The Diversity Mirage: Quality-Gated Evaluation for Biological Sequence-Design Active Learning

Active learning for sequence design aims to find batches of strong, diverse candidates with a limited measurement budget. Common evaluations assess overall quality but measure diversity among each method's own highest-scoring candidates. These candidate sets can differ in quality. A method can therefore look diverse even when its candidates score lower than those found by other methods. We call this the diversity mirage. We introduce quality-gated evaluation to compare methods under the same measurement budget. It uses a shared quality standard to assess both the number and diversity of high-quality candidates. We run controlled experiments on RNA design tasks and experimentally measured protein fitness data from ProteinGym. On RNA tasks, the shared standard reveals a reversal in the diversity ranking. A simple local search finds more distinct groups of high-quality sequences than a generative method designed for diversity. On ProteinGym, we use ESM features from mutated positions to select candidates. We find 42.9% more high-quality candidates on average than proxy-greedy selection, while achieving similar coverage of sequence space. With a shared quality standard, we can directly compare the diversity of high-quality candidates across methods. What matters is how many strong, distinct candidates the same budget delivers.