Universitat Pompeu Fabra

On social networks of large-language-model agents, whether a post spreads depends on how many of each agent's contacts have shared it. Earlier predictions measured a single response curve independent of contact count, or one response in a large fully connected group. Measuring the response separately at each contact count and feeding it into mean-field cascade theory reproduces the cascade sizes and tipping seed fractions of full LLM simulations, while responses that ignore contact count mispredict cascades increasingly on heterogeneous networks.

On social networks of large-language-model agents, whether a post spreads depends on how many of each agent's contacts have shared it. Earlier predictions measured a single response curve independent of contact count, or one response in a large fully connected group. Measuring the response separately at each contact count and feeding it into mean-field cascade theory reproduces the cascade sizes and tipping seed fractions of full LLM simulations, while responses that ignore contact count mispredict cascades increasingly on heterogeneous networks.

Saliency maps for convolutional classifiers on medical images now have a new training route: SalientNet needs no annotated saliency ground truth and instead requires the generated mask to keep enough information for a frozen classifier to reproduce its original prediction within a controlled tolerance. Across BreakHis, Brain MRI ND5 and FracAtlas, it reaches higher faithfulness (Win %) and lower prediction Drop (%) than Grad-CAM, Grad-CAM++ and LIME in nearly all dataset-architecture combinations, and on FracAtlas its maps correlate spatially with practitioner-annotated fracture regions, with a mean ground-truth active fraction of up to 0.96 for ResNet-18.

Saliency maps for convolutional classifiers on medical images now have a new training route: SalientNet needs no annotated saliency ground truth and instead requires the generated mask to keep enough information for a frozen classifier to reproduce its original prediction within a controlled tolerance. Across BreakHis, Brain MRI ND5 and FracAtlas, it reaches higher faithfulness (Win %) and lower prediction Drop (%) than Grad-CAM, Grad-CAM++ and LIME in nearly all dataset-architecture combinations, and on FracAtlas its maps correlate spatially with practitioner-annotated fracture regions, with a mean ground-truth active fraction of up to 0.96 for ResNet-18.