Paper Citation Record · LEDGER

Solving Token Gradient Conflict in Mixture-of-Experts for Large Vision-Language Model

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2406.19905.

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

pith.paper-citation-record.v1
2406.19905 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 2026-08-10T06:31:04.303077+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T18:56:37.922804Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T12:43:17.287048Z

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 b964b0c4-6a36-4c41-835f-e41b653eac50 · inbound

Mixture of Experts (MoE): A Big Data Perspective cites this paper.

Mixture of Experts (MoE): A Big Data Perspective Solving Token Gradient Conflict in Mixture-of-Experts for Large Vision-Language Model

Reference 194

Resolution: unresolved; no resolver link, observed 2026-08-10T18:56:37.922804Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a26faf70-0d95-4a61-9943-32c52e770472 · inbound

Ensembles of Low-Rank Expert Adapters cites this paper.

Ensembles of Low-Rank Expert Adapters Solving Token Gradient Conflict in Mixture-of-Experts for Large Vision-Language Model

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-09T20:29:52.704791Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 29702931-453e-4f96-8a1a-5fda615e4de5 · inbound

SMoES: Soft Modality-Guided Expert Specialization in MoE-VLMs cites this paper.

SMoES: Soft Modality-Guided Expert Specialization in MoE-VLMs Solving Token Gradient Conflict in Mixture-of-Experts for Large Vision-Language Model

Reference 63

Resolution: verified exact; arxiv_id, observed 2026-05-11T21:41:13.343354Z

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 9a967cfa-a892-4b89-825b-a84c6b90e6a0 · inbound

DyGRO-VLA: Cross-Task Scaling of Vision-Language-Action Models via Dynamic Grouped Residual Optimization cites this paper.

DyGRO-VLA: Cross-Task Scaling of Vision-Language-Action Models via Dynamic Grouped Residual Optimization Solving Token Gradient Conflict in Mixture-of-Experts for Large Vision-Language Model

Reference 111

Resolution: verified exact; arxiv_id, observed 2026-05-20T12:43:17.289431Z

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.