6 papers
LLM-Derived Preference Judgments Are Not Self-Consistent
Matthew T. Ford, Francis Bahk, Jingjing Wang +4
Agents increasingly interpret a person's natural-language preferences by querying an LLM for numerical preference judgments, e.g., by asking how much the person would be willing to…
Learning Optimization Proxies for Sequential Contextual Stochastic Programs: An Order Fulfillment Application
Tinghan Ye, Shuaicheng Tong, Changkun Guan +2
Sequential contextual stochastic programs model real-time decision systems in which each time epoch commits to an action under uncertainty whose consequences propagate into future…
Democratizing Large-Scale Re-Optimization with LLM-Guided Model Patches
Tinghan Ye, Arnaud Deza, Ved Mohan +2
Optimization models developed by operations research (OR) experts are often deployed as decision-support systems in industrial settings. However, real-world environments are dynami…
HawkesLLM: Semantic Uncertainty Propagation in Agentic Text Simulation
Zewei Deng, Tinghan Ye, Liyan Xie
Agentic text-simulation systems write in sequence, with each item becoming possible context for later steps. That makes uncertainty path-dependent: an early ambiguity can affect la…
LISTEN to Your Preferences: An LLM Framework for Multi-Objective Selection
Adam S. Jovine, Tinghan Ye, Francis Bahk +4
Human experts often struggle to select the best option from a large set of items with multiple competing objectives, a process bottlenecked by the difficulty of formalizing complex…
Cornell University Uses Integer Programming to Optimize Final Exam Scheduling
Tinghan Ye, Adam S. Jovine, Willem van Osselaer +2
This paper presents an integer programming-based optimization framework designed to effectively address the complex final exam scheduling challenges encountered at Cornell Universi…