AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work introduces IntroConformal, a training-free Conformal Risk Control (CRC) framework that provides finite-sample, distribution-free factuality guarantees and proposes verification probability, a stronger score capturing the model's self-administered judgment on claim factuality.
dLVLMs reverse the yes-bias of AR models in binary visual queries and collapse to near-zero accuracy on underrepresented racial groups with opposite-polarity gender bias, suggesting reliability is shaped by the generative paradigm together with training data.
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