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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2508.01623 |
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| _version_ | 1866911088613785600 |
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| author | Yashwanth, Tadisetty Sai C, Dhatri |
| author_facet | Yashwanth, Tadisetty Sai C, Dhatri |
| contents | This research presents LLM Pokemon League, a competitive tournament system that leverages Large Language Models (LLMs) as intelligent agents to simulate strategic decision-making in Pokémon battles. The platform is designed to analyze and compare the reasoning, adaptability, and tactical depth exhibited by different LLMs in a type-based, turn-based combat environment. By structuring the competition as a single-elimination tournament involving diverse AI trainers, the system captures detailed decision logs, including team-building rationale, action selection strategies, and switching decisions. The project enables rich exploration into comparative AI behavior, battle psychology, and meta-strategy development in constrained, rule-based game environments. Through this system, we investigate how modern LLMs understand, adapt, and optimize decisions under uncertainty, making Pokémon League a novel benchmark for AI research in strategic reasoning and competitive learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_01623 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Multi-Agent Pokemon Tournament for Evaluating Strategic Reasoning of Large Language Models Yashwanth, Tadisetty Sai C, Dhatri Artificial Intelligence This research presents LLM Pokemon League, a competitive tournament system that leverages Large Language Models (LLMs) as intelligent agents to simulate strategic decision-making in Pokémon battles. The platform is designed to analyze and compare the reasoning, adaptability, and tactical depth exhibited by different LLMs in a type-based, turn-based combat environment. By structuring the competition as a single-elimination tournament involving diverse AI trainers, the system captures detailed decision logs, including team-building rationale, action selection strategies, and switching decisions. The project enables rich exploration into comparative AI behavior, battle psychology, and meta-strategy development in constrained, rule-based game environments. Through this system, we investigate how modern LLMs understand, adapt, and optimize decisions under uncertainty, making Pokémon League a novel benchmark for AI research in strategic reasoning and competitive learning. |
| title | A Multi-Agent Pokemon Tournament for Evaluating Strategic Reasoning of Large Language Models |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2508.01623 |