RetailBench: Evaluating Long-Horizon Autonomous Decision-Making and Strategy Stability of LLM Agents in Realistic Retail Environments
#RetailBench #LLM agents #autonomous decision-making #strategy stability #retail environments
📌 Key Takeaways
- RetailBench is a new benchmark for evaluating LLM agents in retail environments.
- It focuses on long-horizon autonomous decision-making capabilities.
- It assesses the stability of strategies over extended periods.
- The benchmark uses realistic retail scenarios for testing.
📖 Full Retelling
arXiv:2603.16453v1 Announce Type: new
Abstract: Large Language Model (LLM)-based agents have achieved notable success on short-horizon and highly structured tasks. However, their ability to maintain coherent decision-making over long horizons in realistic and dynamic environments remains an open challenge.
We introduce RetailBench, a high-fidelity benchmark designed to evaluate long-horizon autonomous decision-making in realistic commercial scenarios, where agents must operate under stochasti
🏷️ Themes
AI Evaluation, Retail Technology
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Original Source
arXiv:2603.16453v1 Announce Type: new
Abstract: Large Language Model (LLM)-based agents have achieved notable success on short-horizon and highly structured tasks. However, their ability to maintain coherent decision-making over long horizons in realistic and dynamic environments remains an open challenge.
We introduce RetailBench, a high-fidelity benchmark designed to evaluate long-horizon autonomous decision-making in realistic commercial scenarios, where agents must operate under stochasti
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