Yeah. Clearly the target for these models is on-device assistant/analyst tasks where really everything is agentic.

Thinking out loud: any text-based generative AI application is on a continuum between:

- document-to-document: solve this language problem using language skills by outputting only new symbols, and

- document-to-tasks: solve this language problem by only operating these tools.

In a sense, given access to vast compute to train a very large model, agentic coding is one of the easier applications that is somewhere between the two. It is manipulating symbols in a mixture of languages that are biased towards context-free (code), it exploits the embedded knowledge in a vast number of weights, and it calls fairly simple tools. The user-focussed solution is happening inside the LLM.

A small model can't do this job well, even if it has a good understanding of programming languages, because it lacks the world knowledge to understand the problem.

At the other end of the continuum is: these words mean do this thing. That Cactus Needle 2 model mentioned earlier is here. It doesn't even produce a language description of what it did, because it one-shots tasks.

In the middle but near this end is the fantastically hard job: train a model that understands language and reasons well enough to respond to queries about the tools it has access to, operate them and reply in natural language, without being large or slow. That is, understand language without being overburdened by details of scenarios that caused the need for the words in the first place.

This is maybe the great grand challenge of LLMs: make it know how to speak and reason and think and work for us while discarding everything that is just knowledge of unnecessary facts.

Small reasoning models are where most of the really big challenges are.