---
title: "Who cites Pashmina Cameron — 216 papers of our library"
description: "216 papers of the Elamur Research library cite the works of Pashmina Cameron, found in their bibliographies; the most cited first."
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---
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# Who cites Pashmina Cameron

216 citations of their works from 216 papers of our library (of the 149,439 whose bibliographies we read) · 32 with a page here, the most cited first · [← Pashmina Cameron](https://md.research.elamur.ai/free/en/authors/pashmina-cameron/)

1. [**QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving MLSys 2025**Yujun Lin, Haotian Tang, Shang Yang, et al.*2024 · 120 citations*](https://md.research.elamur.ai/free/en/papers/lin-2024-qserve-w4a8kv4-quantization-system-co/)
2. [**A Survey on Efficient Inference for Large Language Models arXiv:2404.14294**Zixuan Zhou, Xuefei Ning, Ke Hong, et al.*2024 · 113 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2024-survey-efficient-inference-large-language/)
3. [**GLM-5: from Vibe Coding to Agentic Engineering arXiv:2602.15763**Aohan Zeng, Xin Lv, Zhenyu Hou, et al.*2026 · 95 citations*](https://md.research.elamur.ai/free/en/papers/team-2026-5-glm-5-vibe-coding/)
4. **[EfficientQAT: Efficient Quantization-Aware Training for Large Language Models](https://md.research.elamur.ai/free/en/papers/chen-2025-efficientqat-efficient-quantization-aware-training/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mengzhao Chen, Wenqi Shao, Peng Xu, et al.*2025 · 82 citations*
5. [**Rethinking On-Policy Distillation of Large Language Models: Phenomenology, Mechanism, and Recipe arXiv:2604.13016**Yaxuan Li, Yuxin Zuo, Bingxiang He, et al.*2026 · 58 citations*](https://md.research.elamur.ai/free/en/papers/li-2026-rethinking-policy-distillation-large-language/)
6. [**A Survey of Resource-efficient LLM and Multimodal Foundation Models arXiv:2401.08092**Mengwei Xu, Wangsong Yin, Dongqi Cai, et al.*2024 · 54 citations*](https://md.research.elamur.ai/free/en/papers/xu-2024-survey-resource-efficient-llm-multimodal/)
7. [**From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models arXiv:2406.16838**Sean Welleck, Amanda Bertsch, Matthew Finlayson, et al.*2024 · 51 citations*](https://md.research.elamur.ai/free/en/papers/welleck-2024-decoding-meta-generation-inference-time/)
8. [**A Survey of On-Policy Distillation for Large Language Models arXiv:2604.00626**Mingyang Song, Mao Zheng*2026 · 41 citations*](https://md.research.elamur.ai/free/en/papers/song-2026-survey-policy-distillation-large-language/)
9. [**Revisiting On-Policy Distillation: Empirical Failure Modes and Simple Fixes arXiv:2603.25562**Yuqian Fu, Haohuan Huang, Kaiwen Jiang, et al.*2026 · 31 citations*](https://md.research.elamur.ai/free/en/papers/fu-2026-revisiting-policy-distillation-empirical-failure/)
10. [**MiniCPM4: Ultra-Efficient LLMs on End Devices arXiv:2506.07900**MiniCPM Team, Chaojun Xiao, Yuxuan Li, et al.*2025 · 22 citations*](https://md.research.elamur.ai/free/en/papers/minicpm-team-2025-minicpm4-ultra-efficient-llms/)
11. **[“Give Me BF16 or Give Me Death”? Accuracy-Performance Trade-Offs in LLM Quantization](https://md.research.elamur.ai/free/en/papers/kurtic-2025-give-me-bf16-or-give/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Eldar Kurtic, Alexandre Noll Marques, Shubhra Pandit, et al.*2025 · 21 citations*
12. [**Baichuan-M2: Scaling Medical Capability with Large Verifier System arXiv:2509.02208**Chengfeng Dou, Chong Liu, Fan Yang, et al.*2025 · 19 citations*](https://md.research.elamur.ai/free/en/papers/team-2025-m2-baichuan-m2-scaling-medical/)
13. [**EXP-Bench: Can AI Conduct AI Research Experiments? arXiv:2505.24785**Patrick Tser Jern Kon, Jiachen Liu, Xinyi Zhu, et al.*2025 · 17 citations*](https://md.research.elamur.ai/free/en/papers/kon-2025-exp-bench-ai-conduct/)
14. **[Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2026-locate-steer-improve-practical-survey/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hengyuan Zhang, Zhihao Zhang, Ercong Nie, et al.*2026 · 9 citations*
15. [**KL for a KL: On-Policy Distillation with Control Variate Baseline arXiv:2605.07865**Minjae Oh, Sangjun Song, Gyubin Choi, et al.*2026 · 8 citations*](https://md.research.elamur.ai/free/en/papers/oh-2026-kl-kl-policy-distillation-control/)
16. [**When Are Teacher Tokens Reliable? Position-Weighted On-Policy Self-Distillation for Reasoning arXiv:2605.21606**Xiaogeng Liu, Xinyan Wang, Yingzi Ma, et al.*2026 · 8 citations*](https://md.research.elamur.ai/free/en/papers/liu-2026-teacher-tokens-reliable-position-weighted/)
