When Does Depth Matter For In-Context Learning? Adaptive Inference in Deep Transformers
Transformers perform computations through many successive attention and feedforward blocks, allowing them to learn complex correlations between a large collection of coupled variables. When does stacking successive attention-feedforward computations over many layers provide a computational advantage over a single transformer block? We address this question by examining in-context learning in generalized linear attention transformers. We first introduce a general theory of distributed inference in such transformers, subject to constraints on communication and depth. We show that such systems can exploit internal representations (`function vectors') to infer a latent context variable at inc...
| Authors | Ravin Raj, Gautam Reddy |
|---|---|
| Primary category | cs.LG |
| Published | 15 June 2026 |
| Last updated at source | 27 September 2026 |
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