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Paper Citation Record · LEDGER

3D-MoE: A Mixture-of-Experts Multi-modal LLM for 3D Vision and Pose Diffusion via Rectified Flow

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2501.16698.

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

pith.paper-citation-record.v1
2501.16698 v1

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 2026-08-08T06:32:00.761636+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:46:15.744253Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-24T01:25:54.590480Z

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 00bb4a71-d884-45ce-9847-ba27458fe156 · inbound

A Survey on Vision-Language-Action Models for Embodied AI cites this paper.

A Survey on Vision-Language-Action Models for Embodied AI 3D-MoE: A Mixture-of-Experts Multi-modal LLM for 3D Vision and Pose Diffusion via Rectified Flow

Reference 134

Resolution
verified exact
arxiv_id, observed 2026-05-24T01:25:54.593437Z

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.

source=pdf_text observed=2026-05-24T01:25:10.150459Z digest=sha256:fbbb7af49352622d2c845edd75b402daa1d4eacb4b532b562fcab325d2fd184b

Observation ec264051-b15b-4672-ad89-4c4289841fd2 · inbound

Uni3D-MoE: Scalable Multimodal 3D Scene Understanding via Mixture of Experts cites this paper.

Uni3D-MoE: Scalable Multimodal 3D Scene Understanding via Mixture of Experts 3D-MoE: A Mixture-of-Experts Multi-modal LLM for 3D Vision and Pose Diffusion via Rectified Flow

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-07T13:46:15.744253Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:46:15.744253Z digest=sha256:39d759a7cc639fc7e889f3c666e8e2b9f95715730e7e58afbae9515e84e75006

Observation 6d0e7634-e488-432d-bffa-9d3ed6b250bd · inbound

CorrMoE: Mixture of Experts with De-stylization Learning for Cross-Scene and Cross-Domain Correspondence Pruning cites this paper.

CorrMoE: Mixture of Experts with De-stylization Learning for Cross-Scene and Cross-Domain Correspondence Pruning 3D-MoE: A Mixture-of-Experts Multi-modal LLM for 3D Vision and Pose Diffusion via Rectified Flow

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-06T17:05:08.865212Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:05:08.865212Z digest=sha256:42a5c8e103c54cf20755d9ec2a0d08d43c27b290cd001dda339bb122a0f37d36

Observation a07b1af1-7cd6-468c-8487-aeaec9e9097c · inbound

DoReMi: Bridging 3D Domains via Topology-Aware Domain-Representation Mixture of Experts cites this paper.

DoReMi: Bridging 3D Domains via Topology-Aware Domain-Representation Mixture of Experts 3D-MoE: A Mixture-of-Experts Multi-modal LLM for 3D Vision and Pose Diffusion via Rectified Flow

Reference 22

Resolution
verified exact
arxiv_id, observed 2026-05-17T22:15:22.084861Z

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.

source=pdf_text observed=2026-05-17T22:12:51.364157Z digest=sha256:deaa0fb44bf784a66316c57818a51e17e31c89aba9c3d31dba65f6f699f39c35