Sparse autoencoders applied to Neural Quantum States extract unsupervised features correlating with and causally steering physical observables such as order parameters while preserving variational energy.
Nithin Parsan, David J
5 Pith papers cite this work. Polarity classification is still indexing.
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Introduces PairSAE, a sparse autoencoder for pair representations in structural biology foundation models that produces features aligned with UniProt annotations and affinity predictions.
ESM2 predicts N-terminal methionine via retrieval of a positional prior from the BOS token through distributed attention circuits rather than direct recognition, revealed by a norm-direction decomposition of rotary attention scores.
GeoSAE extracts a compact, interpretable feature set from frozen brain MRI foundation models that predicts MCI-to-AD conversion (AUC 0.746) with age-deconfounded annotations and replicates across cohorts.
ESMC-SAE features enable 78.9% top-1 EC number prediction on 4,868 microbial enzymes, outperforming 3-mer baselines by 37.6% and recovering novel EC1 classes at 47.7% in leave-one-out tests.
citing papers explorer
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Mechanistic Interpretability and Causal Feature Steering of Neural Quantum States via Sparse Autoencoders
Sparse autoencoders applied to Neural Quantum States extract unsupervised features correlating with and causally steering physical observables such as order parameters while preserving variational energy.
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PairSAE: Mechanistic Interpretability from Pair Representations in Protein Co-Folding
Introduces PairSAE, a sparse autoencoder for pair representations in structural biology foundation models that produces features aligned with UniProt annotations and affinity predictions.
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Retrieval and competition: how a protein foundation model starts a protein
ESM2 predicts N-terminal methionine via retrieval of a positional prior from the BOS token through distributed attention circuits rather than direct recognition, revealed by a norm-direction decomposition of rotary attention scores.
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GeoSAE: Geometric Prior-Guided Layer-Wise Sparse Autoencoder Annotation of Brain MRI Foundation Models
GeoSAE extracts a compact, interpretable feature set from frozen brain MRI foundation models that predicts MCI-to-AD conversion (AUC 0.746) with age-deconfounded annotations and replicates across cohorts.
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Interpretable enzyme function prediction via sparse autoencoder features of ESMC across the microbial protein universe
ESMC-SAE features enable 78.9% top-1 EC number prediction on 4,868 microbial enzymes, outperforming 3-mer baselines by 37.6% and recovering novel EC1 classes at 47.7% in leave-one-out tests.