📄 Model Card for Boltz-1 SAEs: Pairformer Trunk (Recycle 0)
This repository contains the trained TopK Sparse Autoencoders (SAEs) for the Pairformer Trunk evaluated at Recycle Iteration 0 as featured in the preprint “Where a folding model keeps biology: probing and sparse-autoencoder analysis of the Boltz-1 trunk and diffusion module”.
These dictionaries map the dense early-stage activation spaces of the representation trunk into an interpretable, sparse latent basis.
Model Details
Base Architecture: Boltz-1 (Open-source AlphaFold3-class structure predictor)
SAE Type: TopK Sparse Autoencoder ($k=256$, total latents $N=2048$)
This repository hosts dictionaries trained explicitly within the Pairformer Trunk during its initial execution step:
Layers Available: Layers 0 to 47
Recycling Configuration:Recycle 0. These weights allow for comparative analysis of how representations form or update before the model utilizes recycled structural states.
Training Dataset & Preprocessing
The SAEs were trained via unsupervised dictionary learning using a structural biology activation dataset of 84,074 unlabelled proteins (~21.96M residues total).
Demeaning Step: Prior to encoding, the training-set mean activation was subtracted. This counteracts Boltz-1's extreme activation-energy concentration (where ~98% of raw squared magnitude sits in the top 10 dimensions), reducing the top-10 energy share to ~29% and enabling the dictionary to learn fine, distributed biology.
Key Scientific Insights (Trunk Stack)
If you use these models, please contextualize them with the architectural insights established in our paper:
Semantic Compiler: The Pairformer trunk linearly encodes both macro-geometry (DSSP secondary structure F1 0.79–0.90) and highly localized sequence chemistry (signal peptides, disordered regions, and disulfide bonds with a targeted mid-trunk hotspot at fractional depth 0.2–0.35).
SAE Feature Concentration: While supervised linear probes dominate overall decodability, these SAEs offer clean, monosemantic single-feature alignment for amino-acid identities (e.g., Cysteine latent F1 $\approx 0.99$) where raw individual neurons fail.
