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ProteinJEPA enhances language models with latent prediction

By PulseAugur Editorial · Summary by gemini-2.5-flash-lite from 2 sources

Researchers have developed ProteinJEPA, a novel approach that enhances protein language models by incorporating latent-space prediction alongside masked language modeling (MLM). This method, termed masked-position MLM+JEPA, involves predicting latent targets specifically at masked positions, showing competitive or superior performance across a suite of 16 downstream tasks compared to MLM-only training. The gains are observed in areas such as protein stability, fitness, and fold retrieval, demonstrating the effectiveness of combining JEPA with MLM for pretraining and continued training. AI

Summary written by gemini-2.5-flash-lite from 2 sources. How we write summaries →

IMPACT Introduces a new training methodology that improves performance on various protein-related tasks, potentially advancing biological AI applications.

RANK_REASON The cluster describes a new research paper detailing a novel method for training protein language models.

Read on arXiv cs.LG →

COVERAGE [2]

  1. arXiv cs.LG TIER_1 Français(FR) · Michal Linial ·

    ProteinJEPA: Latent prediction complements protein language models

    Protein language models are trained primarily with masked language modeling (MLM), which predicts amino-acid identities at masked positions. We ask whether latent-space prediction can complement these token-level objectives under matched wall-clock budget. Across pretrained and r…

  2. arXiv stat.ML TIER_1 Français(FR) · Dan Ofer, Dafna Shahaf, Michal Linial ·

    ProteinJEPA: Latent prediction complements protein language models

    arXiv:2605.07554v1 Announce Type: cross Abstract: Protein language models are trained primarily with masked language modeling (MLM), which predicts amino-acid identities at masked positions. We ask whether latent-space prediction can complement these token-level objectives under …

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