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AtlasVLA introduces a dual-memory architecture with persistent world-ego state modeling to overcome perception and task-progress forgetting in vision-language-action models, achieving state-of-the-art long-horizon manipulation from a single wrist camera.
This paper proposes DLA, a dynamic memory modeling framework for multi-state linear attention that adaptively merges states based on token information variation and maintains a fixed-size state cache, enabling better long-context representation without the quadratic complexity of standard attention.
DLA introduces adaptive state merging and capacity-bounded memory modeling for multi-state linear attention, improving long-context LLM performance.