Over minutes, mimicry looks convincing. But over hours, days, or weeks, acting agentically means deciding what to do next, why, and how to carry those lessons forward. For that, semantic–associative learning is required. Procedural memory alone isn’t enough.

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Are top-down feedback connections enough for robust vision? We found ConvRNN with top-down feedback exhibiting OOD robustness only when trained with dropout, revealing a dual mechanism for robust sensory coding with @marco-d.bsky.social, Karl Friston, Giovanni Pezzulo & @siegellab.bsky.social 🧵👇

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🚀 DinoV3 just became the new go-to backbone for geoloc! It outperforms CLIP-like models (SigLip2, finetuned StreetCLIP)… and that’s shocking 🤯 Why? CLIP models have an innate advantage — they literally learn place names + images. DinoV3 doesn’t.

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Did you know that chemosensitive motile E. coli can efficiently explore nontrivial mazes in times much shorter than a no-memory (Markovian) walk would predict, and can collectively escape from a fractal topology? Now you do. Amazing! #ComplexSystems 🦠🧪🌐💡 journals.aps.org/prx/abstract...

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Meet the speakers🎤 Gerhard Neumann (alr.iar.kit.edu/21_65.php) is a full professor at the KIT, where he is Chair of Autonomous Learning Robots. His research explores the intersection of machine learning, robotics, and human-robot interaction.

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