Paper Citation Record · LEDGER

DynaMo: In-Domain Dynamics Pretraining for Visuo-Motor Control

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2409.12192.

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

pith.paper-citation-record.v1
2409.12192 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 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 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T19:18:44.910833Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T12:36:57.220166Z

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 3b030eb7-2d58-47a4-a126-9588f4679459 · inbound

Latent Action Learning Requires Supervision in the Presence of Distractors cites this paper.

Latent Action Learning Requires Supervision in the Presence of Distractors DynaMo: In-Domain Dynamics Pretraining for Visuo-Motor Control

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-09T19:18:44.910833Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e9a90be-b504-4c6a-b4b2-0f351790537f · inbound

UniLACT: Depth-Aware RGB Latent Action Learning for Vision-Language-Action Models cites this paper.

UniLACT: Depth-Aware RGB Latent Action Learning for Vision-Language-Action Models DynaMo: In-Domain Dynamics Pretraining for Visuo-Motor Control

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-15T20:20:17.647274Z

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 acecdf49-faa0-43a5-bff0-03ede5a11b78 · inbound

Why Latent Actions Fail, and How to Prevent It cites this paper.

Why Latent Actions Fail, and How to Prevent It DynaMo: In-Domain Dynamics Pretraining for Visuo-Motor Control

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-21T08:34:05.326386Z

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 398059aa-8ea1-4eed-aa47-375cec6dcb88 · inbound

CLAW: Learning Continuous Latent Action World Models via Adversarial Latent Regularization cites this paper.

CLAW: Learning Continuous Latent Action World Models via Adversarial Latent Regularization DynaMo: In-Domain Dynamics Pretraining for Visuo-Motor Control

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-07-02T03:36:29.611082Z

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 36162ab0-8a56-4bf3-8c08-c1985a5f5688 · inbound

HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning cites this paper.

HyperVis: Continuous Latent Visual Relational Graphs on the Lorentz Hyperboloid for Compositional Reasoning DynaMo: In-Domain Dynamics Pretraining for Visuo-Motor Control

Reference 20

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T12:36:57.221781Z

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 80919815-5340-42ba-b48d-721840aebdbf · inbound

Patch Policy: Efficient Embodied Control via Dense Visual Representations cites this paper.

Patch Policy: Efficient Embodied Control via Dense Visual Representations DynaMo: In-Domain Dynamics Pretraining for Visuo-Motor Control

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-01T15:40:51.382895Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.