Protein language models (PLMs) face a fundamental divide: masked language models (MLMs) excel at fitness prediction while causal models enable generation, forcing practitioners to maintain separate architectures. We introduce \textbf{Proust}, a 309M-parameter causal PLM that bridges this gap through architectural innovations adapted from recent LLM research, including grouped-query attention with shared K/V projections, cross-layer value residuals, and depthwise causal convolutions. Trained on 33B tokens in 40 B200 GPU-hours, Proust achieves Spearman $ρ= 0.390$ on ProteinGym substitutions, competitive with MLMs requiring 50--200$\times$ the compute. On indels, Proust sets a new state-of-the-art, outperforming models up to 20$\times$ larger. On EVEREST viral fitness benchmarks, it approaches structure-aware methods using sequence alone. These powerful representations position Proust in a sweet spot as it also retains native generative capabilities that MLMs lack by design. Interpretability analysis reveals that per-position entropy variance predicts, to an extent, when retrieval augmentation helps and hurts. Such insights can grow in both quantity and quality at scale and inform capabilities such as test-time scaling. Code and weights are available at https://github.com/Furkan9015/proust-inference
Improving Protein Optimization with Smoothed Fitness Landscapes
Massachusetts Institute of Technology（麻省理工学院）；Czech Academy of Sciences（捷克科学院）；Czech Technical University in Prague（捷克布拉格技术大学）；University of Chemistry and Technology, Prague（布拉格化学与技术大学）
Interpreting Protein Language Model Embeddings via Orthogonal Projection for Protein Fitness Prediction
Hasso Plattner Institute, University of Potsdam（波茨坦大学哈索·普拉特纳研究所）；Broad Institute of MIT and Harvard（麻省理工学院及哈佛大学布罗德研究所）；Humboldt University of Berlin（柏林洪堡大学）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）
Exploring zero-shot structure-based protein fitness prediction
Department of Computer Sciences（计算机科学系）；Department of Biostatistics and Medical Informatics（生物统计学与医学信息学系）；Morgridge Institute for Research（Morgridge研究所）
Robust Optimization in Protein Fitness Landscapes Using Reinforcement Learning in Latent Space
School of Computing, KAIST（韩国科学技术院计算机学院）；Center for Mathematical and Computational Sciences, Institute for Basic Science（基础科学研究院数学与计算科学中心）；Center for Biomolecular and Cellular Structure, Institute for Basic Science（基础科学研究院生物分子与细胞结构中心）；Department of Biological Sciences, KAIST（韩国科学技术院生物科学系）
Linear Fitness Subspace in Protein Language Models Enables Sample-Efficient Directed Evolution
Nanyang Technological University（南洋理工大学）；Sun Yat-sen University（中山大学）；Zhejiang University（浙江大学）；Carnegie Mellon University（卡内基梅隆大学）；Shanghai Institute of Optics and Fine Mechanics（上海光学精密机械研究所）；Zhongnan Hospital, Wuhan University（武汉大学中南医院）；University of Southern California（南加州大学）
Protriever: End-to-End Differentiable Protein Homology Search for Fitness Prediction
Department of Systems Biology, Harvard Medical School（系统生物学系，哈佛医学院）；Department of Computer Science, University of Oxford（计算机科学系，牛津大学）；Department of Computer Science, University of Copenhagen（计算机科学系，哥本哈根大学）；Enzyme research, Novonesis（酶研究，Novonesis）
Evolutionary Profiles for Protein Fitness Prediction
Zhejiang University（浙江大学）；Peking University（北京大学）；East China University of Science and Technology（华东理工大学）；Chengdu University of Information Technology（成都信息工程大学）；Massachusetts Institute of Technology（麻省理工学院）；Zhejiang University of Technology（浙江工业大学）
Multi-Scale Representation Learning for Protein Fitness Prediction
Mila - Québec AI Institute（米拉 - 魁北克人工智能研究所）；Université de Montréal（蒙特利尔大学）；Harvard Medical School（哈佛医学院）；IBM Research（IBM研究院）；Broad Institute（布罗德研究所）；HEC Montréal（蒙特利尔高等商学院）；CIFAR（加拿大高级研究所）
Few-shot Protein Fitness Prediction via In-context Learning and Test-time Training
Harvard Medical School（哈佛医学院）；University of Copenhagen（哥本哈根大学）；Novo Nordisk A/S（诺和诺德公司）；Microsoft Research（微软研究院）；Technical University of Denmark（丹麦技术大学）
ProSpero: Active Learning for Robust Protein Design Beyond Wild-Type Neighborhoods
Institute of AI for Health, Helmholtz Munich（亥姆霍兹慕尼黑人工智能健康研究所）；School of Computation, Information and Technology, Technical University of Munich（慕尼黑技术大学计算、信息与技术学院）；Helmholtz AI（亥姆霍兹人工智能研究所）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Faculty of Mathematics, Informatics and Mechanics, University of Warsaw（华沙大学数学、信息学与力学学院）
Fitness aligned structural modeling enables scalable virtual screening with AuroBind
Shanghai Jiao Tong University（上海交通大学）；Lingang Laboratory（临港实验室）；Sun Yat-sen University（中山大学）；Fudan University（复旦大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Chinese Academy of Sciences（中国科学院）；Massachusetts Institute of Technology（麻省理工学院）；Ningxia Medical University（宁夏医科大学）
Department of Statistics and Data Science, Northwestern University（统计与数据科学系，西北大学）；Department of Biomedical Informatics, Harvard University（生物医学信息学系，哈佛大学）；Department of Computer Science, University of Illinois Chicago（计算机科学系，伊利诺伊大学芝加哥分校）
In-Context Learning can distort the relationship between sequence likelihoods and biological fitness
Computational Biology and Bioinformatics Program（计算生物学与生物信息学项目）；Emeritus Department of Ecology and Evolutionary Biology（退休生态学与进化生物学系）；Department of Evolutionary Biology（进化生物学系）；Hagler Institute for Advanced Studies（哈格勒高级研究 institute）；Department of Physics（物理学系）；Quantitative Biology Institute（定量生物学研究所）
Swarms of Large Language Model Agents for Protein Sequence Design with Experimental Validation
Laboratory for Atomistic and Molecular Mechanics (LAMM), Department of Biological Engineering, Massachusetts Institute of Technology（原子分子力学实验室（LAMM），生物工程系，麻省理工学院）；Department of Biomedical Engineering, Tufts University（生物医学工程系，塔夫茨大学）；Laboratory for Atomistic and Molecular Mechanics (LAMM), Department of Civil and Environmental Engineering, Department of Mechanical Engineering, Center for Computational Science and Engineering, Schwarzman College of Computing, Massachusetts Institute of Technology（原子分子力学实验室（LAMM），土木与环境工程系，机械工程系，计算科学与工程中心，计算机科学学院，麻省理工学院）
