Line-Coupled Language Model
Autoregressive language models generate one token per decoding step, limiting the useful output of each forward pass. Although diffusion models, insertion-based decoding, and multi-token prediction enable parallel generation, they either incur additional training-time token traffic or struggle to predict strongly dependent future tokens. We introduce the Line-Coupled Language Model (LCLM), an autoregressive model that advances multiple text lines together by predicting the next token for every active line while coupling the lines through shared causal context. LCLM interleaves line tokens into a single causal sequence and uses line-staggered rotary positions, retaining the standard next-t...
| Authors | Shiyuan Li, Shaorong Zhang, Zhaorui Yang et al. |
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| Primary category | cs.CL |
| Published | 7 September 2026 |
| Last updated at source | 7 September 2026 |
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