Paper Citation Record · LEDGER

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity

As of 20 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 10 inbound Pith citation observations for arXiv:2505.21411.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.21411 v2

Coverage vector

measured 60 of 60 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:35:57.253552Z

measured 70 of 70 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T13:37:01.570731Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-02T01:46:26.849370Z

Reference resolution

60 of 60 outbound references displayed

verified exact1
verified fuzzy19
unresolved39
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b855188c-e03c-4ef2-9090-89f9eaba304d · outbound

This paper cites The llama 4 herd: The beginning of a new era of natively multimodal ai innovation.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity The llama 4 herd: The beginning of a new era of natively multimodal ai innovation

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:36:03.763370Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 20a1e3b2-6c9d-47e2-9744-8dd0fae02106 · outbound

This paper cites Program Synthesis with Large Language Models.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity Program Synthesis with Large Language Models

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T13:35:51.044133Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 803c82a9-0f79-43b2-82a7-1a30b367d0ae · outbound

This paper cites Piqa: Reasoning about physical commonsense in natural language.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity Piqa: Reasoning about physical commonsense in natural language

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:36:03.562289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 05661c6c-6c28-46c0-90ae-1de9e6cfb006 · outbound

This paper cites A Survey on Mixture of Experts in Large Language Models.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity A Survey on Mixture of Experts in Large Language Models

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T13:35:51.300731Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6ba32807-dd8a-49d8-9e1f-fb295dc296aa · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity Evaluating Large Language Models Trained on Code

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T13:35:51.384764Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 24243e89-b590-4561-989a-bb6cf7795e16 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity Training Verifiers to Solve Math Word Problems

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T13:35:51.500132Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4df660e0-9a18-4203-9e78-19b8b0bacf8f · outbound

This paper cites A span-extraction dataset for Chinese machine reading comprehension.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity A span-extraction dataset for Chinese machine reading comprehension

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:36:03.344965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5c3692cb-1394-4079-89d5-a1043a3f174b · outbound

This paper cites Glam: Efficient scaling of language models with mixture-of-experts.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity Glam: Efficient scaling of language models with mixture-of-experts

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T13:35:51.674744Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4898aa2f-192c-459a-8c06-479a06c6b5a4 · outbound

This paper cites Drop: A reading comprehension benchmark requiring discrete reasoning over paragraphs.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity Drop: A reading comprehension benchmark requiring discrete reasoning over paragraphs

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:36:03.086337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 01120521-6f69-499b-bb85-5c6a6cc6dcf8 · outbound

This paper cites Model Tells You What to Discard: Adaptive KV Cache Compression for LLMs.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity Model Tells You What to Discard: Adaptive KV Cache Compression for LLMs

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T13:35:51.893018Z

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Unavailable: canonical work link unavailable.

Observation c49740f1-ddfa-4f92-b5dd-1853c39f8079 · outbound

This paper cites Chatglm: A family of large language models from glm-130b to glm-4 all tools, 2024.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity Chatglm: A family of large language models from glm-130b to glm-4 all tools, 2024

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T13:35:52.064746Z

Source-reported events for the cited work

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Observation 32a66376-808e-40a2-b052-538e309a3dd6 · outbound

This paper cites Measuring Massive Multitask Language Understanding.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity Measuring Massive Multitask Language Understanding

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T13:35:52.155499Z

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Unavailable: canonical work link unavailable.

Observation 26f6141c-8ec3-49eb-b6cc-272231920d21 · outbound

This paper cites Measuring Mathematical Problem Solving With the MATH Dataset.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity Measuring Mathematical Problem Solving With the MATH Dataset

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T13:35:52.239596Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1bcf451e-8314-4a67-951a-44c295da1da4 · outbound

This paper cites C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T13:35:52.311217Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c042ef2-323a-4bd5-aa4c-1c12487095f9 · outbound

This paper cites LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T13:35:52.466639Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 000c857e-ab4c-477e-9df9-7b927b873625 · outbound

This paper cites Mixtral of Experts.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity Mixtral of Experts

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T13:35:52.554005Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d033ed14-cb67-4d2d-ae89-12e0161c61c2 · outbound

This paper cites RACE: Large-scale ReAding Comprehension Dataset From Examinations.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity RACE: Large-scale ReAding Comprehension Dataset From Examinations

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T13:35:52.638310Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 465249dc-4a8a-4f8a-bad4-9eba3a976795 · outbound

This paper cites {GS}hard: Scaling giant models with conditional computation and automatic sharding.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity {GS}hard: Scaling giant models with conditional computation and automatic sharding

