Preprint Open access
PRAXIS: Learning Dynamics of Self-Improving Models with Symbolic Archives
Self-improving learning systems adapt data selection, optimization, and auxiliary symbolic components, inducing nonstationary objectives outside standard learning assumptions. We introduce \textsc{PRAXIS}, a co-evolutionary framework that models generators, learners, and symbolic archives as interacting dynamical proce …