Seq103: New Neuroevolution Framework Achieves Compact Sequence Models with Minimal Parameter Loss
Researchers have proposed Seq103, a unified neuroevolution framework designed to discover compact neural network architectures for sequence classification tasks. The system uses a NEAT-style evolutionary approach to optimize both network topology and weights, and was evaluated on 8 text classification datasets and 128 univariate time-series datasets from the UCRArchive2018 benchmark. The work demonstrates that highly parameter-efficient models can retain competitive accuracy, which has implications for deploying machine learning in resource-constrained environments.
Seq103 is a neuroevolution framework that applies evolutionary algorithms to discover compact neural architectures for both recurrent and feedforward sequence classification. Its core design consists of a shared evolutionary backbone—featuring node-and-connection representations, RMSE-based evaluation, mutation-driven evolution with class-wise recombination, and elitism—plus an optional hidden-state extension that enables temporal memory for step-wise recurrent tasks. On step-wise recurrent tasks such as text classification, Seq103 retained an average of 86.96% of best-baseline accuracy while using between 34.6x and 3,218x fewer parameters than competing models. On sample-wise feedforward tasks across the full 128-dataset UCRArchive2018 time-series benchmark, it retained 81.95% of best-baseline accuracy with parameter reductions ranging from 11.8x to over 160,000x. The unified design allows the same core search pipeline to handle both task types by simply enabling or disabling the recurrent extension. The paper was submitted to arXiv in June 2026 and has not yet undergone formal peer review.
What's missing
The study does not report wall-clock training or search time costs for the evolutionary process, which is a common practical bottleneck for neuroevolution methods. It is also unclear how Seq103 performs on multivariate time-series or tasks beyond classification. As a preprint, the work has not yet been peer-reviewed, and independent replication has not been reported.
What different sources said
- arXiv cs.AICenter
Seq103: A Unified Neuroevolution Framework for Compact Sequence Architecture Discovery
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