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[2602.13214] BotzoneBench: Scalable LLM Evaluation via Graded AI Anchors
Summary
The paper presents BotzoneBench, a scalable framework for evaluating Large Language Models (LLMs) using graded AI anchors, addressing the limitations of existing benchmarks in assessing dynamic strategic reasoning.
Why It Matters
As LLMs are increasingly utilized in interactive environments, a reliable evaluation framework is crucial for understanding their strategic capabilities. BotzoneBench offers a solution by providing stable performance metrics against fixed skill hierarchies, which can enhance the development and deployment of LLMs in various applications.
Key Takeaways
- BotzoneBench enables linear-time evaluation of LLMs against stable AI benchmarks.
- The framework assesses LLMs across diverse games, revealing significant performance disparities.
- It establishes a reusable evaluation paradigm applicable beyond gaming to any domain with defined skill hierarchies.
- Top-performing LLMs demonstrate strategic capabilities comparable to specialized game AI.
- The approach enhances longitudinal tracking of LLM performance over time.
Computer Science > Artificial Intelligence arXiv:2602.13214 (cs) [Submitted on 22 Jan 2026] Title:BotzoneBench: Scalable LLM Evaluation via Graded AI Anchors Authors:Lingfeng Li, Yunlong Lu, Yuefei Zhang, Jingyu Yao, Yixin Zhu, KeYuan Cheng, Yongyi Wang, Qirui Zheng, Xionghui Yang, Wenxin Li View a PDF of the paper titled BotzoneBench: Scalable LLM Evaluation via Graded AI Anchors, by Lingfeng Li and 9 other authors View PDF HTML (experimental) Abstract:Large Language Models (LLMs) are increasingly deployed in interactive environments requiring strategic decision-making, yet systematic evaluation of these capabilities remains challenging. Existing benchmarks for LLMs primarily assess static reasoning through isolated tasks and fail to capture dynamic strategic abilities. Recent game-based evaluations employ LLM-vs-LLM tournaments that produce relative rankings dependent on transient model pools, incurring quadratic computational costs and lacking stable performance anchors for longitudinal tracking. The central challenge is establishing a scalable evaluation framework that measures LLM strategic reasoning against consistent, interpretable standards rather than volatile peer models. Here we show that anchoring LLM evaluation to fixed hierarchies of skill-calibrated game Artificial Intelligence (AI) enables linear-time absolute skill measurement with stable cross-temporal interpretability. Built on the Botzone platform's established competitive infrastructure, our BotzoneBen...