AI Agents for Inventory Control: Human-LLM-OR Complementarity

• URL: http://arxiv.org/abs/2602.12631v1
• Date: Fri, 13 Feb 2026 05:23:46 GMT
• Title: AI Agents for Inventory Control: Human-LLM-OR Complementarity
• Abstract summary: Large language models (LLMs) have generated interest in AI agents that can reason flexibly and incorporate rich contextual signals.<n>We study how OR algorithms, LLMs, and humans can interact and complement each other in a multi-period inventory control setting.<n>We show that, on average, human-AI teams achieve higher profits than either humans or AI agents operating alone.
• Score: 12.448705668487852
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Inventory control is a fundamental operations problem in which ordering decisions are traditionally guided by theoretically grounded operations research (OR) algorithms. However, such algorithms often rely on rigid modeling assumptions and can perform poorly when demand distributions shift or relevant contextual information is unavailable. Recent advances in large language models (LLMs) have generated interest in AI agents that can reason flexibly and incorporate rich contextual signals, but it remains unclear how best to incorporate LLM-based methods into traditional decision-making pipelines. We study how OR algorithms, LLMs, and humans can interact and complement each other in a multi-period inventory control setting. We construct InventoryBench, a benchmark of over 1,000 inventory instances spanning both synthetic and real-world demand data, designed to stress-test decision rules under demand shifts, seasonality, and uncertain lead times. Through this benchmark, we find that OR-augmented LLM methods outperform either method in isolation, suggesting that these methods are complementary rather than substitutes. We further investigate the role of humans through a controlled classroom experiment that embeds LLM recommendations into a human-in-the-loop decision pipeline. Contrary to prior findings that human-AI collaboration can degrade performance, we show that, on average, human-AI teams achieve higher profits than either humans or AI agents operating alone. Beyond this population-level finding, we formalize an individual-level complementarity effect and derive a distribution-free lower bound on the fraction of individuals who benefit from AI collaboration; empirically, we find this fraction to be substantial.

Related papers

• Ask, Clarify, Optimize: Human-LLM Agent Collaboration for Smarter Inventory Control [11.796330722859574]
  We show that employing LLMs as end-to-end solvers incurs a significant "hallucination tax"<n>We propose a hybrid agentic framework that strictly decouples semantic reasoning from mathematical calculation.<n>Our results position LLMs as natural-language interfaces that make rigorous, solver-based policies accessible to non-experts.
  arXiv  Detail & Related papers  (2025-12-31T21:45:54Z)
• ORPR: An OR-Guided Pretrain-then-Reinforce Learning Model for Inventory Management [9.138155308817215]
  "Pretrain-then-Reinforce" approach reconciles AI's adaptive perception with Operations Research's structural rigor.<n>We show that a lightweight, domain-informed model can deliver state-of-the-art performance and robust transferability when guided by structured OR logic.
  arXiv  Detail & Related papers  (2025-12-22T03:39:43Z)
• Beyond Fast and Slow: Cognitive-Inspired Elastic Reasoning for Large Language Models [39.03483371038282]
  CogER is a framework inspired by human hierarchical reasoning.<n>For queries requiring external tools, we introduce Cognitive Tool-Assisted Reasoning.<n>CogER outperforms state-of-the-art Test-Time scaling methods.
  arXiv  Detail & Related papers  (2025-12-17T05:11:58Z)
• SelfAI: Building a Self-Training AI System with LLM Agents [79.10991818561907]
  SelfAI is a general multi-agent platform that combines a User Agent for translating high-level research objectives into standardized experimental configurations.<n>An Experiment Manager orchestrates parallel, fault-tolerant training across heterogeneous hardware while maintaining a structured knowledge base for continuous feedback.<n>Across regression, computer vision, scientific computing, medical imaging, and drug discovery benchmarks, SelfAI consistently achieves strong performance and reduces redundant trials.
  arXiv  Detail & Related papers  (2025-11-29T09:18:39Z)
• Agentic Reinforced Policy Optimization [66.96989268893932]
  Large-scale reinforcement learning with verifiable rewards (RLVR) has demonstrated its effectiveness in harnessing the potential of large language models (LLMs) for single-turn reasoning tasks.<n>Current RL algorithms inadequately balance the models' intrinsic long-horizon reasoning capabilities and their proficiency in multi-turn tool interactions.<n>We propose Agentic Reinforced Policy Optimization (ARPO), a novel agentic RL algorithm tailored for training multi-turn LLM-based agents.
  arXiv  Detail & Related papers  (2025-07-26T07:53:11Z)
• Comparing Exploration-Exploitation Strategies of LLMs and Humans: Insights from Standard Multi-armed Bandit Experiments [5.1382713576243955]
  Large language models (LLMs) are increasingly used to simulate or automate human behavior in sequential decision-making settings.<n>We focus on the exploration-exploitation (E&E) tradeoff, a fundamental aspect of dynamic decision-making under uncertainty.<n>We find that enabling thinking in LLMs shifts their behavior toward more human-like behavior, characterized by a mix of random and directed exploration.
  arXiv  Detail & Related papers  (2025-05-15T02:09:18Z)
• Human aversion? Do AI Agents Judge Identity More Harshly Than Performance [0.06554326244334868]
  We investigate how AI agents based on large language models assess and integrate human input.<n>We find that the AI system systematically discounts human advice, penalizing human errors more severely than algorithmic errors.
  arXiv  Detail & Related papers  (2025-03-31T02:05:27Z)
• EVOLvE: Evaluating and Optimizing LLMs For In-Context Exploration [76.66831821738927]
  Large language models (LLMs) remain under-studied in scenarios requiring optimal decision-making under uncertainty.<n>We measure LLMs' (in)ability to make optimal decisions in bandits, a state-less reinforcement learning setting relevant to many applications.<n>Motivated by the existence of optimal exploration algorithms, we propose efficient ways to integrate this algorithmic knowledge into LLMs.
  arXiv  Detail & Related papers  (2024-10-08T17:54:03Z)
• How Can LLM Guide RL? A Value-Based Approach [68.55316627400683]
  Reinforcement learning (RL) has become the de facto standard practice for sequential decision-making problems by improving future acting policies with feedback. Recent developments in large language models (LLMs) have showcased impressive capabilities in language understanding and generation, yet they fall short in exploration and self-improvement capabilities. We develop an algorithm named LINVIT that incorporates LLM guidance as a regularization factor in value-based RL, leading to significant reductions in the amount of data needed for learning.
  arXiv  Detail & Related papers  (2024-02-25T20:07:13Z)
• Large Language Model-based Human-Agent Collaboration for Complex Task Solving [94.3914058341565]
  We introduce the problem of Large Language Models (LLMs)-based human-agent collaboration for complex task-solving. We propose a Reinforcement Learning-based Human-Agent Collaboration method, ReHAC. This approach includes a policy model designed to determine the most opportune stages for human intervention within the task-solving process.
  arXiv  Detail & Related papers  (2024-02-20T11:03:36Z)
• SALMON: Self-Alignment with Instructable Reward Models [80.83323636730341]
  This paper presents a novel approach, namely SALMON, to align base language models with minimal human supervision. We develop an AI assistant named Dromedary-2 with only 6 exemplars for in-context learning and 31 human-defined principles.
  arXiv  Detail & Related papers  (2023-10-09T17:56:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.