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Static protein surrogates fail as experimental evidence shifts, but dynamically arbitrating between zero-shot priors and task models exclusively at the high-fitness tail reliably surfaces top variants in under 500 wet-lab measurements.
Natural RNA evolution's sequence variation and selection are finally modeled for RNA design, yielding state-of-the-art 85.19% Pass@1 on Rfam-27.
Quantizing equivariant GNNs no longer has to break symmetry: GAQ achieves FP32 accuracy with W4A8 models, 2.39x speedup, and 4x memory reduction, all while slashing equivariance errors by 30x.