Asymmetric Looped Transformers: Fixed Global Representations for an Iterative Decoder
Runs a causal encoder once, then repeatedly applies a shared decoder that attends to fixed encoder context and its evolving hidden states. Evaluates this division of work against fully looped and standard language models during pretraining.
First public: · Added to this list: October 22, 2026
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Recurrent reasoning — Repeat parts of the model or keep intermediate information in memory while working through a problem. Some methods also learn how many passes to run.