StabilityArc is a method that decodes protein sequence embeddings into generalizable stability landscapes. It uses a shared RoPE transformer to map frozen ESMC-600M residue representations into an Lx20 matrix of substitution effects, with a symmetric, contact-aware residual to predict epistasis. In extensive leave-one-protein-out tests on 134,794 ProteinGym variants, StabilityArc achieves a Spearman correlation of 0.7134, surpassing the best zero‑shot baseline, and further improves Kermut’s performance when used as a prior.
By Aaron L. Feller, Andrew D. Ellington, Claus O. Wilke
The paper introduces a scalable method to interpret sparse autoencoder (SAE) features in the ESM-2 protein language model by leveraging geometrically inspired features of the protein α‑carbon backbone. Across 8M layers of ESM-2, a false discovery rate–controlled analysis shows that local geometry is significantly associated with many SAE features, revealing substructure within known biological labels and enabling annotation of unannotated metagenomic proteins. Ablation experiments demonstrate that removing these geometric features shifts ESM-2’s predicted contact maps toward the descriptor, linking mechanistic interpretability with structural biology.
By Siddharth Setlur, Djordje Mihajlovic, Darrick Lee
ProtModernBERT is a modernized BERT-style protein language model that reduces training cost by incorporating architectural refinements, achieving performance comparable to larger models while cutting pre‑training FLOPs by 80‑90%. The authors benchmark ProtModernBERT against ProtBert, Amplify, and ESM2 on supervised structural tasks and a zero‑shot functional benchmark, showing it matches or closely tracks Amplify‑120M and ProtBert. Their cluster‑based validation and random‑init experiments reveal that modern architectural components provide stronger inductive biases than parameter count in low‑compute regimes.
By Alberto Mart\'inez, Natalia Flechas Manrique, Lloren\c{c} Espinosa-Portal\'es, Luc Andrea
arXiv:2607.01630v2 Announce Type: replace
Abstract: Dynamic expansion methods for class-incremental learning (CIL) protect task-specific knowledge by growing dedicated tokens or subnetworks, yet our...
By Bingchen Huang, Yifu Chen, Zhiling Wang, Yuanchao Du
The paper introduces a method that uses orthogonal projection to remove the influence of known biochemical features from protein language model (PLM) embeddings, allowing the authors to assess how much these features contribute to protein fitness predictions. By applying this technique to high‑order and interaction effects, they demonstrate that eliminating these interpretable features reduces downstream classifier performance, indicating that PLM embeddings encode patterns correlated with biochemical properties. The authors also show that these biochemical features explain a substantial portion of the variance in the classifier’s predictions, suggesting that PLM embeddings capture biologically relevant information.
By Paulo Yanez Sarmiento, Pia Francesca Rissom, Manuel Pfeuffer, Marco Simnacher, Jordan F. Safer, Sumaiya Iqbal, Henrike O. Heyne, Nadja Klein, Bernhard Y. Renard
arXiv:2608. 12090v1 Announce Type: new Abstract: Protein language models (PLMs) have transferred the latest advances from natural language processing to computational biology.
By Roman Joeres, Ilya Senatorov, Olga V. Kalinina
Large protein language models provide a powerful framework for learning sequence-function relationships and guiding experimental design. However, architectural improvements are underexplored, especial...
Dynamic expansion methods for class-incremental learning (CIL) protect task-specific knowledge by growing dedicated tokens or subnetworks, yet our analyses suggest that classification supervision alone does not sufficiently preserve task-agnostic shared backbone representations over long incremental sequences. We identify two intertwined challenges: cross-task confusion from sequential training on predominantly current-task data, which biases decision boundaries toward recent tasks; and under-optimized shared representations in the backbone that cap long-term discriminability as tasks accumulate.
arXiv:2606. 27095v1 Announce Type: cross Abstract: Cold-start exemplar-free class-incremental learning requires learning a growing set of classes without replay, external pretraining, or a large initial task.
By Augustinas Ju\v{c}as, Yangchen Pan
Protein language models (PLMs) have transferred the latest advances from natural language processing to computational biology. These models, trained on large corpora of protein sequence data, are widely used to translate amino acid sequences into latent-space embeddings, ready for use in diverse downstream tasks (DTs).
arXiv:2607. 20993v1 Announce Type: cross Abstract: Large vision-language models are becoming increasingly dominant in 3D medical image interpretation, but we rarely know which internal units encode clinical findings or where that information lives in the representation.
By Farhad Nooralahzadeh, Lea Bogensperger, Christian Bluethgen, Michael Krauthammer
arXiv:2608.07632v2 Announce Type: replace-cross
Abstract: Image-based profiling captures rich phenotypic signatures for drug discovery and functional genomics. Large public datasets like JUMP Cell Pa...
By Al\'an F. Mu\~noz, Johan Fredin Haslum, Runxi Shen, Anne E. Carpenter, Shantanu Singh