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The University of Texas at Austin
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Counterfactual reasoning is the key to unlocking a 5.5% accuracy boost in pragmatic language understanding for LLMs, challenging their traditional reliance on literal interpretations.
Forget hand-engineered force models: a vision-language model, conditioned on past grasp attempts, can predict the perfect pre-grasp force, boosting success rates by 24% on real robots.
