The standard autoresearch loop (brainstorm from code, run experiments, check metrics) works when the optimization surface is visible in the source. The Liquid results prove that. But for problems where the codebase doesn't contain enough information to generate good hypotheses, giving the agent access to papers and competing implementations changes what it tries.
这一声明清晰地区分了两种优化场景：代码可见的优化和需要外部知识的优化。它揭示了AI代理开发中的一个关键洞察：优化方法必须根据问题性质进行调整。对于某些问题，简单的代码分析就足够了；但对于更复杂的问题，需要引入外部知识和研究。这一发现对AI辅助编程系统的设计具有重要指导意义。