Running Fewer Loops Removes Refusal Behaviour in Recurrent-Depth Language Models
Studies how reducing recurrent depth changes refusal behavior in two looped language-model families. Uses probes and interventions to distinguish recognizing a harmful request from refusing it, and tests safety training across randomly selected depths.
First public: · Added to this list: October 18, 2026
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ArXiv preprint
arXiv:2610.15218 · Initial submission: 2026-10-10
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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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