AgenticSTS, an arXiv paper submitted on July 2, 2026, introduces a bounded-memory testbed for long-horizon LLM agents in Slay the Spire 2. Instead of letting every decision inherit a growing transcript, the system assembles each prompt through typed retrieval, so memory layers can be ablated cleanly. The authors report a directional 3/10 to 6/10 win-rate gain when a strategic-skill memory layer is enabled, while noting the sample is not statistically decisive. For practitioners, the useful part is the release of 298 trajectories, memory snapshots, prompt records, and scripts that make agent-memory designs easier to compare.
