arXiv 2607.08601's proposed MAESTRO pushes MoE expert pruning from "local scoring" to "global Markov-chain stationary distribution". Each token only activates a few parameters, but all expert weights must reside in memory — the "memory tax" of deploying MoE large models has long been criticized. MAESTRO (Markov-chain Approximated Expert Sparsification via Transition-based ROuting) doesn't go for new architectures, but focuses on the most basic engineering question: which experts are worth keeping? The authors model the autoregressive expert activation trajectory as an ergodic Markov chain, letting the stationary distribution encode cross-layer dependencies as a global importance heuristic. Common layer-wise scoring methods only look at which experts the token likes in the current layer; MAESTRO is concerned with the long-term "conversation pattern" between experts: who gets called repeatedly, who is just a passing visitor, and the stationary probability lays it all out. Under 50% strict compression, average performance retention across five domains (Safety, Bias, Ethics, etc.) outperforms the SOTA baseline by up to 10.61 percentage points, and cross-task variance also drops significantly. In other words, what's pruned isn't just the low-scoring experts, but the ones that "break routing consistency" — traditional heuristics easily misjudge the "occasionally called backup expert" as redundant, leaving behind truly interfering "routing-noise nodes". What's most interesting isn't the 50% compression itself, but the stability on the robustness dimension. MoE pruning's biggest fear is "capability pruned together, bias quietly remains" — what traditional layer-wise scoring can't do, MAESTRO's global-pruning heuristic given by stationary distribution provides an empirical answer. For all the teams still struggling with "how to actually deploy 200B+ MoE", this is a clear demonstration of upgrading "pruning" from empirical craft to mathematically-grounded method.