Learning to Compute Is Not Learning to Retain or Reuse in Looped Transformers
Separates computing an answer, preserving it through additional loops, and keeping its hidden state usable for later steps. On state-machine and maze tasks, shows that training can improve one ability without securing the others.
First public: · Added to this list: October 18, 2026
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arXiv:2610.19768 · Initial submission: 2026-10-14
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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.
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.
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