Author

Irina Rish

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#artificial intelligence Preprint Oct 2026

Recurrent In-Context Learning for Tabular Foundation Models

Tabular foundation models adapt to new datasets through in-context learning (ICL), and, like most transformers, they tie computational depth to the number of independently parameterized layers. We ask whether the ICL stage of such a model needs a distinct set of parameters for every layer, or only depth. We introduce T...

Tejas Vaidhya, Gaurav Sahu, Irina Rish · 0 citations

#machine learning Preprint Sep 2026

Temporal Recurrence Favors Fewer Layers

Across Sokoban and autoregressive FineWeb language modeling, this work finds that temporal recurrence shifts the best observed compute allocation toward substantially fewer layers, with comparable or better performance.

Ivan Anokhin, Johan S. Obando-Ceron, Irina Rish et al. · 1 citation