Shiwei Li

The paper introduces the Data-centric Reasoning Compiler (DCRC), a novel data-driven framework that enhances financial QA systems by compiling user queries and retrieved documents into verifiable, executable programs to prevent numerical hallucinations.

The paper introduces SLORR, a simple and stateless framework for in-training low-rank regularization of neural networks using two variants based on the Hoyer sparsity metric and the nuclear norm.

This paper introduces Isospectral Optimization (ISO), a new optimization framework for reinforcement learning with verifiable rewards (RLVR), which inherits the base model's weight spectra while optimizing the frames to achieve stronger performance.