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R. Astudillo

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#artificial intelligence Preprint Oct 2026

The Surprising Effectiveness of Shared Memory in Looped Transformers

Looped Transformers apply the same layers several times per token, adding compute to improve quality without more parameters. Each recursion, however, writes its own key-value cache, so memory still grows with compute. Inference-time techniques can shrink this cache at a cost in quality. We pretrain looped language mod...

Giovanni Monea, Keshav Ramji, Yousef El-Kurdi et al. · 0 citations
#machine learning Preprint Oct 2026

Learning to Predict Distributions over Weight Updates for Test-Time Adaptation

Hypernetworks have recently shown success in dynamically adapting the parameters of Large Language Models (LLMs) at runtime based on signals such as task descriptions or additional demostrations. Here we ask: how much adaptation signal can be obtained using only the input query to an LLM?. To answer this, we study quer...

A. Khan, Keshav Ramji, Tahira Naseem et al. · 0 citations

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