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SymbolicArena: A Unified Infrastructure for Benchmark Distillation and Dynamic Evaluation in Symbolic Regression
Ziwen Zhang, Xiju Wu, Yuheng Jing +8
Symbolic regression (SR) seeks concise and interpretable mathematical expressions from data for scientific equation discovery. Existing SR benchmarks face a tradeoff between evalua…
Not Every Term Adds New Structure: Sobolev Novelty for Symbolic Regression
Boxiao Wang, Kai Li, Yuheng Jing +4
Symbolic regression (SR) aims to discover compact and meaningful mathematical equations from data, but searching the vast combinatorial space of symbolic structures remains challen…
MOT-SR: Multi-Objective Tool-Augmented Scientific Equation Discovery with Large Language Models
Boxiao Wang, Runxiang Wang, Kai Li +4
Symbolic Regression (SR) aims to discover analytical equations from observational data and plays a central role in scientific modeling. While recent Large Language Model (LLM) base…
AutoPref: Automatic Discovery of Task-Specific Preference Objectives for Neural Combinatorial Optimization
Shengda Gu, Kai Li, Xinyi Ke +3
Combinatorial optimization problems (COPs) underpin many real-world decisions, but their exponentially large search spaces make high-quality solutions costly to obtain. Neural comb…
EvoPINN: Agentic Discovery of Executable Algorithms for Physics-Informed Neural Networks
Peng Yin, Kai Li, Yifan Zhang +1
Physics-informed neural networks (PINNs) have emerged as a powerful paradigm for solving partial differential equations (PDEs), yet their performance heavily relies on the manual,…
RefineEvo: Planning-Guided Heuristic Evolution with Bidirectional Experience
Yang Wu, Junran Pan, Yifan Zhang +3
Automatic Heuristic Design (AHD) has emerged as a transformative approach for solving combinatorial optimization problems. While recent Large Language Model (LLM)-based methods hav…
Mean Flow Policy Optimization
Xiaoyi Dong, Xi Sheryl Zhang, Jian Cheng
Diffusion models have recently emerged as expressive policy representations for online reinforcement learning (RL). However, their iterative generative processes introduce substant…
Benchmarking the Limits of In-Context Reinforcement Learning for Ad-Hoc Teamwork
Yuheng Jing, Kai Li, Ziwen Zhang +8
In-Context Reinforcement Learning (ICRL) has enabled foundation agents to adapt instantaneously to novel tasks, yet its efficacy in Ad-Hoc Teamwork (AHT)-where coordination with un…
When Good Equations Get Bad Scores: Improving Symbolic Regression Through Better Parameter Optimization
Boxiao Wang, Kai Li, Zhiwei Chen +5
Symbolic Regression (SR) plays a central role in scientific knowledge discovery by distilling mathematical equations from observational data. Most existing SR methods function with…
Game-Theoretic Co-Evolution for LLM-Based Heuristic Discovery
Xinyi Ke, Kai Li, Junliang Xing +2
Large language models (LLMs) have enabled rapid progress in automatic heuristic discovery (AHD), yet most existing methods are predominantly limited by static evaluation against fi…
LLM-Based Scientific Equation Discovery via Physics-Informed Token-Regularized Policy Optimization
Boxiao Wang, Kai Li, Tianyi Liu +4
Symbolic regression aims to distill mathematical equations from observational data. Recent approaches have successfully leveraged Large Language Models (LLMs) to generate equation…