SpireBench: Evaluating Long-Horizon Strategic Play of Agentic Models with Slay the Spire
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.
This paper introduces SpireBench, a benchmark for evaluating long-horizon strategic reasoning in agentic large language models through gameplay of the deck-building game Slay the Spire. The study analyzes model performance, knowledge retention, and decision-making over extended tasks.
What happened
Researchers developed SpireBench to assess how well agentic models handle long-term strategic planning in the game Slay the Spire. They tested 20 large language models at maximum difficulty, finding that while models match average human performance, they fall short of expert players. The study reveals models prioritize health preservation over strategic risk-taking and fail to effectively utilize cross-run memories.
Why it matters
Long-horizon strategic reasoning is critical for real-world AI applications requiring sustained planning. This benchmark helps identify limitations in current models' ability to balance immediate decisions with long-term goals, informing future research directions.
- Current agentic models struggle with sustained strategic planning beyond short-term tasks
- Gameplay benchmarks like SpireBench reveal critical limitations in AI decision-making frameworks
- Cross-run memory utilization remains a significant challenge for long-horizon tasks
- Risk assessment and resource management are key areas needing improvement
- Standardized benchmarks are essential for advancing AI reasoning capabilities
The bigger picture
The benchmark provides a standardized method to evaluate and improve AI systems' long-term planning capabilities, which is essential for applications in complex, dynamic environments.
SpireBench establishes a new benchmark for evaluating long-horizon strategic reasoning in agentic models. While current systems show promise in short-term tasks, they face significant challenges in maintaining effective strategies over extended periods, highlighting areas for future research and development.
Technical details
- Slay the Spire gameplay mechanics involving resource management and strategic decision-making
- Evaluation framework for measuring model performance across extended task durations
- Analysis of knowledge retention between game runs
- Comparison of heuristic baselines vs. agentic model strategies
- Assessment of risk calculation and decision-making trade-offs
FAQ
What is SpireBench and why was it created?
SpireBench is a benchmark for evaluating long-horizon strategic reasoning in agentic models. It was created to address the lack of comprehensive testing for AI systems' ability to make strategic decisions over extended tasks, using the deck-building game Slay the Spire as a testbed.
How do agentic models perform in long-term gameplay?
Frontier models outperform heuristic baselines but still lag behind human experts. They show a tendency to prioritize health preservation over strategic risk-taking, and their performance declines as task duration increases.
What challenges do AI models face in long-horizon tasks?
Models struggle with risk assessment, fail to effectively utilize cross-run memories, and have difficulty balancing immediate decisions with long-term goals. These challenges highlight limitations in current AI reasoning frameworks.
This summary and analysis were generated with AI from the original article and may contain errors. Read the original for the full story.
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Originally published by arXiv - cs.AI on Oct 12, 2026.
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