Paper Citation Record · LEDGER

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity

As of 5 August 2026, this Paper Citation Record lists 0 of 0 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 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+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

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

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 2026-08-05T06:32:48.257954+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 2026-08-05T06:32:48.257954+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 2026-08-05T06:32:48.257954+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 2026-08-05T06:32:48.257954+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 2026-08-05T06:32:48.257954+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 2026-08-05T06:32:48.257954+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 2026-08-05T06:32:48.257954+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 2026-08-05T06:32:48.257954+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.