Martín, Adri Gómez, Möller, Felix, McDonagh, Steven, Abella, Monica, Desco, Manuel, Crowley, Elliot J., Klein, Aaron, Ericsson, Linus · Dec 4, 2025
Sequence Alignment for Evolutionary Architecture Search
Markdown · JSON · Original source
The paper’s authors introduce two adaptations of the Smith-Waterman algorithm, a method for finding matching segments in sequences, for grammar-based evolutionary architecture search. Neural architecture search (NAS) explores possible designs; here, the method calculates edit distance between architectures and helps combine two parent models into hybrid offspring. The authors say these capabilities support crossover-based search heuristics, analysis of the architectural loss landscape, and tracking population diversity. They report that their method “vastly improves computational complexity over previous work” and enables efficient calculation of shortest paths between architectures. In evolutionary searches using crossover, they say it achieves competitive results and outperforms competing methods. The contribution is a tool for comparing and combining architectures in expressive search spaces, which the authors argue can help search algorithms identify and reuse powerful components. They propose future work on discovering components for broader use in neural architecture design and extending the method beyond NAS. The abstract does not give the comparisons or measurements behind the reported performance, so the scope of those results cannot be assessed from this evidence alone.
Headline and summary by Escape Meridian, prepared by Luna from the attributed source. A source check is not independent confirmation of the source’s claims.
Original source
cs.AI · Article
Source details
Original source date: Dec 4, 2025 (2025-12-04).
Date basis: Date basis not provided.
Added to Escape Meridian: 2026-11-09T11:46:33.212Z. First observed: 2026-11-09T05:06:50.855Z. Retrieved: 2026-11-09T10:16:15.328Z.
Referenced by
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Limits
The abstract does not identify the competing methods or provide the evaluation results behind its reported comparative performance. The evidence is the abstract and does not include the paper’s detailed methods or results.
Open question
Which competing methods and evaluation results support the authors’ report that crossover-based searches achieve competitive results and outperform competing methods?
Source-stated
The authors adapt two Smith-Waterman algorithms for local sequence alignment to compute edit distance in grammar-based evolutionary architecture search.
we introduce two adapted variants of the Smith-Waterman algorithm for local sequence alignment and use them to compute the edit distance in a grammar-based evolutionary architecture search
Source-stated
The method calculates a distance metric between neural architectures and generates hybrid offspring from two parent models.
These algorithms enable us to efficiently calculate a distance metric for neural architectures and to generate a set of hybrid offspring from two parent models.
Source-stated
The authors report improved computational complexity over previous work and efficient shortest-path calculation between architectures.
We highlight how our method vastly improves computational complexity over previous work and enables us to efficiently compute shortest paths between architectures.
Source-stated
In evolutionary searches using crossover, the authors report competitive results and performance above competing methods.
When instantiating the crossover in evolutionary searches, we achieve competitive results, outperforming competing methods.
Source-stated
The authors identify discovering reusable components and extending applications beyond NAS as future work.
Future work can build upon this new tool, discovering novel components that can be used more broadly across neural architecture design, and broadening its applications beyond NAS.
Source and contributor context
Martín, Adri Gómez, Möller, Felix, McDonagh, Steven, Abella, Monica, Desco, Manuel, Crowley, Elliot J., Klein, Aaron, Ericsson, Linus · Article author
These are credits stated by the source; they do not establish a profile match or authorship of every passage.
Source media
No suitable media was found by the supported page extraction.
Terms and references
Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.
Neural network
A system of connected simple calculations, loosely inspired by the brain, that learns patterns from data.
arXiv
arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Editorial provenance
Version 1. Publication ID: ff08980d-0476-4a0e-8624-3b266050e579.
Prepared by gpt-6-luna; grounding review by gpt-6-luna.
Source check: passed by anthropic/claude-opus-5.5 on 2026-11-09T11:46:33.212Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: material_development.
Observation: b40a2c380e7d5509755f479e52ab0438c8b633c7eae93e1a2a09c35e7f7a8a96. Source text hash: 33f1037908c0b01e223ea391fe30777e9d249b546c899fa48a8669bac01cfb01.
Revision and source-check history
Version 1 · 2026-11-09T11:46:33.212Z
Sequence Alignment for Evolutionary Architecture Search
The paper’s authors introduce two adaptations of the Smith-Waterman algorithm, a method for finding matching segments in sequences, for grammar-based evolutionary architecture search. Neural architecture search (NAS) explores possible designs; here, the method calculates edit distance between architectures and helps combine two parent models into hybrid offspring. The authors say these capabilities support crossover-based search heuristics, analysis of the architectural loss landscape, and tracking population diversity. They report that their method “vastly improves computational complexity over previous work” and enables efficient calculation of shortest paths between architectures. In evolutionary searches using crossover, they say it achieves competitive results and outperforms competing methods. The contribution is a tool for comparing and combining architectures in expressive search spaces, which the authors argue can help search algorithms identify and reuse powerful components. They propose future work on discovering components for broader use in neural architecture design and extending the method beyond NAS. The abstract does not give the comparisons or measurements behind the reported performance, so the scope of those results cannot be assessed from this evidence alone.
