Quite happy to say that our paper on representation learning for dynamical systems was accepted to #NeurIPS2026 and I'll be presenting it in Sydney this December! Very succinctly, our work, titled "Sparse Koopman Autoencoders Identify Local Dynamical Regimes in Multibasin Systems", is about learning linear representations of state for dynamical systems with multiple basins of attraction. Inducing sparsity in a latent representation can be a very powerful method to model multiple basins of attraction with linear dynamics. By training a Koopman autoencoder with a bias for producing sparse latent representations, we find that a sparse-latent model both forecasts the dynamics better and has greater ability to identify the current local regime of the current state compared to a model without sparsity-inducing effects. More broadly, I think our work on sparse Koopman autoencoders could be relevant to both the robotics/world modeling and interpretability communities (just brainstorming here). - Robotics: To my knowledge, switching dynamics are common in robotic systems, i.e. trajectories seem to transition between dynamical modes. With a sparse code, you could get a switch between modes for free. - Interpretability: Sparse autoencoders might not be all the rage anymore, but I think Koopman modeling is still very natural for understanding the dynamics of models that apply the same computation at multiple steps to advance the dynamics. This applies to most recurrent models, such as looped/weight-tied transformers (dm me if you want to discuss!) Thanks to my amazing collaborators @ukreddy126 @mahanfathi @apsarathchandar @RGoroshin. I was thinking about what to say about this (clueless grad student tries to figure out social media) and this post ended up being really late lol, my bad. See you in Sydney! Paper: arxiv.org/abs/2608.29057 Code: github.com/aidanmrli/skae