RetailBench: Evaluating Long-Horizon Autonomous Decision-Making and Strategy Stability of LLM Agents in Realistic Retail Environments
arXiv:2603.16453v3 Announce Type: replace Abstract: Large language model (LLM) agents have made rapid progress on short-horizon, well-scoped tasks, yet their ability to sustain coherent decisions in dynamic long-horizon environments remains uncertain. We introduce RetailBench, a data-grounded simulation benchmark for evaluating tool-using LLM agents in single-store supermarket operation. RetailBench models retail management as a partially observable decision process and is designed to support thousand-day-scale simulations. In this environment, agents must manage pricing, replenishment, suppli
The rapid advancement of LLMs has reached a point where their practical application in complex, long-horizon decision-making environments is becoming a critical area of research and evaluation.
This benchmark addresses a core limitation of current LLM agents, moving beyond short-horizon tasks to evaluate sustained, coherent autonomy, which is crucial for real-world business integration.
The development of robust benchmarks like RetailBench will accelerate the refinement of LLM agents for practical, autonomous decision-making in dynamic environments, shifting focus from narrow tasks to systemic operational management.
- · AI developers
- · Retail sector
- · SaaS providers
- · LLM researchers
- · Manual retail management processes
- · Inefficient operational systems
RetailBench will become a standard for evaluating the advanced capabilities of LLM agents in complex operational settings.
This will lead to more sophisticated, truly autonomous AI systems being deployed in various industries beyond retail, capable of managing entire workflows.
The demonstrated stability of long-horizon AI decision-making will accelerate the collapse of white-collar workflows and necessitate new economic models.
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Read at arXiv cs.AI