AI 中文总结
本研究评估六种语言模型配置生成六选49彩票号码的表现，发现数字频率集中性显著，有效多样性远低于独立均匀采样阈值，提示模型输出存在偏差。
AI 中文摘要
我们评估了六种语言模型配置在请求从1到49中随机选取六个不同整数时的表现。在使用四种英文提示变体进行的1,200次尝试调用中，1,184次响应生成了有效彩票。数字频率的有效多样性范围为9.9至18.0，而对应样本量下独立均匀六选49采样的模拟第五百分位阈值为46.6至46.7。系统产生了8至93个不同的无序彩票，其众数彩票占有效响应的22.5%至68.0%。两个存档的波兰乐透样本提供了实体彩票对比，在较小样本量下有效多样性分别为41.1和41.4。这些结果表明，在所测试的部署设置下存在显著的集中性。它们并未确定其机制，也未确立在其他提示、温度或工具配置下的表现。
英文摘要
We evaluate six language-model configurations on requests for six distinct random integers from 1-49. Across 1,200 attempted calls using four English prompt variants, 1,184 responses yielded valid tickets. Effective diversity of number frequencies ranged from 9.9 to 18.0, compared with simulated fifth-percentile thresholds of 46.6-46.7 under independent uniform six-of-49 sampling at the corresponding sample sizes. Systems produced 8-93 distinct unordered tickets, and their modal tickets accounted for 22.5-68.0% of valid responses. Two archived Polish Lotto samples provided a physical-lottery comparison, with effective diversities of 41.1 and 41.4 at smaller sample sizes. These results demonstrate substantial concentration under the tested deployment settings. They do not identify its mechanism or establish performance under other prompts, temperatures, or tool configurations.
Comments6 pages, 1 figure. Data, code, and collector at github.com/Rankfor/rankfor-open (research/lotto-models)
