arXiv:2604.07349v5 Announce Type: replace-cross
Abstract: Any rigorously specified problem determines an admissible-output relation $R$, and exact correctness depends only on the induced classes $s sim_R s’ iff mathrmAdm_R(s)=mathrmAdm_R(s’)$. Exact relevance certification asks which coordinates recover those classes. Decision, search, approximation, statistical, randomized, horizon, and distributional guarantees all reduce to this same quotient-recovery problem. Tractable cases still admit a finite primitive basis, but optimizer-quotient realizability is maximal, so quotient shape alone cannot mark the frontier.
For frontier theorems, orbit gaps are the exact obstruction. Exact classification by closure-law-invariant predicates succeeds exactly when the target is constant on closure orbits; on a closure-closed domain, this is equivalent to disjointness of the positive and negative orbit hulls, and when it holds there is a least exact closure-invariant classifier. Across four natural candidate tractability predicates, a uniform pair-targeted affine witness produces same-orbit disagreements and rules out exact structural classification on the full binary pairwise domain. Because the canonical optimizer-set exact specifications of that witness class are already rigorously specified problems, no universal exact-certification characterization over formal problems escapes the same obstruction; this is by internal witness class, not by asserting that every problem is binary pairwise. Restricting the domain helps only by removing orbit gaps. Approximation also has a strict limit: without explicit gap control, arbitrarily small perturbations can flip relevance and sufficiency.
A Systematic Review and Taxonomy of Reinforcement Learning-Model Predictive Control Integration for Linear Systems
arXiv:2604.21030v1 Announce Type: cross Abstract: The integration of Model Predictive Control (MPC) and Reinforcement Learning (RL) has emerged as a promising paradigm for constrained decision-making