1 citations · 2 across the 18 of their papers we have counts for
20 papers
Artificial intelligences and human scientists exhibit complementary strengths in theory building
Ke Li, Spyros I. Zoumpoulis, Phanish Puranam +80
We investigate the effectiveness of artificial intelligences (AI)-specifically large language models (LLMs)-relative to human scientists at high-level cognitive tasks in social sci…
Refined Thompson Learning for Adaptive Bandits: Power-Efficient Flexibility Scheduling Across Data Centers
Zixi Chen, Yifu Ding, Ruicheng Ao +2
The rapid growth of large-scale AI workloads in data centers has placed increasing pressure on power grids in recent years. Since power systems must continuously balance supply and…
Service-Induced Congestion in Memory-Constrained LLM Serving
Ruicheng Ao, Jing Dong, Gan Luo +1
In large language model (LLM) serving, each request accumulates persistent graphics processing unit (GPU) memory during service as its key-value cache grows with every generated to…
Adaptive Inference for Resource-Constrained Dynamic Pricing
Ruicheng Ao, Jiashuo Jiang, David Simchi-Levi
We study dynamic pricing over a finite selling horizon when limited resource capacity determines revenue and the observations available for inference at a prespecified price. Resou…
On the Reliability Limits of LLM-Based Multi-Agent Planning
Ruicheng Ao, Siyang Gao, David Simchi-Levi
This technical note studies the reliability limits of LLM-based multi-agent planning as a delegated decision problem. We model the LLM-based multi-agent architecture as a finite ac…
The Value of Information in Resource-Constrained Pricing
Ruicheng Ao, Jiashuo Jiang, David Simchi-Levi
Firms that price perishable resources -- airline seats, hotel rooms, seasonal inventory -- now routinely use demand predictions, but these predictions vary widely in quality. Under…
Designing Service Systems from Textual Evidence
Ruicheng Ao, Hongyu Chen, Siyang Gao +2
Designing service systems requires selecting among alternative configurations -- choosing the best chatbot variant, the optimal routing policy, or the most effective quality contro…
OptiRepair: Closed-Loop Diagnosis and Repair of Supply Chain Optimization Models with LLM Agents
Ruicheng Ao, David Simchi-Levi, Xinshang Wang
Supply chain optimization models frequently become infeasible because of modeling errors. Diagnosis and repair require scarce OR expertise: analysts must interpret solver diagnosti…
Best Arm Identification with LLM Judges and Limited Human
Ruicheng Ao, Hongyu Chen, Siyang Gao +2
We study fixed-confidence best-arm identification (BAI) where a cheap but potentially biased proxy (e.g., LLM judge) is available for every sample, while an expensive ground-truth…
PPI-SVRG: Unifying Prediction-Powered Inference and Variance Reduction for Semi-Supervised Optimization
Ruicheng Ao, Hongyu Chen, Haoyang Liu +2
We study semi-supervised stochastic optimization when labeled data is scarce but predictions from pre-trained models are available. PPI and SVRG both reduce variance through contro…
ORLoopBench: Solver-in-the-Loop Benchmarks for Self-Correction and Behavioral Rationality in Operations Research
Ruicheng Ao, David Simchi-Levi, Xinshang Wang
Operations Research practitioners debug infeasible models through an iterative process: inspecting Irreducible Infeasible Subsystems ( IIS), identifying constraint conflicts, and r…
Decision-dependent Robust Charging Infrastructure Planning for Light-duty Truck Electrification at Industrial Sites
Yifu Ding, Ruicheng Ao, Pablo Duenas-Martinez +1
Many industrial sites and digital logistics platforms rely on diesel-powered light-duty trucks to transport workers and small-scale facilities, which results in a significant amoun…
Optimizing LLM Inference: Fluid-Guided Online Scheduling with Memory Constraints
Ruicheng Ao, Gan Luo, David Simchi-Levi +1
Large language models now serve millions of users daily, with providers incurring costs exceeding $700,000 per day. Each request requires token-by-token inference, making GPU sched…
Learning to Price with Resource Constraints: From Full Information to Machine-Learned Prices
Ruicheng Ao, Jiashuo Jiang, David Simchi-Levi
We study the dynamic pricing problem with knapsack, addressing the challenge of balancing exploration and exploitation under resource constraints. We introduce three algorithms tai…
Prediction-Guided Active Experiments
Ruicheng Ao, Hongyu Chen, David Simchi-Levi
In this work, we introduce a new framework for active experimentation, the Prediction-Guided Active Experiment (PGAE), which leverages predictions from an existing machine learning…
Two-stage Online Reusable Resource Allocation: Reservation, Overbooking and Confirmation Call
Ruicheng Ao, Hengyu Fu, David Simchi-levi
We study a two-stage online reusable resource allocation problem over T days involving advance reservations and walk-ins. Each day begins with a reservation stage (Stage I), where…
Online Resource Allocation with Average Budget Constraints
Ruicheng Ao, Hongyu Chen, David Simchi-Levi +1
We consider the problem of online resource allocation with average budget constraints. At each time point the decision maker makes an irrevocable decision of whether to accept or r…
Monte Carlo Policy Gradient Method for Binary Optimization
Cheng Chen, Ruitao Chen, Tianyou Li +2
Binary optimization has a wide range of applications in combinatorial optimization problems such as MaxCut, MIMO detection, and MaxSAT. However, these problems are typically NP-har…
Asynchronous Gradient Play in Zero-Sum Multi-agent Games
Ruicheng Ao, Shicong Cen, Yuejie Chi
Finding equilibria via gradient play in competitive multi-agent games has been attracting a growing amount of attention in recent years, with emphasis on designing efficient strate…
Riemannian Natural Gradient Methods
Jiang Hu, Ruicheng Ao, Anthony Man-Cho So +2
This paper studies large-scale optimization problems on Riemannian manifolds whose objective function is a finite sum of negative log-probability losses. Such problems arise in var…