Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T13:35:57.253552Z
Paper Citation Record · LEDGER
Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity
As of 9 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.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-02T13:37:01.570731Z
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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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
Source-reported events for the cited work
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
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
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
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
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
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
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 2026-08-09T06:31:02.800959+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
Source-reported events for the cited work
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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Unavailable: canonical work link unavailable.
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 2026-08-09T06:31:02.800959+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
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:35:53.906262Z
Source-reported events for the cited work
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
Source-reported events for the cited work
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 2026-08-09T06:31:02.800959+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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:35:54.574290Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:35:54.902110Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:35:55.317410Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
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
Source-reported events for the cited work
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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
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
Source-reported events for the cited work
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
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
- Resolution
- malformed identifier
- no resolver link, observed 2026-08-07T13:35:56.134395Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5076e4ab-f1f4-40e8-8aca-5afaf105708d · outbound
This paper cites an unresolved cited work.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 94153891-73cd-4b5c-abc5-6dcd770e983c · outbound
This paper cites an unresolved cited work.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation ff6634e4-88b8-455f-ae8f-2246b212e31e · outbound
This paper cites an unresolved cited work.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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.