PREM: Prefix-Steered Recurrent Memory for Long-Video Understanding
Long-video understanding must capture transient visual evidence under strict token budgets, yet existing methods compress frames, append memory tokens, or alter internal key-value (KV) caches. We introduce Prefix-Steered Recurrent Memory (PREM), a memory-token-free framework for frozen vision-language models (VLMs). PREM separates video ingestion from query answering: a recurrent writer distills visual streams into a compact 256 KiB multi-slot associative state, while a question-conditioned readout adds memory-derived key/value (K/V) steering modulations to existing non-visual prompt prefixes during prefill. This enables write-once, query-many inference without extra prompt tokens or deco...
| Authors | Siru Zhong, Qiongyan Wang, Xiaohui Lv et al. |
|---|---|
| Primary category | cs.CV |
| Published | 20 September 2026 |
| Last updated at source | 20 September 2026 |
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