# Mixture-of-Experts Architectures: Research Digest

> 10 foundational papers with 3,521 combined citations, plus the latest arXiv preprints.

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- **Updated:** 2026-10-16
- **Category:** scholar
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## Key data

- **Field:** mixture of experts architectures
- **Foundational:** 10
- **Recent (arXiv):** 8
- **Updated:** 2026-10-16

10 foundational papers with 3,521 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 survey on mixture of experts in large language models](https://ieeexplore.ieee.org/abstract/document/10937907/)
2. [A comprehensive survey of mixture-of-experts: Algorithms, theory, and applications](https://arxiv.org/abs/2503.07137)
3. [A survey on mixture of experts](https://www.techrxiv.org/doi/abs/10.36227/techrxiv.172055626.64129172)
4. [A survey on inference optimization techniques for mixture of experts models](https://dl.acm.org/doi/abs/10.1145/3794845)
5. [Deepseekmoe: Towards ultimate expert specialization in mixture-of-experts language models](https://aclanthology.org/2024.acl-long.70.pdf)
6. [Mixture of experts in large language models](https://arxiv.org/abs/2507.11181)
7. [The evolution of mixture of experts: A survey from basics to breakthroughs](https://www.preprints.org/frontend/manuscript/d1a82109f0f0d0984a6ae85228ed630e/download_pub)
8. [Towards understanding the mixture-of-experts layer in deep learning](https://proceedings.neurips.cc/paper_files/paper/2022/hash/91edff07232fb1b55a505a9e9f6c0ff3-Abstract-Conference.html)
9. [Pushing mixture of experts to the limit: Extremely parameter efficient moe for instruction tuning](https://proceedings.iclr.cc/paper_files/paper/2024/hash/6d00071564ec447466fc4577743cf1b3-Abstract-Conference.html)
10. [A survey on mixture of experts: Advancements, challenges, and future directions](https://www.techrxiv.org/doi/full/10.36227/techrxiv.173835706.69246194/v1)
11. [When the KV Cache Is Slower Than the GPU: Scaling Remote KV Tiers for Context-Parallel Prefill](https://arxiv.org/abs/2610.21500v1)
12. [DPA-MoE: A Runtime Precision Control Framework for MoE Inference](https://arxiv.org/abs/2610.21378v1)
13. [Task-Phase-Conditioned Expert Routing Is Not a Reliable Proxy for Quantization Sensitivity in MoE FFN Tensors](https://arxiv.org/abs/2610.20826v1)
14. [OrionMoE-v1.0: A Mixture-of-Experts Foundation Model for Tabular In-Context Learning](https://arxiv.org/abs/2610.20418v1)
15. [Cortex: Consolidating Context into Parametric Memory](https://arxiv.org/abs/2610.20399v1)
16. [Beyond Expert Rankings: Contrast Calibration for Mixed-Precision MoE Quantization](https://arxiv.org/abs/2610.20353v1)
17. [Lifted State Policy Optimization: Internalizing Self-Correction through Transition Geometry](https://arxiv.org/abs/2610.20350v1)
18. [Scaling Multilingual Mixture-of-Experts: How Language Coverage Changes Capacity and Compute Allocation](https://arxiv.org/abs/2610.20247v1)

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