GENSTRAT: Toward a Science of Strategic Reasoning in Large Language Models
The paper titled GENSTRAT introduces a new approach to evaluate strategic reasoning in large language models (LLMs). It highlights the limitations of existing benchmarks and proposes a method using procedurally generated strategic environments. The study evaluates various LLMs in a competitive setting, revealing differences in their capability profiles despite similar overall performance.
- ▪GENSTRAT addresses challenges in anticipating the behavior of large language models in economic settings.
- ▪The study generates a distribution of two-player zero-sum imperfect-information card games for evaluation.
- ▪Nine frontier and open-weight LLMs were tested in a tournament with over 36,000 matches, showing varied capability profiles.
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Computer Science > Artificial Intelligence arXiv:2605.23238 (cs) [Submitted on 22 May 2026] Title:GENSTRAT: Toward a Science of Strategic Reasoning in Large Language Models Authors:Vartan Shadarevian, Kia Ghods, Alex Kenich, Anany Kotawala View a PDF of the paper titled GENSTRAT: Toward a Science of Strategic Reasoning in Large Language Models, by Vartan Shadarevian and 3 other authors View PDF HTML (experimental) Abstract:Large language models (LLMs) are increasingly deployed as economic agents in marketplaces, auctions, and bidding settings. Anticipating their behavior in any specific deployment is hard. Existing strategic-reasoning benchmarks evaluate models on fixed canonical games.
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