Recent video-language models increasingly adopt hybrid architectures that interleave linear and full attention layers for efficient long-context processing.
While the recurrent state of linear attention remains fixed in size, the KV cache of full attention continues to grow with the video stream, making eviction necessary under a bounded memory budget.
The key challenge in streaming is that eviction must occur before the question arrives, so what to retain has to be decided without the question.
Existing eviction methods derive token scores from the KV cache itself, using position, attention, or key-value representations, and attention-based scores further require proxy queries or extra computation.
Hybrid backbones offer another source of signal.
In gated-delta linear attention, the recurrent state is updated by the residual between each input and what can already be retrieved from the state, so its change over a chunk of frames reflects how much new information the chunk brings.
We propose DeltaS, a query-agnostic, training-free method that retains video chunks inducing larger normalized state change, or state drift.
In a controlled comparison with the budget and retention policy held fixed, state drift outperforms position-, attention-, and key-value-based signals.
These results suggest that the two memories of hybrid architectures can work cooperatively.
Code is available at https://github.com/MaumAI-Company/DeltaS.