Copertina

OptiRepair: Closed-Loop Diagnosis and Repair of Supply Chain Optimization Models with LLM Agents

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Ao, Ruicheng, Simchi-Levi, David, Wang, Xinshang
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Artificial Intelligence Machine Learning Optimization and Control
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

ORLoopBench: Solver-in-the-Loop Benchmarks for Self-Correction and Behavioral Rationality in Operations Research
di: Ao, Ruicheng, et al.
Pubblicazione: (2026)

Optimizing LLM Inference: Fluid-Guided Online Scheduling with Memory Constraints
di: Ao, Ruicheng, et al.
Pubblicazione: (2025)

Designing Service Systems from Textual Evidence
di: Ao, Ruicheng, et al.
Pubblicazione: (2026)

On the Reliability Limits of LLM-Based Multi-Agent Planning
di: Ao, Ruicheng, et al.
Pubblicazione: (2026)

Learning to Price with Resource Constraints: From Full Information to Machine-Learned Prices
di: Ao, Ruicheng, et al.
Pubblicazione: (2025)

The Value of Information in Resource-Constrained Pricing
di: Ao, Ruicheng, et al.
Pubblicazione: (2026)

Best Arm Identification with LLM Judges and Limited Human
di: Ao, Ruicheng, et al.
Pubblicazione: (2026)

PPI-SVRG: Unifying Prediction-Powered Inference and Variance Reduction for Semi-Supervised Optimization
di: Ao, Ruicheng, et al.
Pubblicazione: (2026)

Online Resource Allocation with Average Budget Constraints
di: Ao, Ruicheng, et al.
Pubblicazione: (2024)

Stochastic Optimization of Inventory at Large-scale Supply Chains
di: Jin, Zhaoyang Larry, et al.
Pubblicazione: (2025)

OptiLoop: Coordination-in-the-Loop Verification and Repair for LLM-Generated Optimization Agents
di: Xu, Yujia, et al.
Pubblicazione: (2026)

From Soliloquy to Agora: Memory-Enhanced LLM Agents with Decentralized Debate for Optimization Modeling
di: Lin, Jianghao, et al.
Pubblicazione: (2026)

Reliability and Effectiveness of Autonomous AI Agents in Supply Chain Management
di: Long, Carol Xuan, et al.
Pubblicazione: (2026)

Closing the Loop: Coordinating Inventory and Recommendation via Deep Reinforcement Learning on Multiple Timescales
di: Jiang, Jinyang, et al.
Pubblicazione: (2025)

ReLoop: Structured Modeling and Behavioral Verification for Reliable LLM-Based Optimization
di: Lian, Junbo Jacob, et al.
Pubblicazione: (2026)

Classical and Deep Reinforcement Learning Inventory Control Policies for Pharmaceutical Supply Chains with Perishability and Non-Stationarity
di: Stranieri, Francesco, et al.
Pubblicazione: (2025)

LLM Serving Optimization with Variable Prefill and Decode Lengths
di: Wang, Meixuan, et al.
Pubblicazione: (2025)

Optimizer-Model Consistency: Full Finetuning with the Same Optimizer as Pretraining Forgets Less
di: Liu, Yuxing, et al.
Pubblicazione: (2026)

Memory-Efficient LLM Pretraining via Minimalist Optimizer Design
di: Glentis, Athanasios, et al.
Pubblicazione: (2025)

Adaptively Robust LLM Inference Optimization under Prediction Uncertainty
di: Chen, Zixi, et al.
Pubblicazione: (2025)

Two-stage Online Reusable Resource Allocation: Reservation, Overbooking and Confirmation Call
di: Ao, Ruicheng, et al.
Pubblicazione: (2024)

Assured autonomy: How operations research powers and orchestrates generative AI systems
di: Dai, Tinglong, et al.
Pubblicazione: (2025)

ARO: A New Lens On Matrix Optimization For Large Models
di: Gong, Wenbo, et al.
Pubblicazione: (2026)

OptiBench Meets ReSocratic: Measure and Improve LLMs for Optimization Modeling
di: Yang, Zhicheng, et al.
Pubblicazione: (2024)

Constructing Industrial-Scale Optimization Modeling Benchmark
di: Li, Zhong, et al.
Pubblicazione: (2026)

From Large Language Models and Optimization to Decision Optimization CoPilot: A Research Manifesto
di: Wasserkrug, Segev, et al.
Pubblicazione: (2024)

A Minimalist Bayesian Framework for Stochastic Optimization
di: Wang, Kaizheng
Pubblicazione: (2025)

Transformers Provably Learn Chain-of-Thought Reasoning with Length Generalization
di: Huang, Yu, et al.
Pubblicazione: (2025)

Designing Information Delays in Supply Chains
di: Talwai, Prem, et al.
Pubblicazione: (2026)

MiMuon: Mixed Muon Optimizer with Improved Generalization for Large Models
di: Huang, Feihu, et al.
Pubblicazione: (2026)

Isotropic Curvature Model for Understanding Deep Learning Optimization: Is Gradient Orthogonalization Optimal?
di: Su, Weijie
Pubblicazione: (2025)

Unsupervised Training of Diffusion Models for Feasible Solution Generation in Neural Combinatorial Optimization
di: Hong, Seong-Hyun, et al.
Pubblicazione: (2024)

Uncovering Symmetry Transfer in Large Language Models via Layer-Peeled Optimization
di: Du, Zhehang, et al.
Pubblicazione: (2026)

DT-PBO: an Interpretable Tree-based Surrogate Model for Preferential Bayesian Optimization
di: Leenders, Nick, et al.
Pubblicazione: (2025)

A Single-Loop Gradient Descent and Perturbed Ascent Algorithm for Nonconvex Functional Constrained Optimization
di: Lu, Songtao
Pubblicazione: (2022)

Contextual Distributionally Robust Optimization with Causal and Continuous Structure: An Interpretable and Tractable Approach
di: Zhang, Fenglin, et al.
Pubblicazione: (2026)

Benchmarking PtO and PnO Methods in the Predictive Combinatorial Optimization Regime
di: Geng, Haoyu, et al.
Pubblicazione: (2023)

Recursive Entropic Risk Optimization in Discounted MDPs: Sample Complexity Bounds with a Generative Model
di: Mortensen, Oliver, et al.
Pubblicazione: (2025)

Optimizing the Optimizer for Physics-Informed Neural Networks and Kolmogorov-Arnold Networks
di: Kiyani, Elham, et al.
Pubblicazione: (2025)

Demystifying Manifold Constraints in LLM Pre-training
di: An, Kang, et al.
Pubblicazione: (2026)