That's not what I meant. I'm thinking more of AI systems that combine multiple modules the same way the brain has different regions. LLMs are just part of the bigger picture here. They're good at tackling a certain types of problems, but other approaches are better for different kinds of problems. Having a system that combines a generative model and a classifier for example would make it a lot more efficient and accurate because it has a bigger toolbox instead of using one algorithm for every problem whether it fits or not.

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verdverm 5 hours ago | parent [-]

they write code and use tools, most of that does not need to be part of the "system" that is trained up

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yogthos 4 hours ago | parent [-]

There are plenty of other use cases aside from agentic coding. However, even here a classifier has a lot of obvious benefits. There are plenty of scenarios where a model has to make a choice, and programming/system tool use happen to be deterministic sets of choices that can be evaluated.

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verdverm 4 hours ago | parent [-]

> programming/system tool use happen to be deterministic sets of choices

I'm not following. There are way more tools in the world, and likely available on every machine, than can fit in any model context. One can create a small list, but that will be very org dependent. Programming is very open ended, I struggle to see the 'deterministic set of choices' here.

I'm looking at Jev/Kev like models for some control over tool calling and similar, but I also think that fine-tuning out behaviors (like qwen 3.8 wanting to use rkt all the time) may be better in the long run. Instead of having to check/change every tool call, we can refine the main LLM to have high enough likelihood of using the tools we actually have, that we won't need to check every call.

Jev is definitely useful, but also being held like a hammer in search of a nail right now, approaching OpenClaw like fanfare and hype levels. No one talks much about OpenClaw these days, I would not be surprised if Jev has a similar trajectory.

	▲	yogthos 4 hours ago \| parent [-]
		I'm not talking about checking/changing every tool call, I don't even know what you mean by that to be honest. The question is about selecting the tool to use in a particular scenario. The whole point of Jev/Kev is efficiency. If you can make decisions faster, cheaper, and more reliably that's the benefit. Even within the model architecture itself, you could use a classifier as a router within MoE to pick the expert that's likely to give the best outcome.