Original source
cs.AI · Article
Source details
Original source date: Dec 4, 2025 (2025-12-04).
Date basis: Date basis not provided.
Added to Escape Meridian: 2026-11-09T11:46:33.212Z. First observed: 2026-11-09T05:06:50.855Z. Retrieved: 2026-11-09T10:16:15.328Z.
Limits
The abstract does not identify the competing methods or provide the evaluation results behind its reported comparative performance. The evidence is the abstract and does not include the paper’s detailed methods or results.
Open question
Which competing methods and evaluation results support the authors’ report that crossover-based searches achieve competitive results and outperform competing methods?
Source-stated
The authors adapt two Smith-Waterman algorithms for local sequence alignment to compute edit distance in grammar-based evolutionary architecture search.
we introduce two adapted variants of the Smith-Waterman algorithm for local sequence alignment and use them to compute the edit distance in a grammar-based evolutionary architecture search
Source-stated
The method calculates a distance metric between neural architectures and generates hybrid offspring from two parent models.
These algorithms enable us to efficiently calculate a distance metric for neural architectures and to generate a set of hybrid offspring from two parent models.
Source-stated
The authors report improved computational complexity over previous work and efficient shortest-path calculation between architectures.
We highlight how our method vastly improves computational complexity over previous work and enables us to efficiently compute shortest paths between architectures.
Source-stated
In evolutionary searches using crossover, the authors report competitive results and performance above competing methods.
When instantiating the crossover in evolutionary searches, we achieve competitive results, outperforming competing methods.
Source-stated
The authors identify discovering reusable components and extending applications beyond NAS as future work.
Future work can build upon this new tool, discovering novel components that can be used more broadly across neural architecture design, and broadening its applications beyond NAS.
Source and contributor context
Martín, Adri Gómez, Möller, Felix, McDonagh, Steven, Abella, Monica, Desco, Manuel, Crowley, Elliot J., Klein, Aaron, Ericsson, Linus · Article author
These are credits stated by the source; they do not establish a profile match or authorship of every passage.
Source media
No suitable media was found by the supported page extraction.
Terms and references
Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.
Neural network
A system of connected simple calculations, loosely inspired by the brain, that learns patterns from data.
arXiv
arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Editorial provenance
Version 1. Publication ID: ff08980d-0476-4a0e-8624-3b266050e579.
Prepared by gpt-6-luna; grounding review by gpt-6-luna.
Source check: passed by anthropic/claude-opus-5.5 on 2026-11-09T11:46:33.212Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: material_development.
Observation: b40a2c380e7d5509755f479e52ab0438c8b633c7eae93e1a2a09c35e7f7a8a96. Source text hash: 33f1037908c0b01e223ea391fe30777e9d249b546c899fa48a8669bac01cfb01.
passed · 2026-11-09T11:46:33.212Z · anthropic/claude-opus-5.5
Publication: ff08980d-0476-4a0e-8624-3b266050e579
attribution — pass: The reading attributes the method, the efficiency claims and the performance claims to the paper's authors. It uses 'they report' and 'they say' rather than presenting the claims as established facts. The arXiv listing names the authors and the abstract.
Authors:Adri Gómez Martín, Felix Möller, Steven McDonagh, Monica Abella, Manuel Desco, Elliot J. Crowley, Aaron Klein, Linus Ericsson
summary — pass: The reading leads with the development and briefly explains Smith-Waterman and NAS. It preserves the authors' quoted claim and conditions, and it flags that the abstract gives no comparison details. Terms such as edit distance, crossover and loss landscape are kept as exact technical terms, so tooltips could explain them. The title, the question and the stated significance match the abstract. Minor wording issue: claim 1 says the authors 'adapt two Smith-Waterman algorithms'. The source describes two adapted variants of one algorithm, and the summary sentence states this correctly.
we introduce two adapted variants of the Smith-Waterman algorithm for local sequence alignment
date — pass: The originalPublished date of 2025-12-04 matches the v1 submission date. It is not the date of the v2 revision on 6 Nov 2026 or the date of the feed entry.
[Submitted on 4 Dec 2025 (v1), last revised 6 Nov 2026 (this version, v2)]
support — pass: Each claim is supported by a verbatim excerpt from the abstract. The reading invents no benefits, costs or stakeholders. The open question follows from the evidence gap: the competing methods are not identified. The limitations correctly note that only the abstract is available.
When instantiating the crossover in evolutionary searches, we achieve competitive results, outperforming competing methods.
media — pass: The selected media is the verified direct link to the original arXiv abstract page. It is the source itself and the most direct way for readers to reach it.
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A system of connected simple calculations, loosely inspired by the brain, that learns patterns from data.
Definition referencearXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXivarXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXivA system of connected simple calculations, loosely inspired by the brain, that learns patterns from data.
Definition referencearXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXivarXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXiv