1 citations · 2 across the 18 of their papers we have counts for
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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…