Answering While You Type: Optimizing Time-to-Answer with Streaming Generation
Work on efficient large-language-model inference has concentrated almost entirely on reducing the cost of decoding once a request is complete. The quantity a user experiences, however, is the interval between submitting a request and receiving a usable response, and that interval can be shortened without making decoding itself faster. While a request is being composed it is already partially specified, yet conventional deployments begin no computation until submission. We propose and study streaming generation, a decoding regime in which the model begins producing a response from an incomplete request and revises it as the remaining input arrives, so that only a small residue of the answe...
| Authors | Nikita Dragunov, Matvey Mikhalchuk, Anton Razzhigaev |
|---|---|
| Primary category | cs.CL |
| Published | 19 October 2026 |
| Last updated at source | 19 October 2026 |
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