They are causal autoregressive models, the output is sensitive even to the implementation nuances in inference. Even 1 token that's badly selected could throw off the entire answer.

▲

segmondy 3 hours ago | parent [-]

you're thinking of one shot. if they are running an agentic loop then they don't need multiple passes. an agentic loop is multiple passes with tool calls and tools could fail and agent would correct from seeing the failure. a bad model will compound on error and fail, a good model will correct. 1 test is fine to gauge the quality of the model.

▲

computerex 2 hours ago | parent [-]

An agent doing a task even with multiple back to back calls like normal without an example is zero shot. An agent doing a task with 1 example is one shot. An agent doing a task with a few examples is few shot. I don't think you are correctly using these terms.

The multiple back to back LLM calls are done on accumulating context, so if there is a sampling error it could throw the entire session out of whack, because LLM's build on the previous context.

It's actually meaningless to argue, one could simply sample more than 1 times and let the numbers speak for themselves.

	▲	gpt5 an hour ago \| parent [-]
		That's not true. An agent in a loop can test itself, review, verify and iterate as much as needed. That's one of the primary reasons more capable models tend to have a higher success rate. I don't disagree that multiple tests increase confidence, but it's not correct to argue that an agent in a loop harness is equivalent to oneshotting