Earlier quoted context omitted.
LLMs are probabilistic by nature so even if you're using without temperature it doesn't remove completely this fact, it would just narrow the output. However here we're aiming for an already defined set of rules on purpose, with no LLM including in the decision workflow on purpose. You can't safely rely on LLM for security, it's contradictory because of the current nature of LLMs, which is one of the issues that we h…
My understanding is that yes they are probabilistic by nature in that they give you a probability distribution over all tokens in the vocabulary for the next token, but if you just take the policy of using max prob for each token their output becomes deterministic right? Same output always results for a given input.
Re: Show HN: Deterministic security solution for AI agents – OpenClaw and 2 more
#11Hmmm yes and no, here the output "tends towards" deterministic (≠ deterministic), because the internal process contains noise (noise can't be nullified, otherwise LLMs wouldn't make mistakes and wouldn't produce unexpected outcomes).
And the thing is that even if the output tends towards deterministic they will always be ways to do prompt injections. So maybe I should clarify by mentioning that the solution I shared/proposed is external to the AI's internal process, it's a complete separate thing from the LLMs