寻找开放性的要素：用大型视觉语言模型复现 Picbreeder
In Search of the Ingredients of Open-Endedness: Replicating Picbreeder with Large Vision-Language Models
AI总结：
本研究通过用前沿视觉语言模型替代人类用户复现 Picbreeder，探索人工智能在无引导发现中的开放性能力，并分析系统输出与人类基线在系统发育复杂性、视觉和语义显著性及新颖性上的差异，同时研究探索性噪声、行为多样性和叙事动量等因素的影响。
摘要：
我们正处于大规模工业和学术努力之中，旨在通过AI驱动的助手自动化科学、技术和创造性生产的过程。历史上，这些过程在人类形式中的一个基本属性是它们的开放性：即生成看似无穷无尽的新颖且有意义的新形式的能力。人工代理是否有能力进行这种富有成果的无引导发现？为了回答这个问题，我们转向Picbreeder，这是人类驱动的开放性搜索的典型范例，用户通过小型神经网络的交互式进化协作生成多样化的图像库。我们复现了Picbreeder，用前沿视觉语言模型（VLM）替代人类用户。我们观察到系统输出与历史人类基线之间存在明显的定性差异，并尝试使用系统发育复杂性、视觉和语义显著性及新颖性的指标来表征这些差异。为了识别导致这些差异的一些因果因素，我们研究了在代理的选择过程中添加探索性噪声、代理之间的行为多样性以及以过去行动记忆形式的叙事动量。我们的代码可在 https://github.com/smearle/picbreeder-vlm 获取。
英文摘要：
We are in the midst of large-scale industrial and academic efforts to automate the processes of scientific, technological and creative production through AI-driven assistants. Historically, a fundamental property of these processes in their human form has been their open-endedness: their capacity for generating a seemingly endless supply of novel and meaningful new forms. Do artificial agents have any capacity for such fruitful unguided discovery? To answer this question, we turn to Picbreeder, the canonical exemplar of human-driven open-ended search, in which users collaboratively generated a diverse library of images through interactive evolution of small neural networks. We replicate Picbreeder, replacing human users with frontier Vision Language Models (VLMs). We observe clear qualitative differences between the output of our system and the historical human baseline, and attempt to characterize them using metrics of phylogenetic complexity and visual and semantic salience and novelty. In an effort to identify some of the causal factors contributing these differences, we study the addition of exploratory noise to the agents' selection process, of behavioral diversity between agents, and of narrative momentum in the form of memory of past actions. We make our code available at https://github.com/smearle/picbreeder-vlm.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
