[Submitted on 9 Sep 2026]
Title:Manifold Projection and Iterative Autoencoder Refinement for Masked Language Modeling
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Abstract:In Transformer-based masked language models, attention is the primary mechanism for context mixing, but there are other ways to mix data across tokens. Recent attention-free mixers replace attention with fixed or hypernetwork-generated MLPs, alternating their dynamic, content-dependent weighting for computational simplicity. We build an alternative that gets the same property from a low-rank bottleneck autoencoder. We replace attention with a stack of autoencoder-based mixing modules, one operating over local neighborhoods, one over the full sequence, and one across attention heads, each compressing and reconstructing its input through a bottleneck, and its width is a hyperparameter rather than a training effect. In masked positions, we introduce an iterative refinement procedure that has two distinct steps. A pulling step that pulls an embedding representation toward a weighted average of its neighbors, and a correcting step that projects the result back to the learned manifold via an autoencoder. Our architecture achieves a significant portion of attention's performance at about $1.9 \times$ fewer FLOPs when pretrained on C4 and evaluated with parameter-matched BERT baselines. Our model equals parameter-matched BERT and TinyBERT baselines on the rarest-token frequency bucket using a frequency-aware training schedule that samples rare tokens more than uniformly for the masking tasks.
Comments: Submitted to IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI)
Subjects:
Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as: arXiv:2609.30288 [cs.CL]
(or arXiv:2609.30288v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.30288
arXiv-issued DOI via DataCite
Submission history
From: Narges Mokhtari [view email] [v1] Wed, 9 Sep 2026 14:33:05 UTC (255 KB)
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