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arXiv · 2604.07733

CivBench: Progress-Based Evaluation for LLMs' Strategic Decision-Making in Civilization V

John Chen·Sihan Cheng·Can Gurkan·Mingyi Lin
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Abstract

Evaluating strategic decision-making in LLM-based agents requires generative, competitive, and longitudinal environments, yet few benchmarks provide all three, and fewer still offer evaluation signals rich enough for long-horizon, multi-agent play. We introduce CivBench, a benchmark for LLM strategists (a model under an agentic setup) in multiplayer Civilization V. Because terminal win/loss is too sparse a signal in games spanning hundreds of turns and multiple opponents, CivBench trains estimators of a state-value function, the probability that a player eventually wins given the game state. cross 307 games with 7 LLMs and multiple CivBench agent conditions, we demonstrate CivBench's potential to estimate strategic capabilities in Civilization V as an unsaturated benchmark, reveal model-specific effects of agentic setup, and outline distinct strategic profiles not visible through outcome-only evaluation.

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John Chen, Sihan Cheng, Can Gurkan, Mingyi Lin. 2026-09-18. CivBench: Progress-Based Evaluation for LLMs' Strategic Decision-Making in Civilization V. https://arxiv.org/abs/2604.07733

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