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The resulting recipe"penalize the path, reward the outcome" achieves high task success with near-zero violations, where outcome-only training violates constraints on nearly every episode.
This work ground perceptual memory in the model, decomposing recall into two subproblems: a vision-language model grounds the referent in context (what and where), and a dedicated encoder extracts an identity key (who), stored as one inline token read by attention at generation with no external round-trip.
NameRank, a [0,1] recognition score, is built: recognition is paid to named, indexable artifacts, not to credentials or titles, because recognition attaches to the artifact's own distinctive name, not to the roster behind it.
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