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University of Pittsburgh
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PIER outperforms standard retrieval methods by ensuring that environmental predictions are consistent with physical dynamics, leading to more accurate modeling of complex systems.
Wheeled bipedal robots can now stably slide and manipulate objects, achieving complex tasks like retrieving items from tight spaces.
LLMs trained on raw code text learn surface-level cues that trigger false positives when detecting vulnerabilities in other languages, but simply feeding them ASTs at inference time can dramatically reduce these errors.
