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[2602.23179] Induction Meets Biology: Mechanisms of Repeat Detection in Protein Language Models
Summary
This article explores how protein language models (PLMs) detect repeating segments in protein sequences, revealing mechanisms for identifying both exact and approximate repeats, which are crucial for understanding protein structure and function.
Why It Matters
Understanding how PLMs identify repeats in protein sequences is essential for advancing bioinformatics and computational biology. This research bridges machine learning with biological insights, potentially enhancing the accuracy of protein analysis and evolutionary studies.
Key Takeaways
- PLMs can identify both exact and approximate repeats in protein sequences.
- The detection mechanism involves building feature representations and using specialized attention heads.
- Understanding these mechanisms can improve the study of complex evolutionary processes.
Computer Science > Machine Learning arXiv:2602.23179 (cs) [Submitted on 26 Feb 2026] Title:Induction Meets Biology: Mechanisms of Repeat Detection in Protein Language Models Authors:Gal Kesten-Pomeranz, Yaniv Nikankin, Anja Reusch, Tomer Tsaban, Ora Schueler-Furman, Yonatan Belinkov View a PDF of the paper titled Induction Meets Biology: Mechanisms of Repeat Detection in Protein Language Models, by Gal Kesten-Pomeranz and 4 other authors View PDF HTML (experimental) Abstract:Protein sequences are abundant in repeating segments, both as exact copies and as approximate segments with mutations. These repeats are important for protein structure and function, motivating decades of algorithmic work on repeat identification. Recent work has shown that protein language models (PLMs) identify repeats, by examining their behavior in masked-token prediction. To elucidate their internal mechanisms, we investigate how PLMs detect both exact and approximate repeats. We find that the mechanism for approximate repeats functionally subsumes that of exact repeats. We then characterize this mechanism, revealing two main stages: PLMs first build feature representations using both general positional attention heads and biologically specialized components, such as neurons that encode amino-acid similarity. Then, induction heads attend to aligned tokens across repeated segments, promoting the correct answer. Our results reveal how PLMs solve this biological task by combining language-based patte...