世界杯竞技场：语言模型与深度研究智能体在足球预测任务上的细粒度评估

摘要

在开球前预测一场足球比赛需要的不仅仅是了解过往结果：模型必须利用不断变化的信息，并在答案揭晓前做出明确的预测。我们提出 WorldCupArena，一个面向语言模型和深度研究代理的动态基准。2026年国际足联世界杯是其首次评估，同样的流程也可用于未来的联赛和杯赛。在每场比赛前，模型要么接收一份通用证据包，要么自行搜索信息。它预测比赛结果和比分、可能的球员和事件、比赛统计数据以及赛事的最终结果。比赛结束后，这些预测将与记录的实际结果进行对比。我们报告比赛结果准确率、精确比分准确率，以及一个比分接近得分（当预测比分接近但不精确时给予一定分数），同时还报告其他预测任务的得分。在104场比赛和13个系统的测试中，结果准确率相近的模型在细节预测上的差异更为明显。与博彩市场和人类球迷的基线相比，最佳系统在比赛结果和精确比分准确率上仅略有提升，但在Scoreline上提升更明显。新的赛程可以在开始时添加，使得该基准能够评估未来的模型，而无需使用已知的结果。代码、提示词、预测和评估脚本已在 https://github.com/wzk1015/WorldCupArena 开源。

English

Predicting a football match before kickoff requires more than knowing past results: a model must use changing information and make a clear prediction before the answer is available. We present WorldCupArena, a dynamic benchmark for language models and deep-research agents. The 2026 FIFA World Cup is its first evaluation, and the same process can be reused for future leagues and cups. Before each match, a model either receives a common evidence package or searches for information itself. It predicts the result and score, likely players and events, match statistics, and the outcome of the competition. After the match, these predictions are compared with the recorded result. We report result accuracy, exact-score accuracy, and a scoreline score that gives some credit when a predicted score is close but not exact, together with scores for the other prediction tasks. Across 104 matches and 13 systems, models with similar result accuracy differ more clearly on detailed predictions. Compared with betting-market and human-fan baselines, the best system shows only small gains in result and exact-score accuracy, but a clearer gain in Scoreline. New schedules can be added as they begin, allowing the benchmark to evaluate future models without using outcomes that are already known. Code, prompts, predictions, and evaluation scripts are open sourced at https://github.com/wzk1015/WorldCupArena.