Can Large Language Models "Hyper-Thread"?
Large language models generate tokens sequentially, but can they execute multiple tasks concurrently while forming each token? Broader attention allocation may provide a mechanism for such task concurrency. Existing approaches to scaling inference primarily rely on longer generations, more samples, or additional verification stages, while attention dispersion is often treated as a signal of interference or error. Task concurrency within serial generation therefore remains underexplored. We propose the Model Hyper-Threading Hypothesis and evaluate its predictions using multiple coordinated tasks that share state within the same problem. We design three conditions (Baseline, Serial Function...
| Authors | Fei Ding |
|---|---|
| Primary category | cs.CL |
| Published | 23 August 2026 |
| Last updated at source | 23 August 2026 |
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