{"total":64,"rows":[{"id":"2308.08155","title":"AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation","created":"2023-08-16","cited_by_count":1988,"area":"agents"},{"id":"2404.15155","title":"MDAgents: An Adaptive Collaboration of LLMs for Medical Decision-Making","created":"2024-04-22","cited_by_count":163,"area":null},{"id":"2402.10172","title":"OptiMUS: Scalable Optimization Modeling with (MI)LP Solvers and Large Language Models","created":"2024-02-15","cited_by_count":109,"area":null},{"id":"2410.13213","title":"LLMOPT: Learning to Define and Solve General Optimization Problems from Scratch","created":"2024-10-17","cited_by_count":102,"area":null},{"id":"2401.08406","title":"RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture","created":"2024-01-16","cited_by_count":81,"area":"rag"},{"id":"2407.19633","title":"OptiMUS-0.3: Using Large Language Models to Model and Solve Optimization Problems at Scale","created":"2024-07-29","cited_by_count":38,"area":null},{"id":"2410.12112","title":"Planning Anything with Rigor: General-Purpose Zero-Shot Planning with LLM-based Formalized Programming","created":"2024-10-15","cited_by_count":35,"area":null},{"id":"2402.00891","title":"Large Language Models in Cybersecurity: State-of-the-Art","created":"2024-01-30","cited_by_count":34,"area":null},{"id":"2410.08288","title":"Towards Foundation Models for Mixed Integer Linear Programming","created":"2024-10-10","cited_by_count":31,"area":null},{"id":"2508.10047","title":"A Survey of Optimization Modeling Meets LLMs: Progress and Future Directions","created":"2025-08-12","cited_by_count":31,"area":null},{"id":"2405.03547","title":"Position: Leverage Foundational Models for Black-Box Optimization","created":"2024-05-06","cited_by_count":21,"area":null},{"id":"2411.10184","title":"Agentic LLMs in the Supply Chain: Towards Autonomous Multi-Agent Consensus-Seeking","created":"2024-11-15","cited_by_count":16,"area":"agents"},{"id":"2403.01757","title":"How Multimodal Integration Boost the Performance of LLM for Optimization: Case Study on Capacitated Vehicle Routing Problems","created":"2024-03-04","cited_by_count":12,"area":"mllm"},{"id":"2402.16269","title":"From Large Language Models and Optimization to Decision Optimization CoPilot: A Research Manifesto","created":"2024-02-26","cited_by_count":11,"area":null},{"id":"2404.02934","title":"GreedLlama: Performance of Financial Value-Aligned Large Language Models in Moral Reasoning","created":"2024-04-03","cited_by_count":9,"area":"lr"},{"id":"2502.09994","title":"Decision Information Meets Large Language Models: The Future of Explainable Operations Research","created":"2025-02-14","cited_by_count":9,"area":null},{"id":"2502.14760","title":"EquivaMap: Leveraging LLMs for Automatic Equivalence Checking of Optimization Formulations","created":"2025-02-20","cited_by_count":9,"area":null},{"id":"2407.00476","title":"Large Language Models for Power Scheduling: A User-Centric Approach","created":"2024-06-29","cited_by_count":8,"area":null},{"id":"2412.12038","title":"LLMs for Cold-Start Cutting Plane Separator Configuration","created":"2024-12-16","cited_by_count":8,"area":null},{"id":"2505.04799","title":"Maris: A Formally Verifiable Privacy Policy Enforcement Paradigm for Multi-Agent Collaboration Systems","created":"2025-05-07","cited_by_count":8,"area":"agents"},{"id":"2510.05115","title":"SAC-Opt: Semantic Anchors for Iterative Correction in Optimization Modeling","created":"2025-09-28","cited_by_count":8,"area":null},{"id":"2602.12631","title":"AI Agents for Inventory Control: Human-LLM-OR Complementarity","created":"2026-02-13","cited_by_count":8,"area":"agents"},{"id":"2412.08072","title":"Using Large Language Models for Parametric Shape Optimization","created":"2024-12-11","cited_by_count":7,"area":null},{"id":"2502.09933","title":"MIR-Bench: Can Your LLM Recognize Complicated Patterns via Many-Shot In-Context Reasoning?","created":"2025-02-14","cited_by_count":7,"area":"lr"},{"id":"2509.18180","title":"Large Language Models in Operations Research: Methods, Applications, and Challenges","created":"2025-09-18","cited_by_count":6,"area":null},{"id":"2405.20347","title":"Small Language Models for Application Interactions: A Case Study","created":"2024-05-23","cited_by_count":5,"area":null},{"id":"2510.03182","title":"Simulation to Rules: A Dual-VLM Framework for Formal Visual Planning","created":"2025-10-03","cited_by_count":5,"area":"mllm"},{"id":"2408.07272","title":"Abstract Operations Research Modeling Using Natural Language Inputs","created":"2024-08-14","cited_by_count":4,"area":null},{"id":"2508.16077","title":"Cooperative Design Optimization through Natural Language Interaction","created":"2025-08-22","cited_by_count":4,"area":null},{"id":"2509.03811","title":"Rethinking Supply Chain Planning: A Generative Paradigm","created":"2025-09-04","cited_by_count":4,"area":null},{"id":"2506.12958","title":"Domain Specific Benchmarks for Evaluating Multimodal Large Language Models","created":"2025-06-15","cited_by_count":3,"area":"mllm"},{"id":"2508.21622","title":"Integrating Large Language Models with Network Optimization for Interactive and Explainable Supply Chain Planning: A Real-World Case Study","created":"2025-08-29","cited_by_count":3,"area":null},{"id":"2511.16383","title":"An Agent-Based Framework for the Automatic Validation of Mathematical