Enterprise AI systems are increasingly expected to handle complex, multistep decisions over extended periods, but most benchmarks focus on short, well-scoped tasks. A new research benchmark called RetailBench aims to close that gap by testing large language model (LLM) agents in a realistic, data-grounded retail simulation spanning 180 days.
What RetailBench Simulates
According to the paper published on arXiv, RetailBench models a single-store supermarket as a partially observable decision process. Agents must manage pricing, replenishment, supplier selection, shelf assortment, inventory aging, customer feedback, external events, and cash-flow constraints — all while operating under a thousand-day-scale simulation capability.
The benchmark is designed to measure long-horizon reasoning and coherent decision making, which are critical for applications in supply chain and retail operations. The environment is data-grounded, meaning decisions are tied to realistic inventory and financial dynamics.
| Decision Area | Description |
|---|---|
| Pricing | Setting product prices dynamically |
| Replenishment | Ordering stock from suppliers |
| Supplier Selection | Choosing among multiple vendors |
| Shelf Assortment | Deciding which products to display |
| Inventory Aging | Managing perishable and non-perishable goods |
| Customer Feedback | Reacting to ratings and complaints |
| External Events | Adapting to holidays, weather, or disruptions |
| Cash Flow | Ensuring sufficient liquidity for operations |
How LLMs Performed
The researchers evaluated seven contemporary LLMs under representative agent frameworks over a 180-day evaluation horizon. They compared results against a privileged oracle policy that has full knowledge of the environment.
The findings, as reported in the paper, show substantial variation across models. Only a small subset of LLMs survived the full evaluation horizon without bankruptcy or major failure. Even the strongest LLM run remained substantially behind the oracle policy in both final net worth and sales outcomes.
Behavioral Shortcomings
Through behavioral analysis, the authors identified three main reasons for the performance gap:
- Incomplete evidence acquisition: Agents failed to gather sufficient information before making decisions.
- Surface-level decision making: Agents relied on shallow heuristics rather than deep analysis.
- Lack of a consistent long-horizon policy: Strategies changed erratically over time, undermining cumulative performance.
These findings highlight that current LLM agents still struggle with sustained autonomous decision making in economically grounded environments, according to the paper.
Implications for Enterprise AI
For enterprise technology leaders evaluating AI for supply chain or retail automation, RetailBench provides a controlled testbed for studying reliable autonomy. The benchmark's focus on long-horizon reasoning is directly relevant to inventory optimization, demand forecasting, and financial planning.
The paper suggests that developing agents capable of coherent multi-step decisions under uncertainty remains an open challenge. Until LLMs can match oracle-level performance in such simulations, human oversight will likely remain necessary for critical retail and supply chain decisions.
RetailBench is available under a CC0 license (public domain), making it freely accessible for research and development. The code, data, and media are linked from the arXiv paper, allowing organizations to reproduce the experiments or extend the benchmark to their own use cases.