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Paper Citation Record · LEDGER

Routing Experts: Learning to Route Dynamic Experts in Multi-modal Large Language Models

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2407.14093.

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

pith.paper-citation-record.v1
2407.14093 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:33:20.319719Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T12:36:57.356695Z

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 d772d0ca-3939-40d3-a7f7-1b8394918ccd · inbound

Skip a Layer or Loop it? Test-Time Depth Adaptation of Pretrained LLMs cites this paper.

Skip a Layer or Loop it? Test-Time Depth Adaptation of Pretrained LLMs Routing Experts: Learning to Route Dynamic Experts in Multi-modal Large Language Models

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-06T18:33:20.319719Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:33:20.319719Z digest=sha256:56ee6fa2ff78213283bc3a1219d819af1d83db7421b1e717d7f8521d5cdedc87

Observation af9ad45a-d066-41fd-8e0f-5f5083bb7efb · inbound

CoGR-MoE: Concept-Guided Expert Routing with Consistent Selection and Flexible Reasoning for Visual Question Answering cites this paper.

CoGR-MoE: Concept-Guided Expert Routing with Consistent Selection and Flexible Reasoning for Visual Question Answering Routing Experts: Learning to Route Dynamic Experts in Multi-modal Large Language Models

Reference 35

Resolution
verified exact
arxiv_id, observed 2026-05-10T07:11:53.252508Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-10T07:09:48.239662Z digest=sha256:3180b71f5c857cbc020fb81afdefc466319c8b454c5191afcc2e787f54ce0578

Observation 7afca9cf-1c55-48cf-b367-9de6783b392e · inbound

AlignCultura: Towards Culturally Aligned Large Language Models? cites this paper.

AlignCultura: Towards Culturally Aligned Large Language Models? Routing Experts: Learning to Route Dynamic Experts in Multi-modal Large Language Models

Reference 9

Resolution
verified exact
arxiv_id, observed 2026-05-11T12:56:04.912799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-10T02:36:36.854805Z digest=sha256:dc2435cf028ef67aae2ceda5e38af3fe628c7aab4d4a62ff169e317bd83f373e

Observation e6ae106e-6105-4167-9c58-76fb67702380 · inbound

Skip a Layer or Loop It? Learning Program-of-Layers in LLMs cites this paper.

Skip a Layer or Loop It? Learning Program-of-Layers in LLMs Routing Experts: Learning to Route Dynamic Experts in Multi-modal Large Language Models

Reference 13

Resolution
verified exact
arxiv_id, observed 2026-07-02T12:36:57.358290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-28T01:56:34.435152Z digest=sha256:1efdf53e9ffc51117332900d160d8c63d99b5f2587536e2c59283de870482ed4