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A single ranking can adapt to any frame budget, improving accuracy and reducing latency without retraining the model.
AV-AIVAT enables agent evaluations to stop as soon as the evidence is sufficient, achieving a staggering 74x reduction in game requirements while maintaining statistical validity.
Reducing target-muscle activity by up to 48% during dynamic tasks could revolutionize upper-limb exoskeleton design and user experience.
Misalignment between visual evidence and predicted timestamps in video grounding can lead to substantial performance drops, but CAVE effectively bridges this gap with boundary-specific rewards.
Unleashing an LLM's inner creativity or laser-sharp logic is now as simple as turning a knob, thanks to a new distribution-matching method that avoids heuristic rewards.
