Paper Citation Record · LEDGER

Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 23 inbound Pith citation observations for arXiv:2212.05055.

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

pith.paper-citation-record.v1
2212.05055 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 23 of 23 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T11:20:23.627982Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
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malformed identifier0
metadata mismatch0

External citation measurements

12
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 67431689-0637-42ec-a05c-6925b5ded578 · inbound

GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints cites this paper.

GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-05-11T06:53:59.369167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation df4dd422-f982-4b2e-a421-ca6db158304c · inbound

MoE-LLaVA: Mixture of Experts for Large Vision-Language Models cites this paper.

MoE-LLaVA: Mixture of Experts for Large Vision-Language Models Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints

Reference 15

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T02:33:30.309336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 56704cea-9f6c-4252-81d6-88872d84893d · inbound

MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies cites this paper.

MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-13T18:00:53.537329Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c041c756-97d8-414c-af0e-3c4532ca826f · inbound

A Survey on Efficient Inference for Large Language Models cites this paper.

A Survey on Efficient Inference for Large Language Models Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints

Reference 91

Resolution: verified exact; arxiv_id, observed 2026-05-15T02:39:33.232205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6f0a874f-b08c-46e0-93b3-a2d5562c126d · inbound

Analytical FFN-to-MoE Restructuring via Activation Pattern Analysis cites this paper.

Analytical FFN-to-MoE Restructuring via Activation Pattern Analysis Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-23T04:12:31.096197Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 04f50ded-00a7-460e-935f-a8e016755d0e · inbound

Training Sparse Mixture Of Experts Text Embedding Models cites this paper.

Training Sparse Mixture Of Experts Text Embedding Models Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-08T11:20:23.627982Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation add9a803-4985-4aa2-9871-2832581282d2 · inbound

Mixture-of-Experts Can Surpass Dense LLMs Under Strictly Equal Resource cites this paper.

Mixture-of-Experts Can Surpass Dense LLMs Under Strictly Equal Resource Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-22T00:05:47.760189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 277012d9-716a-4bd7-abd1-de2f81775792 · inbound

SpikingBrain: Spiking Brain-inspired Large Models cites this paper.

SpikingBrain: Spiking Brain-inspired Large Models Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-18T18:51:45.676873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 243a998b-784f-4ebc-ae15-bca7d1e43c11 · inbound

Expert Upcycling: Shifting the Compute-Efficient Frontier of Mixture-of-Experts cites this paper.

Expert Upcycling: Shifting the Compute-Efficient Frontier of Mixture-of-Experts Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-10T03:29:21.454800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 14ed15c8-2a8e-4c1f-944b-0fa2b58d5e0f · inbound

Expert Upcycling: Shifting the Compute-Efficient Frontier of Mixture-of-Experts cites this paper.

Expert Upcycling: Shifting the Compute-Efficient Frontier of Mixture-of-Experts Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:06:15.330760Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2cdf9ed4-ec0b-4c69-aeb3-2717b7a78f4c · inbound

HodgeCover: Higher-Order Topological Coverage Drives Compression of Sparse Mixture-of-Experts cites this paper.

HodgeCover: Higher-Order Topological Coverage Drives Compression of Sparse Mixture-of-Experts Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-15T05:55:04.804235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 411bc5a4-1c53-437d-a038-161510294170 · inbound

Dense2MoE: Pushing the Pareto Frontier of On-Device LLMs via Unified Pruning and Upcycling cites this paper.

Dense2MoE: Pushing the Pareto Frontier of On-Device LLMs via Unified Pruning and Upcycling Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-06-29T19:43:55.024776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9b7f1f9f-bc4c-4b2f-94ae-612a300fab00 · inbound

Hyperbolic and Evidence-Prioritized Experts for Large Vision-Language Models cites this paper.

Hyperbolic and Evidence-Prioritized Experts for Large Vision-Language Models Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-07-01T19:26:00.040544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7b902ecc-0357-479a-8112-0b8d402e17ee · inbound

Reversible Foundations: Training a 120B Sparse MoE through State-Preserving Scaling cites this paper.

Reversible Foundations: Training a 120B Sparse MoE through State-Preserving Scaling Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints

Reference 49

Resolution: verified exact; arxiv_id, observed 2026-07-02T16:07:09.420797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a217183e-a6fe-4e15-8af5-82bc0caf9123 · inbound

Personalization Meets Safety:Mechanisms,Risks,and Mitigations in Personalized LLMs cites this paper.

Personalization Meets Safety:Mechanisms,Risks,and Mitigations in Personalized LLMs Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints

Reference 197

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T01:07:30.270932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 165e8b90-51e0-4b77-b0be-2e47efa18e5a · inbound

PADD: Path-Aligned Decompression Distillation for Non-Router Teacher to Guide MoE Student Learning cites this paper.

PADD: Path-Aligned Decompression Distillation for Non-Router Teacher to Guide MoE Student Learning Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints

Reference 18

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T05:27:39.751050Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation fec9ca77-1ce8-4142-b12f-91919d556491 · inbound

Behavioral and Representational Evidence of Binomial Ordering Preferences in Large Language Models cites this paper.

Behavioral and Representational Evidence of Binomial Ordering Preferences in Large Language Models Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints

Reference 9

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T06:39:37.844241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e307e6d8-b21f-4c43-8e96-2d76a4f0c5ad · inbound

LLM Evolution as an Industry-Scale Ecosystem: A Lifecycle Perspective on Continual Learning cites this paper.

LLM Evolution as an Industry-Scale Ecosystem: A Lifecycle Perspective on Continual Learning Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints

Reference 54

Resolution: verified exact; arxiv_id, observed 2026-07-03T16:48:39.909349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f82ce639-7bc7-41db-af5d-7f85c2da1d68 · 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 Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints

Reference 32

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T15:39:56.508142Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation bd9b0fee-c622-4e64-b79e-09f147b71b1a · inbound

Rosetta: Composable Native Multimodal Pretraining cites this paper.

Rosetta: Composable Native Multimodal Pretraining Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-07-02T15:47:05.759103Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9f0979a5-30ab-4193-a1fd-ce42ab786fdc · inbound

ACE-Brain-0.5: A Unified Embodied Foundational Model for Physical Agentic AI cites this paper.

ACE-Brain-0.5: A Unified Embodied Foundational Model for Physical Agentic AI Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints

Reference 126

Resolution: unresolved; no resolver link, observed 2026-07-11T19:16:57.396710Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b7d7e243-73ff-4194-a052-a263e3d8d0a1 · inbound

MM-ShiftKV: Decode-Aware Prefill-Stage KV Selection for Multimodal Large Language Models cites this paper.

MM-ShiftKV: Decode-Aware Prefill-Stage KV Selection for Multimodal Large Language Models Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-02T11:58:30.699815Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8df7d279-6abf-4491-a22b-8809a8729a3d · inbound

SpecPrefetch: Parameter-Efficient Expert Prefetching for Sparse MoE Foundation Models cites this paper.

SpecPrefetch: Parameter-Efficient Expert Prefetching for Sparse MoE Foundation Models Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-02T10:18:55.487992Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.