oh hey look an expert at Agentic Operations Research @LawlessOpt working (and teaching) at an Agentic Operations Research company

An LLM can rewrite your optimization problem to improve solve time, pass every test, and still silently change the underlying problem. Our new paper introduces FLARE, an LLM agent that uses Lean to prove that a given MILP reformulation is valid across all possible problem instances, not just those used for testing. On 54 reformulation pairs of NP-hard problems, FLARE correctly classified all (best baseline: 83%) and provided a machine-checkable proof for every accepted reformulation. AI-generated optimization needs proofs; passing tests isn’t enough.

In 2024, we made the unprecedented decision to acquire a hospital system. It was an opportunity to create a blueprint for healthcare transformation to make American healthcare proactive, accessible, and affordable. @SummaHealth and our Health Assurance Transformation Company (HATCo) revealed their technology transformation stack, partnering with some of our portfolio companies @aidocmed, Clarium, @CommureOs, @poweredbyfabric, @hippocraticai, @judihealth, @transcarent, and @versemedical. @percepta is serving as a transformation partner, unifying the tech stack into a seamless Al-native operating system. We believe the future of healthcare is health assurance with tech founders and health system leaders working together to reimagine healthcare in the US and beyond.

Super excited about this project from my time at @MSFTResearch! We explore how to combine LLMs and Constraint Programming to allow non-expert users to design optimization models in the context of meeting scheduling.

"I Want It That Way": Enabling Interactive Decision Support Using Large Language Models and Constraint Programming paper page: huggingface.co/papers/2312.0… A critical factor in the success of decision support systems is the accurate modeling of user preferences. Psychology research has demonstrated that users often develop their preferences during the elicitation process, highlighting the pivotal role of system-user interaction in developing personalized systems. This paper introduces a novel approach, combining Large Language Models (LLMs) with Constraint Programming to facilitate interactive decision support. We study this hybrid framework through the lens of meeting scheduling, a time-consuming daily activity faced by a multitude of information workers. We conduct three studies to evaluate the novel framework, including a diary study (n=64) to characterize contextual scheduling preferences, a quantitative evaluation of the system's performance, and a user study (n=10) with a prototype system. Our work highlights the potential for a hybrid LLM and optimization approach for iterative preference elicitation and design considerations for building systems that support human-system collaborative decision-making processes.

🧵RT 🙏 SEASON5 of the #MachineLearning NeEDS #MathematicalOptimization Online Seminar Series, congreso.us.es/mlneedsmo/, has ended! We thank the 8⃣8⃣ speakers from 2⃣1⃣ countries that we have had since 2021 ! @REA_research @EUROonline_News @imus_us @CBS_ECON @needs_project #orms