% Key papers for MoE LLM compression (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{iclr2024_pushing_33989,
  title = {{Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction Tuning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19086},
}

@inproceedings{iclr2024_merge_34846,
  title = {{Merge, Then Compress: Demystify Efficient SMoE with Hints from Its Routing Policy}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18228},
}

@inproceedings{icml2025_moe_47814,
  title = {{MoE-SVD: Structured Mixture-of-Experts LLMs Compression via Singular Value Decomposition}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44786},
}

@inproceedings{icml2026_when_49327,
  title = {{When Model Merging Breaks Routing: Training-Free Calibration for MoE}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=1ORZJtadQl},
}

@inproceedings{icml2026_gemq_49752,
  title = {{GEMQ: Global Expert-Level Mixed-Precision Quantization for MoE LLMs}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=5Q4ts0pr9T},
}

@inproceedings{icml2026_eigenvectors_50931,
  title = {{Eigenvectors of Experts are Training-free Non-collapsing Routers}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=H1mxs9qGQX},
}

@inproceedings{icml2026_tileq_51984,
  title = {{TileQ: Efficient Low-Rank Quantization of Mixture-of-Experts with 2D Tiling}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=ReSBUj2514},
}

@inproceedings{icml2026_effective_53184,
  title = {{Effective MoE-based LLM Compression by Exploiting Heterogeneous Inter-Group Experts Routing Frequency and Information Density}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=czym69s5GU},
}

@inproceedings{neurips2025_s_76642,
  title = {{S'MoRE: Structural Mixture of Residual Experts for Parameter-Efficient LLM Fine-tuning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=LbNL8xGai2},
}

@inproceedings{neurips2025_noise_77901,
  title = {{Noise-Robustness Through Noise: A Framework combining Asymmetric LoRA with Poisoning MoE}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=YtwEVr2ONF},
}

@inproceedings{neurips2025_pt_77941,
  title = {{PT-MoE: An Efficient Finetuning Framework for Integrating Mixture-of-Experts into Prompt Tuning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=ZSAWYtIwGg},
}

@inproceedings{neurips2026_from_93210,
  title = {{From Experts to Sub-experts: Fine-grained Parameter-Efficient Fine-Tuning for MoE LLMs}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/155254},
}

@inproceedings{neurips2026_fouriermoe_94121,
  title = {{FourierMoE: Fourier Mixture-of-Experts Adaptation of Large Language Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/151989},
}

@inproceedings{neurips2026_rotmole_94678,
  title = {{RotMoLE: Enhancing Mixture of Low-Rank Experts through Rotational Gating Mechanism}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/148910},
}

@inproceedings{icml2025_delta_49165,
  title = {{Delta Decompression for MoE-based LLMs Compression}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/43461},
}