4 papers
Do LLMs Build Spatial World Models? Evidence from Grid-World Maze Tasks
Weijiang Li, Yilin Zhu, Rajarshi Das +1
Foundation models have shown remarkable performance across diverse tasks, yet their ability to construct internal spatial world models for reasoning and planning remains unclear. W…
Charting Empirical Laws for LLM Fine-Tuning in Scientific Multi-Discipline Learning
Lintao Wang, Zhuqiang Lu, Yilin Zhu +6
While large language models (LLMs) have achieved strong performance through fine-tuning within individual scientific domains, their learning dynamics in multi-disciplinary contexts…
Optimization over Trained Neural Networks: Going Large with Gradient-Based Algorithms
Jiatai Tong, Yilin Zhu, Thiago Serra +1
When optimizing a nonlinear objective, one can employ a neural network as a surrogate for the nonlinear function. However, the resulting optimization model can be time-consuming to…
An Extended Validity Domain for Constraint Learning
Yilin Zhu, Samuel Burer
We consider embedding a predictive machine-learning model within a prescriptive optimization problem. In this setting, called constraint learning, we study the concept of a validit…