Saurav Jha, Qianli Liao, Robert Vernon, Sarath Chandar, Quentin Fournier · 2026 · arXiv
DOI: 10.48550/arXiv.2610.26747 · open-access PDF
Protein language models (pLMs) are powerful tools for protein annotation and mutation-effect prediction, but progress has largely depended on scaling model size and data, making them expensive and prone to memorization. Test-time scaling offers a complementary path, but methods developed for autoregressive LLMs do not transfer directly: masked encoders lack a generation loop, and amino acids do not verbalize in a reasoning language. Instead, we adapt recursive pretraining to masked pLMs, where a weight-tied Transformer core refines residue-aligned hidden states over an adjustable number of cyc
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