> We find that frontier LLMs are surprisingly good at natively reading and writing neural network weights

This claim is backed up by the ability of Astra, given a text representation of the weights of tiny (20k) special-purpose models, to figure out what those models do and in some cases improve them beyond what RL could do just by reading the weights with no tool calls. They call this "weight-space programming".

> Likewise, asked to make Qwen3.5-0.8B better at math without gradients, Astra identifies an issue in its weights causing reasoning loops that Qwen’s own model card warns about, and fixes this by natively editing 0.016% of the weights

Here it did use tool calls, and seemingly used standard steering vector techniques to identify and fix reasoning loops in this small Qwen model. This doesn't seem that related to the previous results.