[1/n] Happy to share exciting results, combining a rare mix of theoretical physics tools to answer a fundamental question in temporal information processing: . How do cortical networks operate near criticality with improved working memory without requiring precise parameter fine-tuning? 🧠⚛️

Curious about the scalability of analog VQAs that harness particular quantum phases of matter as an ansatz ⚛️? Check out our new study, led by a talented master’s student Kasidit - nice summary thread below. 👇

🧵 1/4 Ever wondered how tools from statistical physics can help understand learning in diverse reinforcement-learning populations? Check out our new PNAS paper (Special Feature: Collective Artificial Intelligence & Evolutionary Dynamics) here pnas.org/doi/10.1073/... #PNASNews

Check out our latest work spearheaded by a talented master student, Kasidit. See a summary thread below 👇

Wondering whether simply scaling up system sizes and cranking up chaos really supercharges a quantum reservoir, or if dissipation is really a resource at scale? 🌀 We demystify these QRC myths in our new preprint: [https://scirate.com/arxiv/2505.10080] 🧵 Key takeaways in a great thread below!👇

Published in #PNAS 🎉 Noise isn't just disruptive; it can enhance neural computations, especially in working memory tasks! Biologically plausible RNNs harnessing noise also operate near the “edge of chaos,” supporting the critical brain hypothesis 🧠✨ Check out 👇 www.pnas.org/doi/10.1073/...

Hey everyone, just wanted to share our new preprint on crystal structure generation (CrystalGRW) is live on arXiv! We’ve been playing with diffusion-based models that treat crystal structures in their “natural domain” (Riemannian manifolds) (like Torus for coords capturing periodicity) and it works!