Paper Citation Record · LEDGER

MoE++: Accelerating Mixture-of-Experts Methods with Zero-Computation Experts

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2410.07348.

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

pith.paper-citation-record.v1
2410.07348 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:23:01.959332Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T15:39:56.545642Z

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 71aaedb7-2d88-46d6-b444-47c72670721f · inbound

I2MoE: Interpretable Multimodal Interaction-aware Mixture-of-Experts cites this paper.

I2MoE: Interpretable Multimodal Interaction-aware Mixture-of-Experts MoE++: Accelerating Mixture-of-Experts Methods with Zero-Computation Experts

Reference 1991

Resolution: unresolved; no resolver link, observed 2026-08-07T14:23:01.959332Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 60b066d4-cd5a-4f24-bb97-1ac7c5b0d370 · inbound

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts cites this paper.

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts MoE++: Accelerating Mixture-of-Experts Methods with Zero-Computation Experts

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-05T21:55:10.476732Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 85905def-4ea5-47ee-a46e-70d9ec76f4c0 · inbound

Breaking the MoE LLM Trilemma: Dynamic Expert Clustering with Structured Compression cites this paper.

Breaking the MoE LLM Trilemma: Dynamic Expert Clustering with Structured Compression MoE++: Accelerating Mixture-of-Experts Methods with Zero-Computation Experts

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-04T14:50:27.505927Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 24186a55-4f05-4d73-90d4-135a963d702b · inbound

DTop-p MoE: Sparsity-Controlled Dynamic Top-p MoE for Foundation Model Pre-training cites this paper.

DTop-p MoE: Sparsity-Controlled Dynamic Top-p MoE for Foundation Model Pre-training MoE++: Accelerating Mixture-of-Experts Methods with Zero-Computation Experts

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-03T16:21:31.465156Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9f44f3c4-82db-47f7-9b8c-1fa6d85b74e3 · inbound

Mixture of Predefined Experts: Maximizing Data Usage on Vertical Federated Learning cites this paper.

Mixture of Predefined Experts: Maximizing Data Usage on Vertical Federated Learning MoE++: Accelerating Mixture-of-Experts Methods with Zero-Computation Experts

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-16T01:57:06.084930Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a7fea64c-5bf2-4891-9d32-8204ab5c53be · inbound

PromptDx: Differentiable Prompt Tuning for Multimodal In-Context Alzheimer's Diagnosis cites this paper.

PromptDx: Differentiable Prompt Tuning for Multimodal In-Context Alzheimer's Diagnosis MoE++: Accelerating Mixture-of-Experts Methods with Zero-Computation Experts

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-12T08:31:24.521393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation af064616-f770-404a-bf6c-f6cbf52f64a2 · inbound

BEAM: Binary Expert Activation Masking for Dynamic Routing in MoE cites this paper.

BEAM: Binary Expert Activation Masking for Dynamic Routing in MoE MoE++: Accelerating Mixture-of-Experts Methods with Zero-Computation Experts

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-15T01:59:38.671477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2a71f0ac-5951-4357-8cb4-d3b8b1a84efd · inbound

Post-Trained MoE Can Skip Half Experts via Self-Distillation cites this paper.

Post-Trained MoE Can Skip Half Experts via Self-Distillation MoE++: Accelerating Mixture-of-Experts Methods with Zero-Computation Experts

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-20T12:03:15.161526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 36ffc0eb-8178-4bbe-b71c-02d2dee019d9 · inbound

Post-Trained MoE Can Skip Half Experts via Self-Distillation cites this paper.

Post-Trained MoE Can Skip Half Experts via Self-Distillation MoE++: Accelerating Mixture-of-Experts Methods with Zero-Computation Experts

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-06-30T18:25:00.030441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0425ad2c-0526-4edb-9692-949509b196cb · inbound

GeMoE: Gating Entropy is All You Need for Uncertainty-aware Adaptive Routing in MoE-based Large Vision-Language Models cites this paper.

GeMoE: Gating Entropy is All You Need for Uncertainty-aware Adaptive Routing in MoE-based Large Vision-Language Models MoE++: Accelerating Mixture-of-Experts Methods with Zero-Computation Experts

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-07-04T15:39:56.547923Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f64a9024-9b81-4c66-a70c-9ad90887a5ed · inbound

MEPA: Multi-Scale Representation Alignment for Visual Autoregressive Modeling with Mixture of Experts cites this paper.

MEPA: Multi-Scale Representation Alignment for Visual Autoregressive Modeling with Mixture of Experts MoE++: Accelerating Mixture-of-Experts Methods with Zero-Computation Experts

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-07-02T15:17:07.214080Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.