SMI Brings Understanding-Driven Spatial Memory Management to Long-Video World Models
Ying Yang · hf · 2026-10-05
A new paper introduces Spatial Memory Intelligence (SMI), the first framework to systematically use an understanding model (MLLM) to manage spatial memory in long-video world models, addressing the challenge of long-range spatial context as memory sequences grow.
SMI coordinates four atomic operations: spatial clustering, within-cluster sparsification, action-aware retrieval, and reliability-aware filtering. Experiments across multiple baselines, benchmarks, and world-model backbones show comprehensive improvements in memory sparsity, generation stability, and spatial consistency.
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