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 introduces a \emph{quality-gated post-training conversion} framework using CeNN-inspired cellular-recurrent layers with bounded local processing, compact recurrent memory, routing, fusio

So what

What this event means by reading role—not a longer recap.

BuilderA conservative layer-by-layer conversion recipe with accept-or-rollback checks could let builders shrink KV cache on small pretrained models, but reported gains
ResearcherThe paper frames attention replacement as a representation-compatibility problem, showing layer 3 of SmolLM2-135M is rejected on fidelity despite acceptable NLL

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