17. [**Uni-OPD: Unifying On-Policy Distillation with a Dual-Perspective Recipe arXiv:2605.03677**Wenjin Hou, Shangpin Peng, Weinong Wang, et al.*2026 · 6 citations*](https://md.research.elamur.ai/free/en/papers/hou-2026-uni-opd-unifying-policy-distillation/)
18. **[ARCQuant: Boosting NVFP4 Quantization with Augmented Residual Channels for LLMs](https://md.research.elamur.ai/free/en/papers/meng-2026-arcquant-boosting-nvfp4-quantization-augmented/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Haoqian Meng, Yilun Luo, Yafei Zhao, et al.*2026 · 5 citations*
19. **[Exploring Layer-wise Information Effectiveness for Post-Training Quantization in Small Language Models](https://md.research.elamur.ai/free/en/papers/xiao-2026-exploring-layer-wise-information-effectiveness/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**He Xiao, Qingyao Yang, Dirui Xie, et al.*2026 · 5 citations*
20. **[MixKVQ: Query-Aware Mixed-Precision KV Cache Quantization for Long-Context Reasoning](https://md.research.elamur.ai/free/en/papers/zhang-2026-mixkvq-query-aware-mixed-precision/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tao Zhang, Ziqian Zeng, Hao Peng, et al.*2026 · 5 citations*
21. **[Balancing Fidelity and Plasticity: Aligning Mixed-Precision Fine-Tuning with Linguistic Hierarchies](https://md.research.elamur.ai/free/en/papers/zhou-2026-balancing-fidelity-plasticity-aligning-mixed/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Changhai Zhou, Shiyang Zhang, Yuhua Zhou, et al.*2026 · 3 citations*
22. **[Bit-by-Bit: Progressive QAT Strategy with Outlier Channel Splitting for Stable Low-Bit LLMs](https://md.research.elamur.ai/free/en/papers/xu-2026-bit-bit-progressive-qat-strategy/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Binxing Xu, Hao Gu, Lujun Li, et al.*2026 · 3 citations*
23. **[SharVeT: Similarity-aware Parameter Sharing with Vector-based Tuning for Efficient LLM Compression](https://md.research.elamur.ai/free/en/papers/yun-2026-sharvet-similarity-aware-parameter-sharing/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jeongin Yun, Jaeri Lee, Jongjin Kim, et al.*2026 · 1 citation*
24. **[Unleashing Low-Bit Inference on Ascend NPUs: A Comprehensive Evaluation of HiFloat Formats](https://md.research.elamur.ai/free/en/papers/zhao-2026-unleashing-low-bit-inference-ascend/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Pengxiang Zhao, Hui-Ling Zhen, Xing Li, et al.*2026 · 1 citation*
25. **[ACBQ: Adaptive Cross-Block Quantization of Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2026-acbq-adaptive-cross-block-quantization/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hailing Wang, Jianglin Lu, Yitian Zhang, et al.*2026*
26. **[Benchmarking Post-Training Quantization of Large Language Models under Microscaling Floating Point Formats](https://md.research.elamur.ai/free/en/papers/zhang-2026-benchmarking-post-training-quantization-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Manyi Zhang, Ji-Fu Li, Zhongao Sun, et al.*2026*
27. **[BWLA: Breaking the Barrier of W1AX Post-Training Quantization for LLMs](https://md.research.elamur.ai/free/en/papers/zhao-2026-bwla-breaking-barrier-w1ax-post/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhixiong Zhao, Zukang Xu, Dawei Yang*2026*
28. **[Layer-Wise High-Impact Parameter Ratio Optimization in Post-Training Quantization for Large Language Models](https://md.research.elamur.ai/free/en/papers/pham-2026-layer-wise-high-impact-parameter/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Cuong Pham, Anh Dung Hoang, Cuong C. Nguyen, et al.*2026*
29. **[LBLLM: Lightweight Binarization of Large Language Models via Three-Stage Distillation](https://md.research.elamur.ai/free/en/papers/song-2026-lbllm-lightweight-binarization-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Siqing Song, Chuang Wang, Yong Lang, et al.*2026*
30. **[PE-QAT: Parameter-Efficient Quantization-Aware Training for Large Language Models](https://md.research.elamur.ai/free/en/papers/mishra-2026-pe-qat-parameter-efficient-quantization/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shresth Mishra*2026*
31. **[pQuant: Towards Effective Low-Bit Language Models via Decoupled Linear Quantization-Aware Training](https://md.research.elamur.ai/free/en/papers/zhang-2026-pquant-effective-low-bit-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Wenzheng Zhang, Bingzheng Liu*2026*
32. **[VecInfer: Efficient LLM Inference with Low-Bit KV Cache via Outlier-Suppressed Vector Quantization](https://md.research.elamur.ai/free/en/papers/yao-2026-vecinfer-efficient-llm-inference-low/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Dingyu Yao, Chenxu Yang, Zhengyang Tong, et al.*2026*

184 more citing papers of our library have no page here yet.

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