Second Workshop on MLxOR: Mathematical Foundations and Operational Integration of Machine Learning for Uncertainty-Aware Decision-Making

We propose a second MLxOR workshop, building on the momentum of the inaugural workshop in 2025, with a more focused and timely theme "decision-making with GenAI+OR". The inaugural workshop was motivated by the two-way synergy between operations research (OR) and AI/ML. Through tools ranging from stochastic modeling and simulation to optimization, the model-based approach of OR is capable of translating explicit modeling assumptions and principled methodologies into interpretable and risk-quantifiable solutions, making OR central to reliable decision-making across a wide range of industry applications. At the same time, the analytical tractability of OR models also restricts the level of real-world complexity that they can tackle. To this end, rapid advances in AI/ML offer a powerful opportunity to complement traditional OR and substantially improve decision performance, while, conversely, decades of rigorous OR research can help address key challenges surrounding black-box systems in AI/ML by providing principled tools to analyze their performances and risks in high-stakes applications. The proposed second MLxOR workshop will focus on the above two-way OR-ML synergy in the context of "GenAI", which broadly refers to generative models, including language models, diffusion models, and related foundation models, that can represent, generate, and reason over complex, multimodal scenarios and actions. The emergence of GenAI has already begun to reshape research in OR and shown promise in producing decisions at a scale and complexity previously unattainable, yet it also raises fundamental challenges around evaluation, reliability and safety that counteract the core principles of OR. This theme is thus urgent and impactful in steering the future OR direction, and the proposed workshop aims to address the bottlenecks via collective community discussion and effort. Specifically, the workshop will focus on the following subtopics: 1) Adaptive generative model for operational decision-making; 2) Integration of GenAI into data/ML-driven optimization; 3) Assessing and hedging failure risks of GenAI via rare-event methods and vice versa; and 4) Experimentation and evaluation interweaving GenAI and digital twins. Through these subtopics, the goal is to bring together researchers from diverse backgrounds to develop a shared understanding of the emerging challenges and opportunities at the GenAI-OR interface, ultimately laying rigorous foundations for reliable, uncertainty-aware, and resource-efficient decision-making.