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TOPAS slashes job completion times by up to 49.4% in multi-agent LLM serving by intelligently balancing prefix caching and request scheduling.
RNN-guided load balancing slashes global workload imbalance from 11.3% to 3.5%, drastically improving simulation efficiency in complex multicellular growth models.
RefVideo-6M revolutionizes video editing datasets by providing 5 million high-quality editing samples that prioritize visual references over flawed automatic edits.
A coding agent can maintain persistent world states and generate coherent visualizations, revolutionizing how we model complex environments.
CAT's trajectory-level continuous action representation significantly enhances robotic manipulation success rates by eliminating representational redundancy and ensuring temporal consistency.
Grounding action representations in environmental context can drastically improve robotic manipulation performance, especially for complex tasks.
