TinyCeNN-LM uses quality-gated post-training conversion with CeNN-inspired cellular-recurrent layers to replace attention in pretrained language models.
Read the original at arxiv.org→arXiv:2609.21139v1 Announce Type: new Abstract: Replacing attention in a pretrained language model is a compatibility problem: a plausible substitute may alter representations expected by later layers. TinyCeNN-LM...
Original headline: "TinyCeNN-LM: Quality-Gated Conversion of Pretrained Attention with CeNN-Inspired Cellular-Recurrent Layers"
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- Sep 21, 04:00 UTC arXiv cs.AI lead source TinyCeNN-LM: Quality-Gated Conversion of Pretrained Attention with CeNN-Inspired Cellular-Recurrent Layers