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T13:35:52.750396Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 711085a9-581a-4e4d-a1a4-378648a52233 · outbound

This paper cites CMMLU: Measuring massive multitask language understanding in Chinese.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity CMMLU: Measuring massive multitask language understanding in Chinese

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T13:35:52.904766Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c619d4d9-38c0-4d24-b3b9-bf21aabb4f44 · outbound

This paper cites Gonzalez, and Ion Stoica.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity Gonzalez, and Ion Stoica

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T13:35:53.053387Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eef40266-67af-4058-8cb1-d56f9e858a07 · outbound

This paper cites Ccpm: A chinese classical poetry matching dataset, 2021.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity Ccpm: A chinese classical poetry matching dataset, 2021

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:36:02.931095Z

Source-reported events for the cited work

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Observation d45ac6b6-2732-46e0-86bb-3bf85bd9bcc1 · outbound

This paper cites Kvtuner: Sensitivity-aware layer-wise mixed precision kv cache quantization for efficient and nearly lossless llm inference, 2025.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity Kvtuner: Sensitivity-aware layer-wise mixed precision kv cache quantization for efficient and nearly lossless llm inference, 2025

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:36:02.740107Z

Source-reported events for the cited work

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Observation 1b90d2b4-b040-4f0e-8837-1171aab4a4a6 · outbound

This paper cites Davinci: A scalable architecture for neural network computing.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity Davinci: A scalable architecture for neural network computing

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:36:02.438510Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4c6ba6b6-a0ac-4584-91cf-9298b6ab34ed · outbound

This paper cites DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T13:35:53.477835Z

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Unavailable: canonical work link unavailable.

Observation 27798dcc-671e-4c94-8380-f44afbf9d32a · outbound

This paper cites KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T13:35:53.562870Z

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Observation 07dffa8a-f0b5-4cb6-8439-29ae8354267e · outbound

This paper cites Codeforces.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity Codeforces

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T13:35:53.689480Z

Source-reported events for the cited work

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Observation f515775d-e498-4e72-ac21-706508a466d0 · outbound

This paper cites Codeforces.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity Codeforces

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:36:02.238578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b8c44789-3cc0-47f1-9ad6-3af9955c7c49 · outbound

This paper cites Gpqa: A graduate-level google-proof q&a benchmark.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity Gpqa: A graduate-level google-proof q&a benchmark

Reference 28

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Unavailable: canonical work link unavailable.

Observation b8149bf8-bdc2-4232-b1dc-adba50e43d94 · outbound

This paper cites Winogrande: An adversarial winograd schema challenge at scale, 2019.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity Winogrande: An adversarial winograd schema challenge at scale, 2019

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T13:35:54.018931Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c248afc-00e3-45ab-bbc1-d8b945517e90 · outbound

This paper cites Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T13:35:54.126595Z

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Unavailable: canonical work link unavailable.

Observation cc285603-26f3-4e76-825e-7c43a1894820 · outbound

This paper cites Language models are multilingual chain-of-thought reasoners.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity Language models are multilingual chain-of-thought reasoners

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:36:01.998947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6f7cca01-fe35-4e23-95d2-8e6c671e452e · outbound

This paper cites Investigating prior knowledge for challenging chinese machine reading comprehension, 2019.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity Investigating prior knowledge for challenging chinese machine reading comprehension, 2019

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:36:01.643461Z

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Observation fb285300-4e45-4f8d-afce-a2d1df2fa8f0 · outbound

This paper cites Le, Ed H.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity Le, Ed H

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:36:01.406635Z

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Observation 60074898-431a-4f56-a300-27296aa3bbe6 · outbound

This paper cites Pangu ultra moe: How to train your big moe on ascend npus, 2025.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity Pangu ultra moe: How to train your big moe on ascend npus, 2025

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:36:01.188216Z

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Observation 08d160ef-1a29-40e1-96d9-e161378cbc5a · outbound

This paper cites Gemma 3 Technical Report.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity Gemma 3 Technical Report

Reference 35

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Observation 5ef1b1d4-71fb-40ed-9cfe-dfcd6d386960 · outbound

This paper cites Supergpqa: Scaling llm evaluation across 285 graduate disciplines, 2025.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity Supergpqa: Scaling llm evaluation across 285 graduate disciplines, 2025

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:36:01.008729Z

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Observation dd661264-2204-45bd-949a-30a543b610c4 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity LLaMA: Open and Efficient Foundation Language Models

Reference 37

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Observation bc18a172-d754-470f-87cb-b92cb1d6224b · outbound

This paper cites Mmlu-pro: A more robust and challenging multi-task language understanding benchmark.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity Mmlu-pro: A more robust and challenging multi-task language understanding benchmark

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T13:35:54.984910Z

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Observation c98d2a89-2b72-49a6-836f-e37b4a6df0d1 · outbound

This paper cites PanGu-$\pi$: Enhancing Language Model Architectures via Nonlinearity Compensation.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity PanGu-$\pi$: Enhancing Language Model Architectures via Nonlinearity Compensation

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T13:35:55.098832Z

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Unavailable: canonical work link unavailable.

