Not necessarily. Language models can do stuff—well, figure out or determine stuff—purely through inference. Give them the input on their context window and look at the output, no need to generate or run any programs.

You can take it further with a reasoning model and let it generate + consume a bunch of additional tokens before getting your output. Still not writing or running any programs.

To my understanding, that's how the examples of determining which game a small neutral net was trained to play. The weights of the neural net for into the context window, and then Astra was able to analyze them purely by reasoning rather than running external tools.

No it's a huge difference. Someone who can quickly compute 123^456 in his head has a very different skillset to someone who uses a calculator.

The distinction here is the equivalent of Astra passing the pelican test by prompting an image generation model versus emitting the SVG.

The former will likely produce a much higher quality image, but claiming it as a demonstration of Astra's capabilities would be a lie.

It obviously is an important distinction to the paper's authors, which is why they used the modifier "native" to mark it as a distinct form of communication than what's already been on the market for years (coding agents). But it's not native, or rather some of the results were native but they mixed toy-scale native results with non-toy-scale non-native results for marketing purposes.

This is like someone telling you they made a "native" brain-to-brain interface but it's just a tin-can phone.