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Robert Obryk

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#artificial intelligence Preprint Oct 2026

Retrieval-Centric Deep Learning in Growing Nonparametric Neural Networks

We investigate a general-purpose layer for deep learning that, instead of compressing arbitrary-size training data into fixed-size weight matrices, stores a new pair of key-value representations for every data point during training, and retrieves and recombines these representations through an attention mechanism at in...

Maximilian Schlegel, Rajai Nasser, Seijin Kobayashi et al. · 0 citations
#machine learning Preprint Sep 2026

Diffusion as a Training Curriculum for Timestep-Free Iterative Reasoning

This work adds a persistent hidden state to a diffusion denoiser and removes its timestep conditioning, leaving a single shared update that can be run to arbitrary depth, and develops an anytime solver that keeps improving with inference depth far beyond the rollout lengths and backpropagation window used in training.

M. Drozdova, Aidan Sirbu, Pietro Miotti et al. · 3 citations

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