A Mechanistic Analysis of Looped Reasoning Language Models
Analyzes how hidden states and attention change across repeated transformer layers. Finds that recurrent blocks often repeat the stages seen in feedforward models, while input injection and normalization affect whether those stages remain stable at greater depth.
First public: · Added to this list: October 26, 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.