---
title: "Who cites Demons in the Detail: On — 12 citing papers"
description: "12 papers of the Elamur Research library cite «Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized…"
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---
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# Who cites «Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models»

12 papers of our library cite it (of the 149,439 whose bibliographies we read) · 4 with a page here, the most cited first · [← back to «Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models»](https://md.research.elamur.ai/free/en/papers/qiu-2025-demons-detail-implementing-load-balancing/)

1. [**Qwen3 Technical Report arXiv:2505.09388**An Yang, Anfeng Li, Baosong Yang, et al.*2025 · 3,382 citations*](https://md.research.elamur.ai/free/en/papers/yang-2025-qwen3-technical-report/)
2. [**Pangu Ultra MoE: How to Train Your Big MoE on Ascend NPUs arXiv:2505.04519**Yehui Tang, Yichun Yin, Yaoyuan Wang, et al.*2025 · 8 citations*](https://md.research.elamur.ai/free/en/papers/tang-2025-pangu-ultra-moe-train/)
3. **[Seeing but Not Thinking: Routing Distraction in Multimodal Mixture-of-Experts](https://md.research.elamur.ai/free/en/papers/xu-2026-seeing-but-not-thinking-routing/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Haolei Xu, Haiwen Hong, Hongxing Li, et al.*2026 · 1 citation*
4. **[Profiling-Free Mixed-Precision Quantization for MoE LLMs via Fuzzy Rule Interpolation](https://md.research.elamur.ai/free/en/papers/qi-2026-profiling-free-mixed-precision-quantization/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Huachen Qi, Ruiyu Zhuo, Bowen Shi, et al.*2026*

8 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/qiu-2025-demons-detail-implementing-load-balancing/) — Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models