AI RESEARCH
Dual Filter: A Transformer-like Inference Architecture for Hidden Markov Models
arXiv CS.LG
•
ArXi:2505.00818v2 Announce Type: replace This paper presents a mathematical framework for causal nonlinear prediction in settings where observations are generated from an underlying hidden Marko model (HMM). Both the problem formulation and the proposed solution are motivated by the decoder-only transformer architecture, in which a finite sequence of observations (tokens) is mapped to the conditional probability of the next token. Our objective is not to construct a mathematical model of a transformer.