What "performance plateau"? The "plateau" disappears the moment you get harder unsaturated benchmarks.
It's getting more and more challenging to do that - just not because the models don't improve. Quite the opposite.
Framing "improve general accuracy" as "something no one is doing" is really weird too.
You need "general accuracy" for agentic behavior to work at all. If you have a simple ten step plan, and each step has a 50% chance of an unrecoverable failure, then your plan is fucked, full stop. To advance on those benchmarks, the LLM has to fail less and recover better.
Hallucinations is a "solvable but very hard to solve" problem. Considerable progress is being made on it, but if there's "this one weird trick" that deletes hallucinations, then we sure didn't find it yet. Humans get a body of meta-knowledge for free, which lets them dodge hallucinations decently well (not perfectly) if they want to. LLMs get pathetic crumbs of meta-knowledge and little skill in using it. Room for improvement, but, not trivial to improve.
It's getting more and more challenging to do that - just not because the models don't improve. Quite the opposite.
Framing "improve general accuracy" as "something no one is doing" is really weird too.
You need "general accuracy" for agentic behavior to work at all. If you have a simple ten step plan, and each step has a 50% chance of an unrecoverable failure, then your plan is fucked, full stop. To advance on those benchmarks, the LLM has to fail less and recover better.
Hallucinations is a "solvable but very hard to solve" problem. Considerable progress is being made on it, but if there's "this one weird trick" that deletes hallucinations, then we sure didn't find it yet. Humans get a body of meta-knowledge for free, which lets them dodge hallucinations decently well (not perfectly) if they want to. LLMs get pathetic crumbs of meta-knowledge and little skill in using it. Room for improvement, but, not trivial to improve.