SpireBench: Evaluating Long-Horizon Strategic Play of Agentic Models with Slay the Spire

A new arXiv paper (2610.12596v1) introduces SpireBench, a long-horizon benchmark in which 20 large language models play the complete commercial deck-builder Slay the Spire at its highest difficulty. Frontier models beat heuristic baselines and match the average human player but remain far behind experts, with the gap widening as runs lengthen; a self-consistent renaming of every game term showed part of their performance rests on prior knowledge of the game, and models did not improve from memories written across runs, which were often wrong and overly cautious.

arXiv:2610.12596v1 Announce Type: new Abstract: Agentic large language models now tackle complex tasks, yet strategic reasoning over long horizons is under-studied: most agentic benchmarks are short and episodic, and performance on long tasks is hard to dissect into stored knowledge, reasoning and luck. We introduce SpireBench, in which models play the complete commercial deck-builder Slay the Spire StS , where early decisions decide long-term success. Across 20 large language models LLMs at the game's highest difficulty, frontier models beat heuristic baselines and match the average human player but remain far behind experts. This gap widens as the run lengthens: the strongest models win the first act as often as experts, then fall behind. What separates strong models from weak is how well they play each fight; all of them misjudge risk over a run, preserving health rather than spending it to grow stronger. A comprehensive, self-consistent renaming of every game term shows that part of their performance rests on prior knowledge of the game, and models do not improve from the memories they write across runs, which are often wrong and overly cautious. Our harness is the first to give a model the complete original game, and we compare it with existing harnesses. Overall, SpireBench offers an unsaturated long-horizon benchmark with tools to locate where and why agents fail.