alt Hacker NewsIf you don't like these experiments, then I can only assume you would complain about CRISPR as well, considering it's a general framework with many disparate parts that work together synergistically, similar to the work Takehi et al. describe. CRISPR was also famously rejected multiple times by reviewers.
Note that being able to both inspect raw weights and run Python code to perform additional analysis means the model has multiple possible techniques at its disposal (generally considered a positive, rather than a "confusing mix").
I don't understand CRISPR well enough to comment on the analogy.
The blog post claims to show that frontier LLMs can understand and craft neural network weights natively. The experiments where the model completes coding tasks are not relevant to this conclusion. Those experiments require the model to demonstrate an analytic understanding of how a network might behave under various conditions, along with reasoning skills, coding skills etc. As a coding task this is a significant challenge. But it's entirely different from "natively reading and writing neural network weights".
Would you also consider it a benefit if there were experiments showing that a frontier model can download and run PyTorch, or write its own network and optimiser? That's another technique it has at its disposal to improve an ML model. It's just not one that's relevant to the paper's core claims.