Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
CryptoMoE: Privacy-Preserving and Scalable Mixture of Experts Inference via Balanced Expert Routing
Authors: Yifan Zhou, Tianshi Xu, Jue Hong, Ye Wu, Meng Li
NeurIPS 2025 | Venue PDF | LLM Run Details | Input Tokens: 25,460 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,522 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on Deep Seek Mo E-16.4B, OLMo E-6.9B, and QWen Mo E-14.3B show that Crypto Mo E achieves 2.8 3.5 end-to-end latency reduction and 2.9 4.3 communication reduction over a dense baseline with minimum accuracy loss. |
| Researcher Affiliation | Academia | Yifan Zhou Peking University Tianshi Xu Peking University Jue Hong Independent Researcher Ye Wu Independent Researcher Meng Li* Peking University *Corresponding author: EMAIL |
| Pseudocode | Yes | Algorithm 1: Secure Dispatch Protocol Πdispatch Algorithm 2: Secure Combine ProtocolΠcombine |
| Open Source Code | Yes | Code is available at: https://github.com/PKU-SEC-Lab/Crypto Mo E. |
| Open Datasets | Yes | All the models are evaluated on eight famous zero-shot common sense reasoning tasks, including SIQA [13], OBQA [17], Bool Q [11], ARC-easy, ARC-challenge [16], Hella Swag [14], PIQA [12], and Wino Grande [15]. |
| Dataset Splits | No | All the models are evaluated on eight famous zero-shot common sense reasoning tasks, including SIQA [13], OBQA [17], Bool Q [11], ARC-easy, ARC-challenge [16], Hella Swag [14], PIQA [12], and Wino Grande [15]. |
| Hardware Specification | Yes | All the experiments are performed on a machine with an Intel Xeon Platinum 8468 CPU (48 cores and 2.1GHz). We consider two network environments: 1) LAN setting with 3Gbps bandwidth and 0.2ms latency; 2) WAN setting with 400Mbps bandwidth and 40ms latency. |
| Software Dependencies | No | We implement Crypto Mo E upon the Secret Flow-SPU framework [47], which is a popular framework for privacy-preserving deep learning. |
| Experiment Setup | Yes | We benchmark the accuracy, end-to-end amortized latency, and communication cost of different methods in Table 3, using a batch size of 16 and Crypto Mo Et=2.0. ... The token count t assigned to each expert plays a critical role in balancing accuracy and efficiency. ... We consider two network environments: 1) LAN setting with 3Gbps bandwidth and 0.2ms latency; 2) WAN setting with 400Mbps bandwidth and 40ms latency. |