Exploration of Fast-Slow Latent Recurrence for Train-Short, Test-Long Generalization

arXiv:2604.01577v3 Announce Type: replace Abstract: We study out of distribution generalization in streaming tasks where models are trained on short sequences but must operate over much longer, unknown horizons under bounded memory. Our focus is on a persistent fast slow recurrent formulation in which a latent state is maintained across observations rather than reset at each stream step. For each incoming observation, the model performs multiple weight-shared latent updates with a recurrent core and then carries the resulting state forward to the next observation. This allows the model to main

Source: arXiv cs.LG — read the full report at the original publisher.

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