PRAXIS: Learning Dynamics of Self-Improving Models with Symbolic Archives
DGX agentarXiv:2610.11803v1 Announce Type: new Abstract: Self-improving learning systems adapt data selection, optimization, and auxiliary symbolic components, inducing nonstationary objectives outside standar