MLLM Embeddings Compatible with Hard Negative Amplification
Lexiang Hu, Youze Xue, Dian Li +2
Multimodal embeddings serve as a bridge for aligning vision and language, with the two primary implementations -- CLIP-based and MLLM-based embedding models -- both limited to capt…
CORE-Bench: A Comprehensive Benchmark for Code Retrieval in the Era of Agentic Coding
Fuwei Zhang, Yanzhao Zhang, Mingxin Li +5
The paper presents CORE-Bench, a large benchmark designed to evaluate code retrieval tasks needed by coding agents, covering code understanding, issue-to-edit localization, and bro…
Explicit Discovery of Nonlinear Symmetries from Dynamic Data
Lexiang Hu, Yikang Li, Zhouchen Lin
Symmetry is widely applied in problems such as the design of equivariant networks and the discovery of governing equations, but in complex scenarios, it is not known in advance. Mo…
Incorporating Arbitrary Matrix Group Equivariance into KANs
Lexiang Hu, Yisen Wang, Zhouchen Lin
Kolmogorov-Arnold Networks (KANs) have seen great success in scientific domains thanks to spline activation functions, becoming an alternative to Multi-Layer Perceptrons (MLPs). Ho…