ARC-AGI-3 顶尖方案均采用 LLM 引导的符号世界模型合成
Jeremy's excellent work here is a great illustration of a very powerful type of…
Jeremy's excellent work here is a great illustration of a very powerful type of approach: LLM-guided on-the-fly synthesis of a symbolic world model, i.e. making sense of the world by writing executable code that encodes your understanding of the causal mechanics of the world.
杰里米在这里的出色工作很好地展示了一种非常强大的方法：由LLM引导的即时符号世界模型合成，即通过编写可执行代码来编码你对世界因果机制的理解，从而理解世界。
So far, all of the top-performing harnesses on ARC-AGI-3 use this style of approach. Which is also the approach we recommended when we initially released the benchmark (Jeremy would know this better than most, as a former Ndea member of technical staff).
到目前为止，ARC-AGI-3上所有表现最好的工具都采用了这种方法。这也是我们最初发布基准测试时推荐的方法（杰里米作为前Ndea技术团队成员，对此应该比大多数人更了解）。
I'm happy that ARC 3 has incentivized more research and more progress in this area.
我很高兴ARC 3激励了这一领域更多的研究和进步。
更进一步：量化金融体系
看懂新闻只是起点——沿量化金融路径，把它变成能交付的工程能力