#KAIST (7)

WorldKV: managing a world model's memory like a cache

Memory in autoregressive video world models is a bind: full KV attention preserves revisit consistency, but memory footprint and attention cost grow linearly with rollout length, passing 200 GB per minute and exceeding a B200. Sliding window is real-time but drifts. WorldKV trains nothing and works purely at inference: it retrieves evicted KV chunks by camera/action similarity and compresses each chunk to half size by key-key cosine similarity, fitting roughly twice the history at roughly twice the throughput of full KV, with no memory training at all.