Attention

TL;DR for operators Teams are easy to label. Understanding who actually listens to whom is harder. Hong’s paper on learned coordination conventions proposes a diagnostic for inspecting how cooperative reinforcement-learning agents route information between predefined roles.1 The central move is architectural: place role labels in both the querying agent’s representation and each ally’s representation, then use cross-attention to expose a role-to-role routing matrix. ...

Mind the Model: When Generative AI Teaches Neuroscience New Tricks A model is not a mind. This should not need saying, but then again, neither should “do not use benchmark scores as a personality test,” and here we are. The more useful point is subtler. Modern generative AI does not matter to neuroscience because transformers are secretly brains in a hoodie. It matters because machine learning has turned several once-vague ideas about cognition into working engineering mechanisms. Not perfect mechanisms. Not biological mechanisms by default. But mechanisms clear enough to test, stress, reject, adapt, or steal with appropriate academic manners. ...