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The University of Hong Kong, INFIFORCE
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Hierarchical latent actions in HiMem-WAM boost long-horizon manipulation robustness, outperforming existing models in real-world scenarios.
MemDreamer narrows the performance gap with human experts in long video understanding to just 3.7 points while processing only 2% of the full context.
Streaming reasoning steps can boost multi-agent system performance by 7.3 percentage points on average, revealing a new dimension for scaling effectiveness and efficiency.
