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GROVE achieves state-of-the-art performance in memory retrieval for wearable assistants by seamlessly integrating reactive and proactive memory access from continuous video streams.
PReM achieves a remarkable balance between context preservation and refresh, outperforming existing methods even at 32x compression rates.
Proactive assistance can significantly enhance user experience by intelligently deciding when to intervene based on rich contextual understanding rather than waiting for prompts.
Training generative inverse renderers on AAA game footage closes the realism gap, enabling superior generalization and controllable generation compared to models trained on existing synthetic datasets.
