# Gap Detection

Language models often return semantically similar answers to open-ended questions even when many plausible alternatives exist. Gap Detection searches a shared text embedding space for relevant but unoccupied directions, scoring each by the product of query relevance (Demand) and dissimilarity to the nearest existing item (Defensibility), then mapping directions to concept words and text. On five curated topics and 90 INFINITY-CHAT queries, reported mean relative reductions in output Collision are 12.8% and 10.8%; in iterative synthetic-data augmentation with 100 training examples per method, Collision falls from 0.324 to 0.298 and the Vendi Score rises from 27.16 to 29.20, while accuracy differs by one example on a 40-example test set.

Language models often return semantically similar answers to open-ended questions even when many plausible alternatives exist. Gap Detection searches a shared text embedding space for relevant but unoccupied directions, scoring each by the product of query relevance (Demand) and dissimilarity to the nearest existing item (Defensibility), then mapping directions to concept words and text. On five curated topics and 90 INFINITY-CHAT queries, reported mean relative reductions in output Collision are 12.8% and 10.8%; in iterative synthetic-data augmentation with 100 training examples per method, Collision falls from 0.324 to 0.298 and the Vendi Score rises from 27.16 to 29.20, while accuracy differs by one example on a 40-example test set.