most citedLocalized Zeroth-Order Prompt Optimization

1 citations · 1 across the 6 of their papers we have counts for

collaborators

6 papers

cs.AI2026

A foundation model of numerical intelligence with cross-disciplinary generalization

Chenghan Wu, Zongmin Yu, Liu Yang

Intelligence is commonly understood as the ability to acquire and apply knowledge, adapt to unfamiliar situations and solve new problems. Large language models exhibit this capacit…

cs.LG2026

Agentic Symbolic Search: Characterizing PDEs Beyond Hand-crafted Expressions, Meshes, and Neural Networks

Zongmin Yu, Liu Yang

Mathematicians understand a PDE solution through mathematical structures rather than tables of computed values. Historically, this has been the product of mathematical analysis, ca…

cs.AI2026

Self-Evolving Scientific Agent Designs Physically Reasoned White-Box Fluid Control

Boai Sun, Wenjin Guo, Zongmin Yu +1

While neural networks excel in autonomous control, their black-box nature makes control decisions difficult to interpret and diagnose in dynamic fluids. Here, we show how self-evol…

cs.NE2026

Evolving Ensemble of Agents

Zongmin Yu, Liu Yang

We introduce the Evolving Ensemble of Agents (EvE), a decentralized framework that organizes existing, highly capable coding agents into a live, co-evolving system for algorithmic…

cs.LG2026

Graph In-Context Operator Networks for Generalizable Spatiotemporal Prediction

Chenghan Wu, Zongmin Yu, Boai Sun +1

In-context operator learning enables neural networks to infer solution operators from contextual examples without weight updates. While prior work has demonstrated the effectivenes…

cs.AI20241 cited

Localized Zeroth-Order Prompt Optimization

Wenyang Hu, Yao Shu, Zongmin Yu +5

The efficacy of large language models (LLMs) in understanding and generating natural language has aroused a wide interest in developing prompt-based methods to harness the power of…