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Исследования2026年9月22日
TinyCeNN-LM: Quality-Gated Conversion of Pretrained Attention with CeNN-Inspired Cellular-Recurrent Layers
Replacing attention in a pretrained language model is a compatibility problem: a plausible substitute may alter representations expected by later layers. TinyCeNN-LM introduces a \emph{quality-gated post-training conversion} framework using CeNN-inspired cellular-recurrent…
Kabeh Mohsenzadegan, Vahid Tavakkoli, Kyandoghere Kyamakya·arXiv — cs.AI
Original SourceKey Takeaways
- 01.Replacing attention in a pretrained language model is a compatibility problem: a plausible substitute may alter representations expected by later layers
- 02.TinyCeNN-LM introduces a \emph{quality-gated post-training conversion} framework using CeNN-inspired cellular-recurrent…
This material is provided as an extended analytical summary.
Read full article at sourceAggregated within AI Qazaqstan national intelligence stream with author attribution.
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