Author

U. Acikalin

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#artificial intelligence Preprint Sep 2026

MoRE: Mixture of Reused Experts

Mixture-of-Experts (MoE) architectures decouple model capacity from computational cost, yet incur high memory footprints as parameters grow linearly with the number of experts. Recurrent Transformers achieve parameter efficiency by reusing layer weights, but typically lack the capacity for competitive language modeling...

Eric S. Qiu, U. U. Acikalin, Justin Lovelace et al. · 1 citation

Open access Aug 2026

Charting the small-molecule universe from mass spectra with neuro-symbolic AI

AIMe (AI Molecule Explorer), a multi-agent neuro-symbolic AI framework that transforms the interpretation of unknown spectra into an omics-scale exploration across the known structural space, providing chemically interpretable annotations, is introduced.

U. U. Acikalin, Dieqiao Feng, Aaron M. Ferber et al. · bioRxiv · 0 citations