# Mixture-of-Experts Architectures: Research Digest

> 10 foundational papers with 8,768 combined citations, plus the latest arXiv preprints.

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- **Updated:** 2026-10-30
- **Category:** scholar
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## Key data

- **Field:** mixture of experts architectures
- **Foundational:** 10
- **Recent (arXiv):** 8
- **Updated:** 2026-10-30

10 foundational papers with 8,768 combined citations, plus the latest arXiv preprints.

_…full analysis and the complete dataset are available to agents for $0.02 — fetch `/scholar/mixture-of-experts-architectures-research-digest.data.json` (HTTP 402)._

## Sources

1. [A comprehensive survey of mixture-of-experts: Algorithms, theory, and applications](https://arxiv.org/abs/2503.07137)
2. [Outrageously large neural networks: The sparsely-gated mixture-of-experts layer](https://arxiv.org/abs/1701.06538)
3. [Towards understanding the mixture-of-experts layer in deep learning](https://proceedings.neurips.cc/paper_files/paper/2022/hash/91edff07232fb1b55a505a9e9f6c0ff3-Abstract-Conference.html)
4. [A survey of mixture of experts models: Architectures and applications in business and finance](https://philpapers.org/rec/JOSASO-5)
5. [The evolution of mixture-of-experts architectures in large language models: Routing, topology, load balancing, and expert parallelism](https://arxiv.org/abs/2608.08650)
6. [More: 3d visual geometry reconstruction meets mixture-of-experts](https://openaccess.thecvf.com/content/CVPR2026/html/Gao_MoRE_3D_Visual_Geometry_Reconstruction_Meets_Mixture-of-Experts_CVPR_2026_paper.html)
7. [Towards greater leverage: Scaling laws for efficient mixture-of-experts language models](https://proceedings.iclr.cc/paper_files/paper/2026/hash/32b640528f5b67975562210f00c131ed-Abstract-Conference.html)
8. [X-moe: Enabling scalable training for emerging mixture-of-experts architectures on hpc platforms](https://dl.acm.org/doi/abs/10.1145/3712285.3759886)
9. [Infrastructure Economics of Sparse Mixture-of-Experts in Cloud-Native NLP: Benchmarking Cost, Accuracy, and Performance](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5355861)
10. [Holistic Scaling Laws for Optimal Mixture-of-Experts Architecture Optimization](https://arxiv.org/abs/2603.21862)
11. [CRISP: Co-designing Pruning and Scheduling for Efficient MoE Inference on Edge Servers](https://arxiv.org/abs/2610.37442v1)
12. [Channel Selection for Mixture-of-Experts Decoding Needs No Calibration Data](https://arxiv.org/abs/2610.37386v1)
13. [Agent-Conditioned Routing: Diverse Reasoning Paths Within One Frozen Model](https://arxiv.org/abs/2610.37375v1)
14. [GradCO: Behavior-Aware Gradient-Conflict Offloading for Text-Based Person Anomaly Search](https://arxiv.org/abs/2610.37267v1)
15. [EnergyCal: Taming Loss Spikes in Muon-Family Optimizers for Sparse MoE Training](https://arxiv.org/abs/2610.37062v1)
16. [SpecLST: Spectral-Enhanced Long-Short Term Spatio-Temporal Multimodal Modeling for Traffic Forecasting](https://arxiv.org/abs/2610.36393v1)
17. [Physical structure as an organizing principle for universal Hamiltonian learning](https://arxiv.org/abs/2610.36135v1)
18. [DuplexTune: Coupled-State Replay for Memory-Efficient Mixture-of-Experts Adaptation](https://arxiv.org/abs/2610.36009v1)

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