Preprint Alert 🚀
Multi-agent reinforcement learning (MARL) often assumes that agents know when other agents cooperate with them. But for humans, this isn’t always the case. For example, plains indigenous groups used to leave resources for others to use at effigies called Manitokan.
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Step 1: Understand how scaling improves LLMs.
Step 2: Directly target underlying mechanism.
Step 3: Improve LLMs independent of scale. Profit.
In our ACL 2025 paper we look at Step 1 in terms of training dynamics.
Project: mirandrom.github.io/zsl
Paper: arxiv.org/pdf/2506.05447
(1/n)🚨Train a model solving DFT for any geometry with almost no training data
Introducing Self-Refining Training for Amortized DFT: a variational method that predicts ground-state solutions across geometries and generates its own training data!
📜 arxiv.org/abs/2506.01225
💻 github.com/majhas/self-...
I'm very excited to announce the publication of our new book Neural Interfaces, published by Elsevier. The book is a comprehensive resource for all those interested and gravitating around neural interfaces and brain-computer interfaces (BCIs).
shop.elsevier.com/books/neural...
Andrei Mircea
Dane Carnegie Malenfant
Neural Interfaces is a comprehensive book on the foundations, major breakthroughs, and most promising future developments of neural interfaces. The bo
Excited to share that POSSM has been accepted to #NeurIPS2025! See you in San Diego 🏖️
🚨 New preprint alert!
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We propose a theory of how learning curriculum affects generalization through neural population dimensionality. Learning curriculum is a determining factor of neural dimensionality - where you start from determines where you end up.
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A 🧵:
tinyurl.com/yr8tawj3
Davide Valeriani, PhD 🧠+💪+❤️+😴+👨💻
tinyurl.com
Generalization of visual perceptual learning (VPL) to unseen conditions varies across tasks. Previous work suggests that training curriculum may be integral to generalization, yet a theoretical explan...
New preprint! 🧠🤖
How do we build neural decoders that are:
⚡️ fast enough for real-time use
🎯 accurate across diverse tasks
🌍 generalizable to new sessions, subjects, and even species?
We present POSSM, a hybrid SSM architecture that optimizes for all three of these axes!
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