Sequential Correlations Change In-Context Learning: Effective Context Length and Architectural Mismatch
Modern sequence models have a striking capacity for in-context learning (ICL); they can perform new tasks based only on examples given in the prompt. Understanding how this ability emerges requires theory that captures important properties of natural data. Linear regression has served as a useful sandbox for ICL theory, but existing work has largely focused on prompts with independent examples. In this work, we extend this setting to sequentially correlated data, a basic feature of real sequences. We present a solvable model based on linear attention and test our predictions on realistic transformer architectures. We identify two distinct effects: First, when the query token is independen...
| Authors | Mary Letey, Yue M. Lu, Cengiz Pehlevan et al. |
|---|---|
| Primary category | stat.ML |
| Published | 4 July 2026 |
| Last updated at source | 4 July 2026 |
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