Optimization Models","created":"2025-11-20","cited_by_count":3,"area":"agents"},{"id":"2311.12241","title":"InteraSSort: Interactive Assortment Planning Using Large Language Models","created":"2023-11-20","cited_by_count":2,"area":null},{"id":"2405.15115","title":"Towards Better Understanding of In-Context Learning Ability from In-Context Uncertainty Quantification","created":"2024-05-24","cited_by_count":2,"area":null},{"id":"2506.10527","title":"LogiPlan: A Structured Benchmark for Logical Planning and Relational Reasoning in LLMs","created":"2025-06-12","cited_by_count":2,"area":"lr"},{"id":"2510.17491","title":"Empowering Real-World: A Survey on the Technology, Practice, and Evaluation of LLM-driven Industry Agents","created":"2025-10-20","cited_by_count":2,"area":"lr"},{"id":"2602.00549","title":"Beyond the Node: Clade-level Selection for Efficient MCTS in Automatic Heuristic Design","created":"2026-01-31","cited_by_count":2,"area":null},{"id":"2602.02029","title":"Canonical Intermediate Representation for LLM-based optimization problem formulation and code generation","created":"2026-02-02","cited_by_count":2,"area":null},{"id":"2605.02728","title":"ORPilot: A Production-Oriented Agentic LLM-for-OR Tool for Optimization Modeling","created":"2026-05-04","cited_by_count":2,"area":"agents"},{"id":"2605.18692","title":"Democratizing Large-Scale Re-Optimization with LLM-Guided Model Patches","created":"2026-05-18","cited_by_count":2,"area":null},{"id":"2607.20474","title":"VeriSimpl: Robust Optimization Modeling from Natural Language using Simplification-based Verification","created":"2026-05-24","cited_by_count":2,"area":null},{"id":"2412.19088","title":"Integrating Artificial Open Generative Artificial Intelligence into Software Supply Chain Security","created":"2024-12-26","cited_by_count":1,"area":null},{"id":"2502.08886","title":"Generative AI for Internet of Things Security: Challenges and Opportunities","created":"2025-02-13","cited_by_count":1,"area":null},{"id":"2508.13942","title":"The Collaboration Paradox: Why Generative AI Requires Both Strategic Intelligence and Operational Stability in Supply Chain Management","created":"2025-08-19","cited_by_count":1,"area":null},{"id":"2508.18091","title":"Teaching LLMs to Think Mathematically: A Critical Study of Decision-Making via Optimization","created":"2025-08-25","cited_by_count":1,"area":"lr"},{"id":"2605.00369","title":"InvEvolve: Evolving White-Box Inventory Policies via Large Language Models with Performance Guarantees","created":"2026-05-01","cited_by_count":1,"area":null},{"id":"2607.05347","title":"WildSplat: Feedforward Gaussian Splatting from Unposed In-the-Wild Images","created":"2026-07-06","cited_by_count":1,"area":null},{"id":"2608.25220","title":"FLARE: Verifying MILP Reformulations with LLM-Based Theorem Proving","created":"2026-08-25","cited_by_count":1,"area":null},{"id":"2403.00822","title":"InteraRec: Screenshot Based Recommendations Using Multimodal Large Language Models","created":"2024-02-26","cited_by_count":0,"area":null},{"id":"2506.17484","title":"From Unstructured Communication to Intelligent RAG: Multi-Agent Automation for Supply Chain Knowledge Bases","created":"2025-06-20","cited_by_count":0,"area":"rag"},{"id":"2506.17637","title":"Step-Opt: Boosting Optimization Modeling in LLMs through Iterative Data Synthesis and Structured Validation","created":"2025-06-21","cited_by_count":0,"area":"synth"},{"id":"2602.08377","title":"Reinforcement Learning with Backtracking Feedback","created":"2026-02-09","cited_by_count":0,"area":"alignment"},{"id":"2604.12421","title":"Agentic Insight Generation in VSM Simulations","created":"2026-04-14","cited_by_count":0,"area":"agents"},{"id":"2604.14590","title":"AgileLog: A Forkable Shared Log for Agents on Data Streams","created":"2026-04-16","cited_by_count":0,"area":"agents"},{"id":"2604.17708","title":"Co-evolving Agent Architectures and Interpretable Reasoning for Automated Optimization","created":"2026-04-20","cited_by_count":0,"area":"lr"},{"id":"2605.15223","title":"GenAI-Driven Approach to RISC-V Supply Chain Exploration","created":"2026-05-13","cited_by_count":0,"area":null},{"id":"2606.15577","title":"Large Language Models as Optimizers: A Survey of Direct vs. Tool-Augmented Approaches and Their Performance Frontiers","created":"2026-04-09","cited_by_count":0,"area":null},{"id":"2606.16183","title":"LLM-Powered Virtual Population for Demand Simulation and Pricing","created":"2026-06-15","cited_by_count":0,"area":null},{"id":"2607.11980","title":"LP Mining with LP2Graph: A Use Case for Railway Rescheduling","created":"2026-07-13","cited_by_count":0,"area":null},{"id":"2609.12863","title":"GenOR-Twin: A Semantic Middleware for Integrating Operational Discourse with Mathematical Optimization","created":"2026-09-11","cited_by_count":0,"area":null},{"id":"2609.13945","title":"SAILOR: Solver-Assisted Interactive LLM-based Optimization Recovery","created":"2026-09-12","cited_by_count":0,"area":null},{"id":"2610.02014","title":"Atoms to Processes: The Role of Artificial Intelligence and Machine Learning in Chemical Engineering","created":"2026-10-01","cited_by_count":0,"area":null},{"id":"2610.14736","title":"Think in Strategies, Act with Tools: Traceable Agentic Optimization for Combinatorial Problems","created":"2026-10-10","cited_by_count":0,"area":"lr"}],"next_cursor":null}