Paper Citation Record · LEDGER

Taming Sparsely Activated Transformer with Stochastic Experts

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2110.04260.

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

pith.paper-citation-record.v1
2110.04260 v3

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 2026-08-18T06:34:40.430872+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:40:01.551591Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T15:17:07.182288Z

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 202a855d-5903-44e1-bd12-8d1df84630b8 · inbound

DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models cites this paper.

DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models Taming Sparsely Activated Transformer with Stochastic Experts

Reference 167

Resolution: verified exact; arxiv_id, observed 2026-05-13T01:07:22.292061Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 52602f3f-2653-4d8f-b858-a60f96c09ede · inbound

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models cites this paper.

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models Taming Sparsely Activated Transformer with Stochastic Experts

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-12T20:10:00.260529Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1b998076-e7ee-4d05-b0e6-200ffa91e923 · inbound

Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation cites this paper.

Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation Taming Sparsely Activated Transformer with Stochastic Experts

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-12T14:24:00.396667Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f3528265-2cb6-43d4-8e75-46fc1d5bf4d8 · inbound

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing cites this paper.

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing Taming Sparsely Activated Transformer with Stochastic Experts

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-11T12:03:43.387092Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b59191aa-fddc-4ba4-b15e-aea3f8979729 · inbound

Transforming Vision Transformer: Towards Efficient Multi-Task Asynchronous Learning cites this paper.

Transforming Vision Transformer: Towards Efficient Multi-Task Asynchronous Learning Taming Sparsely Activated Transformer with Stochastic Experts

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-10T20:54:41.906572Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 82837663-8add-443f-a6c0-3baf4e2fdac8 · inbound

DOCS: Quantifying Weight Similarity for Deeper Insights into Large Language Models cites this paper.

DOCS: Quantifying Weight Similarity for Deeper Insights into Large Language Models Taming Sparsely Activated Transformer with Stochastic Experts

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-10T11:47:24.923354Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aee6c30a-1aff-4e7b-9b8a-135de7eab909 · inbound

You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models cites this paper.

You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models Taming Sparsely Activated Transformer with Stochastic Experts

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-16T12:40:01.551591Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4cbcd22f-5233-4ee4-ac32-627f4265ded2 · inbound

QoS-Efficient Serving of Multiple Mixture-of-Expert LLMs Using Partial Runtime Reconfiguration cites this paper.

QoS-Efficient Serving of Multiple Mixture-of-Expert LLMs Using Partial Runtime Reconfiguration Taming Sparsely Activated Transformer with Stochastic Experts

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:12.253030Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a8b4d82a-8da6-4d42-ba48-29c7ac74bfcf · inbound

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation cites this paper.

Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation Taming Sparsely Activated Transformer with Stochastic Experts

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-15T18:44:43.912081Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 543a92b1-cf1d-4faf-8ffe-6e152c431736 · 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 Taming Sparsely Activated Transformer with Stochastic Experts

Reference 66

Resolution: verified exact; arxiv_id, observed 2026-07-02T15:17:07.183793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.