SpireBench Released: First Long-Horizon Strategic Reasoning Benchmark for Agentic Models Based on Slay the Spire
Summary:arXiv introduces SpireBench, a novel benchmark for evaluating the long-horizon strategic reasoning capabilities of agentic models using the commercial deck-building game Slay the Spire. The benchmark challenges models to play the game at its highest difficulty, assessing their decision-making and strategic planning over extended periods. Results show that while frontier models can match average human performance in early stages, they fall behind expert players as the game progresses, highlightin
Key Breakthroughs
SpireBench is the first benchmark platform for evaluating the strategic reasoning capabilities of agentic models using the full commercial game Slay the Spire. Unlike traditional benchmarks that focus on short-horizon and episodic tasks, SpireBench immerses models in the complete game environment, challenging them to make decisions that have long-term consequences.
Technical Mechanism Analysis
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Game Environment Simulation: SpireBench integrates the full game logic of Slay the Spire, including level design, enemy behavior, and resource management mechanisms. Models are required to make decisions at different stages of the game, which directly affect subsequent gameplay and the final outcome.
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Long-Horizon Reasoning Challenge: Models need to engage in long-term planning across various game stages, balancing short-term gains with long-term objectives. For instance, deciding whether to sacrifice health for stronger combat abilities in the early game can have profound implications for later stages.
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Performance Evaluation Metrics: SpireBench introduces multi-dimensional evaluation metrics, such as level completion rate, resource management efficiency, and risk assessment accuracy, to comprehensively assess model performance across different aspects.
Engineering Trade-offs and Experimental Results
Experimental results show that while frontier models can match average human performance in the early stages of the game, they fall behind expert players as the game progresses. This is primarily due to issues with risk assessment and long-term planning, specifically:
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Risk Misjudgment: Models tend to be overly conservative in preserving health rather than taking risks for greater rewards.
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Ineffective Memory Utilization: Models often fail to effectively utilize historical experience recorded across runs, as the recorded memories are frequently inaccurate or overly cautious.
Developer Deployment and Recommendations
SpireBench offers developers a new tool for evaluating and improving agentic models in long-horizon complex tasks. Here are some recommendations:
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Reinforcement Learning Strategy Optimization: Developers can optimize model reinforcement learning strategies based on SpireBench's evaluation metrics to enhance performance in long-horizon tasks.
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Memory Mechanism Improvement: Enhance the model's memory mechanism to better leverage historical experience for decision-making.
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Risk Assessment Mechanism: Introduce more sophisticated risk assessment mechanisms to enable models to make more informed decisions in critical moments.
Conclusion
SpireBench provides new insights and methods for studying agentic model performance in long-horizon tasks, highlights current model deficiencies in complex decision-making, and points the way for future research.
— END —Source: ArXiv AI (cs.AI) (2026-10-12)
Tags: #Agentic Models #Long-Horizon Reasoning #Benchmark #LLMs & Foundation Models #Game AI
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