🧵 4/4
To stat mech crowd: think of congestion games as out-of-equilibrium many-body active matter.
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This is an exactly solvable active system (multi-agent RL) where microscopic chaos coexists with macroscopic ergodic convergence - check it out!
www.pnas.org/doi/10.1073/...

🧵 3/4
✨ Remarkably, yet the long-run average number of agents on route 1 settles on the social-optimum / Nash equilibrium (bottom right) ⛳️, despite the day-to-day head-count of route 1 being provably chaotic (bottom left)! 🌪️

🧵 2/4

Results: When some agents learn (adapt) very fast, their individual strategies turn chaotic 🌪️. Top panel - x axis: agent type with different learning rates, y-axis fraction of that agent selecting route 1.

Kudos to the team (especially to Tang and Teerachote) for this computational feat powered by thousands of NVIDIA GPU hours and LOADs of trials and errors! But eventually they succeeded at rivaling state-of-the-art models!

Feedbacks are welcome!

Paper --> arxiv.org/abs/2501.08998

We show that CrystalGRW yields stable, unique, and novel structures (S.U.N. materials) close to their DFT ground states. The fun part for me is to revisit the theory of random walks on Riemannian manifolds and make this works for generative modeling.

If you’re interested in materials discovery or generative modeling, CrystalGRW might cut down the guesswork and skip expensive ab initio calculations and also let you specify, say, a target crystallographic point group or composition right off the bat.

The coolest part (in my humble opinion) is how it balances crystal symmetry requirements, periodicity, compositional constraints, and training stability in a single, unified generative modeling framework: diffusion models on natural Riemannian manifolds that suitably represent crystal properties