CivBench: Progress-Based Evaluation for LLMs' Strategic Decision-Making in Civilization V
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 pote...
| Authors | John Chen, Sihan Cheng, Can Gurkan et al. |
|---|---|
| Primary category | cs.AI |
| Published | 9 April 2026 |
| Last updated at source | 18 September 2026 |
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