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Semantic relevance is a dangerously loose proxy for safety: replacing standard RAG with formal, scene-grounded rule entailment prevents vision-language planners from acting on ungrounded risk assumptions.
Transforming object-centric prediction into a semantic interface, SR-WM enables planning-oriented decision-making that captures the complex roles of entities in physical interactions.
SLIM achieves state-of-the-art performance in robot manipulation with just 0.5B parameters, outperforming larger models while slashing GPU memory usage and inference latency.
