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Georgia Institute of Technology
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AnyBody enables humanoid robots to seamlessly control movements from any chosen subset of body keypoints, revolutionizing how we approach humanoid locomotion and manipulation.
Rule-grounded reasoning can cut average distance errors in driving VLAs by nearly half, fundamentally enhancing their decision-making transparency and reliability.
MLLM-empowered re-ranking can dramatically enhance person re-identification accuracy in unseen domains, overcoming traditional encoder limitations.
EOM-CCSD achieves unprecedented accuracy in band gap predictions, outperforming traditional methods with a mean absolute error of just 0.27 eV.
Existing corpus poisoning attacks falter in realistic RAG systems, but the new CRCP framework ensures adversarial effectiveness by aligning chunking and reranking processes.
