Pith. sign in

Paper Citation Record · LEDGER

Revisiting MoE and Dense Speed-Accuracy Comparisons for LLM Training

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2405.15052.

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

pith.paper-citation-record.v1
2405.15052 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T16:26:58.571348Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T02:06:15.294236Z

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 1030b647-c9d1-4893-b1b6-7d7d65700e0a · inbound

Apple Intelligence Foundation Language Models: Tech Report 2025 cites this paper.

Apple Intelligence Foundation Language Models: Tech Report 2025 Revisiting MoE and Dense Speed-Accuracy Comparisons for LLM Training

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-06T16:26:58.571348Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:26:58.571348Z digest=sha256:f74ee182f1666c343588e8c7d07f559299e3eb6c1252587154f6c5e878381a02

Observation cde7d86d-5877-483f-80f7-a268292c726c · 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 Revisiting MoE and Dense Speed-Accuracy Comparisons for LLM Training

Reference 10

Resolution
verified exact
arxiv_id, observed 2026-05-10T03:29:21.485118Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-05-10T03:29:16.555166Z digest=sha256:86ee6a01d48c48104e38a73f19518a1ec9f69bf0a594565d91763ddcd87ab673

Observation 5f91cbc4-dc2c-4fa7-b1a7-448a098d30ca · 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 Revisiting MoE and Dense Speed-Accuracy Comparisons for LLM Training

Reference 10

Resolution
verified exact
arxiv_id, observed 2026-05-12T02:06:15.296559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-05-12T02:03:02.654035Z digest=sha256:17bd88c386a09236769e9aa585f8c2e9cb029edc1bc6a0556f00d458ee0ecf91

Observation e9430e14-a9f1-4186-b8ea-18e2ccd84f87 · inbound

Multi-level context Modeling for consistent expert selection in Mixture-of-Experts cites this paper.

Multi-level context Modeling for consistent expert selection in Mixture-of-Experts Revisiting MoE and Dense Speed-Accuracy Comparisons for LLM Training

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-01T21:01:42.897579Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T21:01:42.897579Z digest=sha256:0f179e91848800ccee8a1c89868ceb772bb2df4f4dce2c0ddaf6592242f7bf6e

Observation 248168a5-3351-464d-97e0-7416d7d609d9 · inbound

SmartMage: Dynamic Modality Orchestration for 3D Scene Understanding cites this paper.

SmartMage: Dynamic Modality Orchestration for 3D Scene Understanding Revisiting MoE and Dense Speed-Accuracy Comparisons for LLM Training

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-06T04:28:21.538572Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T04:28:21.538572Z digest=sha256:1a87b6d79f93bc5f523f1f91d12404553bb0f1a8f79f1b00903354f3e40de9b1