Observation 4abde72f-a772-4efa-8c97-4ae4359240d4 · outbound

This paper cites Cmath: Can your language model pass chinese elementary school math test?, 2023.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity Cmath: Can your language model pass chinese elementary school math test?, 2023

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:36:00.679607Z

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Observation dc76f2d3-cea4-403f-ba56-0dac811f79e4 · outbound

This paper cites Efficient Streaming Language Models with Attention Sinks.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity Efficient Streaming Language Models with Attention Sinks

Reference 41

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Observation 94c05d5a-9d1a-4f1b-8bce-a9a013273df9 · outbound

This paper cites CLUE: A Chinese Language Understanding Evaluation Benchmark.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity CLUE: A Chinese Language Understanding Evaluation Benchmark

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T13:35:55.479079Z

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Unavailable: canonical work link unavailable.

Observation 1a85f877-4bf4-446d-9838-7e91a55719e4 · outbound

This paper cites Qwen3 Technical Report.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity Qwen3 Technical Report

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T13:35:55.550408Z

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Unavailable: canonical work link unavailable.

Observation ebc41cbe-8226-41e9-ba32-7ec28839adf8 · outbound

This paper cites Qwen2.5 Technical Report.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity Qwen2.5 Technical Report

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-07T13:35:55.682799Z

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Observation e8fa8b89-0d9e-48be-89c1-385f96c35fe7 · outbound

This paper cites Attentionpredictor: Temporal pattern matters for efficient llm inference.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity Attentionpredictor: Temporal pattern matters for efficient llm inference

Reference 45

Resolution: verified exact; raw_fallback, observed 2026-08-07T13:35:57.655602Z

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Observation 7d93ee82-3d6f-4c76-bed3-44042e87cd8b · outbound

This paper cites Entropy Law: The Story Behind Data Compression and LLM Performance.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity Entropy Law: The Story Behind Data Compression and LLM Performance

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-07T13:35:55.872646Z

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Unavailable: canonical work link unavailable.

Observation 9d95d959-f21a-4da6-a341-6e665856c1fd · outbound

This paper cites Pangu ultra: Pushing the limits of dense large language models on ascend npus.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity Pangu ultra: Pushing the limits of dense large language models on ascend npus

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:36:00.480023Z

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Observation 4f27fcae-6385-4473-9f66-2f218323e3db · outbound

This paper cites Hellaswag: Can a machine really finish your sentence? InAnnual Meeting of the Association for Computational Linguistics, 2019.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity Hellaswag: Can a machine really finish your sentence? InAnnual Meeting of the Association for Computational Linguistics, 2019

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:36:00.279184Z

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Observation 1709d3b8-26c9-47cf-9ca4-98370c602a73 · outbound

This paper cites Instruction-Following Evaluation for Large Language Models.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity Instruction-Following Evaluation for Large Language Models

Reference 49

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Observation 5076e4ab-f1f4-40e8-8aca-5afaf105708d · outbound

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Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity Unresolved cited work

Reference 50

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:36:00.062569Z

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Observation 94153891-73cd-4b5c-abc5-6dcd770e983c · outbound

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Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity Unresolved cited work

Reference 51

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:35:59.873313Z

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Observation ff6634e4-88b8-455f-ae8f-2246b212e31e · outbound

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Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity Unresolved cited work

Reference 52

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:35:59.582421Z

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Observation f7be79a0-b7e8-4517-93eb-fd51ed3b1c95 · outbound

This paper cites 如果昨天是明天就好了，那么今天就是周五了，请问今天周几？.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity 如果昨天是明天就好了，那么今天就是周五了，请问今天周几？

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:35:59.277506Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d54ffbae-2be4-4f69-83ca-80be5952a5d9 · outbound

This paper cites an unresolved cited work.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity Unresolved cited work

