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Kai: typed decisions under deterministic policy, and its results against public benchmarks
2026-10-22 10:20:06 -07:00

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\section{Limits}
\label{sec:limits}
\paragraph{Advice, not actuation.} A policy verdict is advice to a person or to
a system that checks it again. Kai's grounding was qualified on generated
scenes in settings it had not seen, not on incidents in the field, and its own
yes/no answer does not weigh stakes; Kai is not used to actuate physical
systems, and a consequential action stays with a person when the evidence does
not establish its counts.
\paragraph{Counts.} The production policy reads counts from 0 to 100 and
escalates anything past them or stated inexactly. Reading an exact count of
any size, bound to the right course, from the model alone is the next release's
work; no suite reported here measures it.
\paragraph{Breadth.} Kai decides; it does not reason through knowledge-heavy or
multi-hop tasks, where it trails generative models (Section~\ref{sec:trails}).
Its transfer to decision tasks unlike its training is below the best open
model's, and its jailbreak recall is below Jev's.
\paragraph{Samples.} Some reads are small: 100 items a dataset in the zero-shot
pilot, 300 steps a split for the hosted comparison on Mind2Web, 254 cases where
no tool applies. Their intervals, not their points, are the claim.