Paper Citation Record · LEDGER

Upcycling Large Language Models into Mixture of Experts

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 inbound Pith citation observations for arXiv:2410.07524.

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

pith.paper-citation-record.v1
2410.07524 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 13 of 13 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T10:21:00.725253Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T16:07:09.402995Z

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 08b928ef-fcaa-4d83-8a1b-653c58669297 · inbound

Scaling Laws for Upcycling Mixture-of-Experts Language Models cites this paper.

Scaling Laws for Upcycling Mixture-of-Experts Language Models Upcycling Large Language Models into Mixture of Experts

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-09T10:21:00.725253Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 429f91ef-ec6b-419b-85c8-820860df9c47 · inbound

Scaling Fine-Grained MoE Beyond 50B Parameters: Empirical Evaluation and Practical Insights cites this paper.

Scaling Fine-Grained MoE Beyond 50B Parameters: Empirical Evaluation and Practical Insights Upcycling Large Language Models into Mixture of Experts

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T11:18:58.896820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ca0b421a-e734-4678-a90d-d4c2e5a5216b · 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 Upcycling Large Language Models into Mixture of Experts

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-22T00:05:47.640835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f319c1af-7c8e-430a-851d-df31903dabd4 · inbound

Automatic Expert Discovery in LLM Upcycling via Sparse Interpolated Mixture-of-Experts cites this paper.

Automatic Expert Discovery in LLM Upcycling via Sparse Interpolated Mixture-of-Experts Upcycling Large Language Models into Mixture of Experts

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T00:56:26.187530Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 25135784-4b59-4a67-972e-feebfa6fbd7c · inbound

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

SpikingBrain: Spiking Brain-inspired Large Models Upcycling Large Language Models into Mixture of Experts

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-18T18:51:45.742897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 948fbb49-4471-4e45-9fa2-e49c6de2f255 · inbound

Beyond Sunk Costs: Boosting LLM Pre-training Efficiency via Orthogonal Growth of Mixture-of-Experts cites this paper.

Beyond Sunk Costs: Boosting LLM Pre-training Efficiency via Orthogonal Growth of Mixture-of-Experts Upcycling Large Language Models into Mixture of Experts

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-21T20:40:36.469330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a7a11777-3a83-4b70-9ae5-76f91184af70 · inbound

ScatterPrism: convergence for generative simulation and inverse problems in particle and nuclear physics cites this paper.

ScatterPrism: convergence for generative simulation and inverse problems in particle and nuclear physics Upcycling Large Language Models into Mixture of Experts

Reference 19

Resolution: unresolved; no resolver link, observed 2026-07-13T14:28:05.261852Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 10e95024-95ad-42d7-80fa-9e0311fbef25 · inbound

ScatterPrism: convergence for generative simulation and inverse problems in particle and nuclear physics cites this paper.

ScatterPrism: convergence for generative simulation and inverse problems in particle and nuclear physics Upcycling Large Language Models into Mixture of Experts

Reference 19

Resolution: unresolved; no resolver link, observed 2026-07-15T11:44:19.622453Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 233874d5-5b0d-4533-9c9a-2a547347cac2 · 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 Upcycling Large Language Models into Mixture of Experts

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-10T03:29:21.490090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2369a1ef-cacc-4879-b676-cfe75af1b087 · 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 Upcycling Large Language Models into Mixture of Experts

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:06:15.333831Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6eaade6c-b6fc-4c73-a5c1-80b6ce4ccb6d · inbound

Tackling Multimodal Learning Challenges with Mixture-of-Expert: A Survey cites this paper.

Tackling Multimodal Learning Challenges with Mixture-of-Expert: A Survey Upcycling Large Language Models into Mixture of Experts

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-06-30T15:34:48.302417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 27f8962d-9ae7-4be6-8894-b4aba9fbcf7e · 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 Upcycling Large Language Models into Mixture of Experts

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-07-02T16:07:09.404976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e16c331f-e8ef-4bce-afb7-c56184c45523 · inbound

MoLGE: Mixture of Language Group Experts for Efficient Scaling of Massively Multilingual Speech Recognition cites this paper.

MoLGE: Mixture of Language Group Experts for Efficient Scaling of Massively Multilingual Speech Recognition Upcycling Large Language Models into Mixture of Experts

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-07-31T23:18:46.956823Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.