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我遇到了一篇关于HRM（层次推理模型）的解释文章，其中提到了一些惊人的数据。文章声称在推理任务上，比如数独、30X30迷宫和ARC-AGI，HRM能够超越像3.7 sonnet和o3-mini这样的LLM（大型语言模型）。这是解释文章的链接：https://towardsdatascience.com/your-next-large-language-model-might-not-be-large-afterall-2 我目前在一家基于产品的初创公司工作，正在致力于自动化PCB设计。这项工作也需要一些深入的推理能力，比如知道USB连接器应该放在电路板的边缘，感应负载和电容负载应该分开，同时还要优化布线长度。我想问一下，这种方法是否适合解决我的使用案例？你认为这样做可行吗？因为我确实发现HRM在解决某些问题上比LLM更有效，而我的使用案例与这些问题有一些相似之处。

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I came across this HRM (Hierarchical Reasoning Models) explainer, and it claimed some wild numbers. It claims to beat LLMs like 3.7 sonnet and o3-mini on reasoning tasks like Sudoku, 30X30 mazes, and ARC-AGI. Here is the explainer: https://towardsdatascience.com/your-next-large-language-model-might-not-be-large-afterall-2/<p>I am currently working in a product-based startup and am working on automating PCB design. It also requires some hardcore reasoning, as in knowing that USB connectors should be at the edge of the board, inductive and capacitive loads should be apart, all while optimizing the routing length.<p>I wanted to ask if this is a viable approach to solving my use case? Think this would work out? Because I did see some similarities with the problems HRM solves better than LLMs, and my use case.

一个拥有2700万个参数的模型在推理任务上超越了大型语言模型（LLMs）。