Frontier Language Models Struggle to Copy: Text Can Be Better Viewed in 2D
While large language models (LLMs) can solve advanced reasoning problems in seconds, we show that even frontier models fail to perform a much simpler operation: exactly copying an input string that lies well within their context windows. We attribute this failure to positional encodings in Transformer architectures, whose inductive bias favors copying through a shortcut based on matching local contexts rather than carefully locating the corresponding input positions. To address this issue, we introduce 2D-RoPE, which organizes text into a 2D grid rather than a 1D sequence and assigns each token a row ID and a column ID. Under this view, copying becomes simply retrieving input tokens at a ...
| Authors | Haodong Wen, Yiran Zhang, Yingfa Chen et al. |
|---|---|
| Primary category | cs.CL |
| Published | 17 July 2026 |
| Last updated at source | 17 July 2026 |
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