> what are the use cases for this kind of model? Could it be used in the context of coding agents

Yeah, it could. The most obvious usage would be to have local fast cheap "feedback" / "control" over a slower more expensive agent (i.e. cc / codex / opencode). Things like "goals" could now be split from a long prompt into "actions" and "verifiers". Where for each action you also produce a verifier. Then after each action you run the verifier w/ this kind of "universal classifier" and decide if the step was done correctly, if it needs follow-up and so on.

Example: implement auth in this repo -> llm_plan() -> for item in plan generate_verifier() -> for item in plan implement() ; verify() ; accept() / followup().

Verifiers could be something like this. take a plan item as input, generate classification questions that might verify the task "is this following project conventions?" | "is this touching files from other tasks?", etc.

You can do that with LLMs, but some things might become cheaper / faster. And you can pretty much use it to check against an ever growing list of conventions. Yours or project specific.

I don’t think this is a very good use case. You could do it better with a strong LLM and structured outputs.

The problem is that you want the model to carefully reason about the goal and code.

Zero shot classification with an approach like this isn’t going to do that. It’ll answer on first pass vibes.

Why not just let the LLM write a test instead of a "verifier"?