别猜了，Dylan用48个提示跑了7个模型，结论是AI实验室没作弊，鹈鹕骑自行车并不比其他组合强。
Dylan Castillo针对AI实验室是否专门训练模型生成“骑自行车的鹈鹕”进行了系统测试。他设计了8种动物×6种车辆共48个提示，每个提示在7个模型（GPT-5.6 Terra、Claude Sonnet 5、Gemini 3.5 Flash、Grok 4.5、Qwen3.7-Max、GLM-5.2、DeepSeek V4 Pro）上运行3次，并用GPT-5.6 Luna和Gemini 3.1 Flash-Lite辅助评估。结果显示，没有任何模型在鹈鹕骑自行车场景上表现更好，GLM-5.2虽有微小正向偏差但统计不显著。结论是AI实验室并未系统性地优化这一特定组合。
Are AI labs pelicanmaxxing?
Are AI labs pelicanmaxxing? Excellent piece of work by Dylan Castillo, who took a deep-dive into the frequently pondered question of whether the AI labs have been deliberately training models to draw pelicans riding bicycles in response to my deeply unscientific benchmark . I've been randomly spot-checking this in the past by testing models against other animals riding other types of vehicle, but never with anything close to the diligence of Dylan's methodology here. Dylan took 8 animals × 6 vehicles = 48 prompts and ran them three times each through 7 different models ( GPT-5.6 Terra, Claude Sonnet 5, Gemini 3.5 Flash, Grok 4.5, Qwen3.7-Max, GLM-5.2, and DeepSeek V4 Pro). He then used GPT-5.6 Luna and Gemini 3.1 Flash-Lite to help evaluate the results. There's a neat filter view for exploring the results: For the models he tested he could find no evidence of pelimaxxing: The pelicans on bicycles don’t look any better Labs are not better at drawing pelicans Labs are not better at drawing bicycles Labs are not better at drawing pelicans on bicycles, even adjusting for difficulty The pelican-bicycle scenes don’t look memorized [...] Pelicans aren’t drawn any better than other animals. Bicycles aren’t drawn any better than other vehicles. And no lab draws the combination better than its pelicans and bicycles already predict. GLM-5.2 comes closest: it has the largest boost on the exact pelican-bicycle cell, and and its first pelican-on-bicycle sample caught my eye. But the effect is small and not significant, so I wouldn’t put too much weight on it. Via Hacker News Tags: ai , generative-ai , llms , evals , pelican-riding-a-bicycle