Loopback Transformer: Towards a Unified View of Residual Looped Transformers
Treats residual connections as memory across depth. Loopback keeps the original input separate, decays earlier block updates, and learns attention weights over those updates to control their contribution during repeated transformer passes.
First public: · Added to this list: November 9, 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.
Analysis and evaluation — Measure how well these methods work, investigate what their internal computation does, and identify where they fail. This includes experiments, studies of model internals, and mathematical results.