Context Memorization for Efficient Long Context Generation
Modern large language model (LLM) applications increasingly rely on long conditioning prefixes to control model behavior at inference time. While prefix-augmented inference is effective, it incurs two structural limitations: i) the prefix's influence fades as generation proceeds, and ii) attention computation over the prefix scales linearly with its length. Existing approaches either keep the prefix in attention while compressing it, or internalize it into model parameters through gradient-based training. The former still attends to the prefix at inference, while the latter is training-intensive and ill-suited to prefix updates. To address these issues, we propose attention-state memory, ...
| Authors | Yasuyuki Okoshi, Hao Mark Chen, Guanxi Lu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 18 May 2026 |
| Last updated at source | 18 May 2026 |
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