papers/kai/sections/01-introduction.tex

\section{Introduction}
\label{sec:intro}

Most decisions a system takes are typed. A ticket goes to one of ten queues, a
message is or is not phishing, a request is urgent on a scale of three, an
action is allowed, asked about or denied. A generative model can answer each of
these by writing text, but the text must then be parsed, its probability is not
the probability of the decision, and the same question asked twice can come
back in two forms. Kai answers the decision itself. Given a state and a set of
typed questions, it returns for each question a probability over exactly the
answers the caller declared.

A model's answer is not an authorization. Kai's decisions that lead to action
pass through Nio, a deterministic policy that holds authority over what is
done: the model recommends, the policy authorizes, and a model verdict joined
with a policy verdict on \emph{allow} $<$ \emph{ask} $<$ \emph{deny} can only
make it stricter. Where a decision turns on consequences, Kai reads the
consequences into typed state and Nio decides from that state alone; where the
evidence does not establish them, the decision goes to a person.

This paper reports what Kai achieves against public benchmarks and the models
they are usually read with. Section~\ref{sec:interface} states the interface.
Section~\ref{sec:evaluation} gives the evaluation protocol and the baselines.
Section~\ref{sec:results} gives the results, where Kai leads and where it
trails, and Section~\ref{sec:limits} what they do not show.