Recognition is not taste: interpretive space as the missing object in AI for science

Artificial intelligence (AI) for Science is shifting from systems that execute scientific tasks to systems that judge and generate research questions. Recognition-trained systems, including Reinforcement Learning from Community Feedback (RLCF), convert citations, review scores, or preference signals into supervisory signals for future research. This Perspective argues that such systems risk optimizing the wrong evaluative object: recognition is an institutional compression of scientific value, recording what a community has already rewarded, whereas scientific judgment must also preserve interpretive space, the socially available future of a claim before institutional recognition stabilizes a dominant appraisal of its value. Taste is therefore recast as one mode of appraising interpretive space, and answerability as the governance principle that keeps it open. The article proposes an interpretive layer above reward models as a research agenda for preserving interpretive space rather than merely predicting recognized success.