# Expert routing strategies Area: [Mixture-of-experts](https://mlsanity.com/moe/index.md). HTML: https://mlsanity.com/moe/expert-routing-strategies/ ## Key facts - Expert routing strategies: 323 papers on arXiv since 2019 (data through 14 Oct 2026). - 192 of its 323 mapped papers (59%) cite “Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity” (2021, arXiv 2101.03961). - 88 of its 323 mapped papers (27%) cite “Mixture-of-Experts with Expert Choice Routing” (2022, arXiv 2202.09368). - 32 new papers 17 Sep–14 Oct. ## Most cited here - [Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity](https://mlsanity.com/paper/2101.03961/) (2021), cited by 192 papers here - [Mixture-of-Experts with Expert Choice Routing](https://mlsanity.com/paper/2202.09368/) (2022), cited by 88 papers here - [Auxiliary-Loss-Free Load Balancing Strategy for Mixture-of-Experts](https://mlsanity.com/paper/2408.15664/) (2024), cited by 36 papers here - [On the Representation Collapse of Sparse Mixture of Experts](https://mlsanity.com/paper/2204.09179/) (2022), cited by 33 papers here - [StableMoE: Stable Routing Strategy for Mixture of Experts](https://mlsanity.com/paper/2204.08396/) (2022), cited by 22 papers here - [Taming Sparsely Activated Transformer with Stochastic Experts](https://mlsanity.com/paper/2110.04260/) (2021), cited by 19 papers here - [ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing](https://mlsanity.com/paper/2412.14711/) (2024), cited by 18 papers here - [Harder Tasks Need More Experts: Dynamic Routing in MoE Models](https://mlsanity.com/paper/2403.07652/) (2024), cited by 14 papers here - [SAFEx: Analyzing Vulnerabilities of MoE-Based LLMs via Stable Safety-critical Expert Identification](https://mlsanity.com/paper/2506.17368/) (2025), cited by 11 papers here - [Steering MoE LLMs via Expert (De)Activation](https://mlsanity.com/paper/2509.09660/) (2025), cited by 11 papers here ## New (17 Sep–14 Oct) - [Task-Phase-Conditioned Expert Routing Is Not a Reliable Proxy for Quantization Sensitivity in MoE FFN Tensors](https://mlsanity.com/paper/2610.20826/) (2026-10-14) - [Shaping Latent Mixture-of-Experts: From Expert Geometry to Layerwise Capacity Allocation](https://mlsanity.com/paper/2610.14644/) (2026-10-10) - [DuoRate: Two-Rate Per-Expert Precision Allocation for Mixture-of-Experts Serving](https://mlsanity.com/paper/2610.13328/) (2026-10-09) - [RouterInterp: Understanding Superposed Specialisation in Mixture of Experts Routing](https://mlsanity.com/paper/2610.11775/) (2026-10-08) - [DivMoE: Fine-Grained MoE Upcycling via Cross-Domain Expert Composition](https://mlsanity.com/paper/2610.11317/) (2026-10-08) - [When Routing Reveals Membership: Privacy Leakage from MoE Router Telemetry](https://mlsanity.com/paper/2610.10616/) (2026-10-07) - [MASKerade: Token-Routed Mask Experts for Dense-to-MoE Upcycling](https://mlsanity.com/paper/2610.07809/) (2026-10-06) - [Reading, Not Manipulating: Leveraging Router Logits for Multimodal Safety in MoE Vision-Language Models](https://mlsanity.com/paper/2610.07774/) (2026-10-06) - [Memory-Efficient Expert Routing for Distributed MoE Training](https://mlsanity.com/paper/2610.07333/) (2026-10-05) - [Distributionally Robust Mixture-of-Experts Training](https://mlsanity.com/paper/2610.07207/) (2026-10-05) - [BRANCH-MoE: Balance-Aware Tree Routing for Large Embedding Models](https://mlsanity.com/paper/2610.06725/) (2026-10-05) - [How Sparse Probability Maps Shape Mixture-of-Experts Routing](https://mlsanity.com/paper/2610.06677/) (2026-10-05) - [Frequency Is Not Sensitivity Identifying Safety-Sensitive Experts in Sparse MoE LLM](https://mlsanity.com/paper/2610.02910/) (2026-10-02) - [Conditional Capacity and Routing in Mixture-of-Experts Particle Transformers](https://mlsanity.com/paper/2610.02701/) (2026-10-02) - [Dense Mixture-of-Experts as a Reparameterized Wide FFN: A Granularity Sweep at Fixed Compute](https://mlsanity.com/paper/2610.02584/) (2026-10-01) - [SpikeMoE: Brain-Inspired Competitive Routing for Flexible Spiking Mixture-of-Experts](https://mlsanity.com/paper/2610.01418/) (2026-10-01) - [Looping Beyond Twice: A Scalable Recipe for Looped Mixture-of-Experts](https://mlsanity.com/paper/2610.01153/) (2026-10-01) - [Score the Update, Not the Token: Descent-Aligned Routing for Combinatorial LoRA Experts](https://mlsanity.com/paper/2610.00493/) (2026-09-30) - [MoRA: MoE Pruning via Router Bias Learning and Expert Approximation](https://mlsanity.com/paper/2610.00367/) (2026-09-30) - [RAZOR: Pruning Replaceable Experts in LLMs](https://mlsanity.com/paper/2609.30465/) (2026-09-30) - [Cross-Entropy Guided Routing in Mixture-of-Experts Large Language Models](https://mlsanity.com/paper/2609.37751/) (2026-09-29) - [Routing in Gradient Space: Balanced Usage Is Not Expert Specialization](https://mlsanity.com/paper/2609.36724/) (2026-09-29) - [MoRE: Scaling mixture of experts with hardware-aware low-rank routing](https://mlsanity.com/paper/2609.36301/) (2026-09-28) - [How to Loop MoE: Flatten the Experts, Untie the Attention](https://mlsanity.com/paper/2609.35751/) (2026-09-28) - [A Persistent State for Auditable Mixture-of-Experts Routing](https://mlsanity.com/paper/2609.34634/) (2026-09-28) - [X-MoD: Practical Scaling Laws for Sparse-Depth Routing Beyond Mixture-of-Depths](https://mlsanity.com/paper/2609.34212/) (2026-09-28) - [EAT: Expert Account Tracker for Efficient MoE Inference](https://mlsanity.com/paper/2609.33614/) (2026-09-27) - [Routing Drift Alone Does Not Diagnose Failure in Merged MoE LLMs](https://mlsanity.com/paper/2609.32821/) (2026-09-26) - [HERO-MoE: Historical Expert Routing with Scale-Preserving Fusion](https://mlsanity.com/paper/2609.32581/) (2026-09-26) - [Function Over Form: Distributional Orthogonalization in Mixture-of-Experts with Replica Expert Mechanism](https://mlsanity.com/paper/2609.32398/) (2026-09-26)