KuaFu: Compressing Long User Behavior into Understanding at Billion Scale
Conversational agents, generative recommenders, and personalized advertising all rest on one capability: understanding each user from raw behavior. Prevailing industrial practice is task-specific: for each task, a relevant subsequence is extracted from the full history and a dedicated model trained on it. In production it hits two bottlenecks. First, even after filtering, a single-task sequence stays extremely long: content-interest summarization reads several hundred items per user, tens of thousands of tokens once serialized as prompt text. Second, profiles are refreshed routinely: a billion users weekly, roughly 100K QPM in aggregate, which under a fixed GPU budget sets a hard throughp...
| Authors | Jiahao Hui, Lin Zhu, Yishen Hu et al. |
|---|---|
| Primary category | cs.IR |
| Published | 25 September 2026 |
| Last updated at source | 25 September 2026 |
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