How Depth Growth and Looping Shape In-Context Learning in Language Models
Compares dense, progressively grown and looped Transformers on in-context learning. Attention-head tracing and ablation link stronger context use in grown and looped models to greater reliance on induction heads.
First public: · Added to this list: November 2, 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.