15 papers
Refined Thompson Learning for Adaptive Bandits: Sustainable Power-Efficient Flexibility Scheduling Across Data Centers
Yifu Ding, Zixi Chen, Ruicheng Ao +2
The rapid rise in energy consumption from large-scale AI workloads in data centers placed the increasing pressures on power grids in recent years. Since grids must maintain real-ti…
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…
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…
Adaptive Inference for Resource-Constrained Dynamic Pricing
Ruicheng Ao, Jiashuo Jiang, David Simchi-Levi
We study resource-constrained dynamic pricing when the seller seeks revenue and valid inference about demand at a price fixed before the selling season. Depletion can remove every…
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…
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…