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Implicit reasoning steering can covertly amplify latent biases in language models, shifting their predictions through seemingly innocuous text.
Energy-based learning can significantly enhance the robustness and accuracy of tensegrity structure predictions, overcoming traditional method limitations.
Claw-like agents are vulnerable to severe security breaches, with malicious plugins achieving a 100% success rate in attacks.
Visual reasoning models can slash token usage by up to 90% without sacrificing accuracy by adaptively choosing reasoning paths based on task complexity.
