# StoreBench

The authors introduce StoreBench, a long-horizon environment in which an agent runs a mid-size online apparel store for 30 to 365 simulated days on a production-grade Medusa v2 commerce backend, using 29 merchant tools under an operation-metered time budget with anchor-calibrated pass thresholds and an exploit-hardened reward; no frontier model matches the scripted smart-triage policy on average (best DeepSeek-V4-Pro 0.700 vs. 0.764), human experts edge out every model at 0.708, most models improve sharply over a full simulated year under Claude Code, and a GRPO post-training run lifts Qwen3.5-27B from 0.136 to 0.373 on held-out tasks.

The authors introduce StoreBench, a long-horizon environment in which an agent runs a mid-size online apparel store for 30 to 365 simulated days on a production-grade Medusa v2 commerce backend, using 29 merchant tools under an operation-metered time budget with anchor-calibrated pass thresholds and an exploit-hardened reward; no frontier model matches the scripted smart-triage policy on average (best DeepSeek-V4-Pro 0.700 vs. 0.764), human experts edge out every model at 0.708, most models improve sharply over a full simulated year under Claude Code, and a GRPO post-training run lifts Qwen3.5-27B from 0.136 to 0.373 on held-out tasks.