DECE：基于十款 Eurogame 的 LLM 智能体统一竞技场
A Unified Arena for LLM Agents across Diverse Eurogame
一群研究者把十款桌游打包成评测场，跑了 1032 场对局，发现同一个模型换个执行框架成绩就不一样，还附成本账。
arXiv 论文提出 DECE（Diverse Eurogame Competitive Environments），一个覆盖十款桌游的 LLM 智能体统一评测竞技场，共享接口与评测协议。主研究测试了 12 组模型-执行框架配置，涉及 9 个模型和 4 种执行框架，在十款游戏中进行 480 场三人对局，加上补充研究共完成 1032 场对局。结果显示各游戏间排名大体一致，但同一模型在不同执行框架下表现不同。对局表现的优势也不必然带来更好的 token 和成本效率，论文主张同时从竞技成绩和资源消耗两方面评测完整智能体配置。
A Unified Arena for LLM Agents across Diverse Eurogame
Interactive environments offer a practical way to evaluate large language model (LLM) agents, and competitive games extend this evaluation to decisions shaped by competing goals and other agents' choices. Yet existing evaluations confined to individual games or execution frameworks leave unclear which competitive advantages persist across settings. We introduce DECE (Diverse Eurogame Competitive Environments), a unified arena spanning ten Eurogames with shared interfaces and evaluation protocols. These games combine lasting strategic commitments, competition over shared resources, and multiple routes to victory. Our primary study evaluates 12 model-harness configurations, spanning nine models and four execution harnesses, in 480 three-player matches across ten games. The main and supplementary studies comprise 1,032 completed matches in total. These comparisons reveal broad cross-game ranking agreement alongside game-specific strengths. Matched studies show that the same model can perform differently across execution harnesses, adding another source of variation to these profiles. These performance advantages do not consistently translate into better token or cost efficiency, making resource use an additional consideration when comparing configurations. Together, the findings support evaluating complete agent configurations by both their competitive performance across games and the resources needed to achieve it.