Yeti is a compact tokenizer for protein structures that delivers strong codebook use, token diversity, and reconstruction while enabling from-scratch multimodal generation of plausible sequences and structures with 10x fewer parameters than ESM3.
Proteina: Scaling flow-based protein structure generative models
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
SymDrift makes drifting models produce symmetry-invariant samples in one step via symmetrized coordinate drifts or G-invariant embeddings, outperforming prior one-shot baselines on molecular benchmarks and cutting compute by up to 40x.
Combining diffusion priors as a product-of-experts and optimizing exponents via Bayesian evidence maximization enables prior tuning from one observation in inverse imaging problems.
citing papers explorer
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Yeti: A compact protein structure tokenizer for reconstruction and multi-modal generation
Yeti is a compact tokenizer for protein structures that delivers strong codebook use, token diversity, and reconstruction while enabling from-scratch multimodal generation of plausible sequences and structures with 10x fewer parameters than ESM3.
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SymDrift: One-Shot Generative Modeling under Symmetries
SymDrift makes drifting models produce symmetry-invariant samples in one step via symmetrized coordinate drifts or G-invariant embeddings, outperforming prior one-shot baselines on molecular benchmarks and cutting compute by up to 40x.
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Optimizing Diffusion Priors in Image Reconstruction from a Single Observation
Combining diffusion priors as a product-of-experts and optimizing exponents via Bayesian evidence maximization enables prior tuning from one observation in inverse imaging problems.