gym-invmgmt is a new benchmarking framework that evaluates inventory policies across optimization and learning methods, finding stochastic programming strongest among non-oracle approaches and PPO-Transformer best among learned ones in tested scenarios.
InvAgent: Alargelanguagemodelbasedmulti-agentinventorymanagementsystem.arXiv preprint arXiv:2407.11384v1
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
representative citing papers
InvEvolve evolves white-box inventory policies from LLMs with statistical safety guarantees and outperforms classical and deep learning methods on synthetic and real retail data.
EconWebArena is a new benchmark with 360 curated economic tasks across 82 authoritative websites for evaluating multimodal web agents on navigation, grounding, and data extraction.
Heterogeneous LLM agents in supply chain simulations exhibit myopic self-interested behaviors that worsen inefficiencies, but information sharing mitigates these effects.
LLM Orchestration integrates modality experts via an LLM controller, cross-modal memory, and interaction layer to enable multimodal input-output without gradient-based training.
citing papers explorer
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gym-invmgmt: An Open Benchmarking Framework for Inventory Management Methods
gym-invmgmt is a new benchmarking framework that evaluates inventory policies across optimization and learning methods, finding stochastic programming strongest among non-oracle approaches and PPO-Transformer best among learned ones in tested scenarios.
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InvEvolve: Evolving White-Box Inventory Policies via Large Language Models with Performance Guarantees
InvEvolve evolves white-box inventory policies from LLMs with statistical safety guarantees and outperforms classical and deep learning methods on synthetic and real retail data.
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EconWebArena: Benchmarking Autonomous Agents on Economic Tasks in Realistic Web Environments
EconWebArena is a new benchmark with 360 curated economic tasks across 82 authoritative websites for evaluating multimodal web agents on navigation, grounding, and data extraction.
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Dynamics of Cognitive Heterogeneity: Investigating Behavioral Biases in Multi-Stage Supply Chains with LLM-Based Simulation
Heterogeneous LLM agents in supply chain simulations exhibit myopic self-interested behaviors that worsen inefficiencies, but information sharing mitigates these effects.
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Training-Free Multimodal Large Language Model Orchestration
LLM Orchestration integrates modality experts via an LLM controller, cross-modal memory, and interaction layer to enable multimodal input-output without gradient-based training.
- Reliability and Effectiveness of Autonomous AI Agents in Supply Chain Management