Differentiable Filtering for Learning Hidden Markov Models

ArXi:2511.10571v2 Announce Type: replace Hidden Marko Models (HMMs) are fundamental for modeling sequential data, yet learning their parameters from observations remains challenging. Classical methods like the Baum-Welch algorithm are computationally intensive and prone to local optima, while modern spectral algorithms offer provable guarantees but may produce probability outputs outside valid ranges. This work