Reference 54

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:35:59.110140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 128a9e12-4515-435b-94f4-73145921dbbe · outbound

This paper cites an unresolved cited work.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity Unresolved cited work

Reference 55

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:35:58.913775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3d796c94-213a-405d-87ef-42796f620934 · outbound

This paper cites 昨天（假设中的）是明天（实际中的）.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity 昨天（假设中的）是明天（实际中的）

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:35:58.769344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 969e8356-9cfc-4f6b-bc7d-f93e9fcc8f0a · outbound

This paper cites 左”为尊贵方位（如 “左丞相.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity 左”为尊贵方位（如 “左丞相

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:35:58.650902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 13005985-dbdf-4573-ada7-21c12e2b617b · outbound

This paper cites an unresolved cited work.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity Unresolved cited work

Reference 58

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:35:58.539328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ebb67527-ed75-4bc0-abc0-86149f2015c6 · outbound

This paper cites an unresolved cited work.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity Unresolved cited work

Reference 59

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:35:58.227678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2ebc6305-a3fa-4426-82df-3a714b160c14 · outbound

This paper cites an unresolved cited work.

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity Unresolved cited work

Reference 60

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:35:58.051076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

Observation 16f35138-4123-4a47-b070-9748ec198a14 · inbound

Intrinsic Fingerprint of LLMs: Continue Training is NOT All You Need to Steal A Model! cites this paper.

Intrinsic Fingerprint of LLMs: Continue Training is NOT All You Need to Steal A Model! Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity

Reference 13

Resolution: metadata mismatch; arxiv_id, observed 2026-05-19T06:42:07.456953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f54dbbfc-b90b-41c9-bd9d-93fb20156c8a · inbound

SMoES: Soft Modality-Guided Expert Specialization in MoE-VLMs cites this paper.

SMoES: Soft Modality-Guided Expert Specialization in MoE-VLMs Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity

Reference 50

Resolution: verified exact; arxiv_id, observed 2026-05-11T21:36:18.740196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4b7eec21-8fd9-449f-907e-3912a7b79853 · inbound

RouteHijack: Routing-Aware Attack on Mixture-of-Experts LLMs cites this paper.

RouteHijack: Routing-Aware Attack on Mixture-of-Experts LLMs Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-11T15:46:17.565308Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6b963ff4-f6f5-4c78-aee5-303e8ec873ff · inbound

Adaptive Inverted-Index Routing for Granular Mixtures-of-Experts cites this paper.

Adaptive Inverted-Index Routing for Granular Mixtures-of-Experts Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-05-09T06:55:43.442366Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a14ea9f3-6f43-4187-a16d-a652e3b72a0f · inbound

Hierarchical Mixture-of-Experts with Two-Stage Optimization cites this paper.

Hierarchical Mixture-of-Experts with Two-Stage Optimization Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-05-12T07:46:26.763167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 96467f3d-5c02-45de-a285-09bf84196d66 · inbound

NASiC: 3D NAND-based CAM-Selected Multibit CIM Architecture for Efficient On-Device Mixture-of-Experts LLM Inference cites this paper.

NASiC: 3D NAND-based CAM-Selected Multibit CIM Architecture for Efficient On-Device Mixture-of-Experts LLM Inference Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-25T03:00:16.049858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d927f8df-9979-4810-9063-888012011b53 · inbound

Complete-muE: Optimal Hyperparameter Transfer and Scaling for MoE Models cites this paper.

Complete-muE: Optimal Hyperparameter Transfer and Scaling for MoE Models Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-25T04:35:20.992810Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation da3223b2-ed6c-42bf-8a09-c80adde7a789 · inbound

Rethinking the Role of Tensor Decompositions in Post-Training LLM Compression cites this paper.

Rethinking the Role of Tensor Decompositions in Post-Training LLM Compression Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-07-02T01:46:26.851154Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 809e000c-94c5-48c5-849d-c86782b06bc2 · inbound

Spectral Signatures of Large Language Models cites this paper.

Spectral Signatures of Large Language Models Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity

Reference 43

Resolution: unresolved; no resolver link, observed 2026-07-12T02:55:58.459452Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ef1004e-7f20-4668-a9f4-7ea44f98a8c8 · inbound

SCOPE and SCION: A Benchmark and an Auditable Reference Pipeline for Schema Induction and Fusion from Text cites this paper.

SCOPE and SCION: A Benchmark and an Auditable Reference Pipeline for Schema Induction and Fusion from Text Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity

Reference 115

Resolution: unresolved; no resolver link, observed 2026-08-02T13:37:01.570731Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.