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Paper Citation Record · LEDGER

ReVLA: Reverting Visual Domain Limitation of Robotic Foundation Models

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2409.15250.

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

pith.paper-citation-record.v1
2409.15250 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T23:01:42.766719Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T11:18:03.378290Z

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 21ea6122-de84-480b-bbef-7a3df3b139b2 · inbound

Generative Emergent Communication: Large Language Model is a Collective World Model cites this paper.

Generative Emergent Communication: Large Language Model is a Collective World Model ReVLA: Reverting Visual Domain Limitation of Robotic Foundation Models

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-10T23:01:42.766719Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T23:01:42.766719Z digest=sha256:611f0d1da3280f84e4d5def5d1780f18d87e9ae3457201e5739b9a956112eb4d

Observation 1500b8f9-1374-4269-98c9-6bd200cab139 · inbound

SPEAR-1: Scaling Beyond Robot Demonstrations via 3D Understanding cites this paper.

SPEAR-1: Scaling Beyond Robot Demonstrations via 3D Understanding ReVLA: Reverting Visual Domain Limitation of Robotic Foundation Models

Reference 10

Resolution
verified exact
arxiv_id, observed 2026-05-17T20:22:04.593277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-17T20:21:12.375936Z digest=sha256:a997b3f17005a3c97038d541a59e6b7cb37588cd32b25b38db3781673064e17f

Observation 684c729a-edfc-4fbb-bbd3-a7e97714a1cd · inbound

Safe-Night VLA: Seeing the Unseen via Thermal-Perceptive Vision-Language-Action Models for Safety-Critical Manipulation cites this paper.

Safe-Night VLA: Seeing the Unseen via Thermal-Perceptive Vision-Language-Action Models for Safety-Critical Manipulation ReVLA: Reverting Visual Domain Limitation of Robotic Foundation Models

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-02T18:44:44.202726Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T18:44:44.202726Z digest=sha256:d87402effc2734378deb16977eb2ef17d5ef6d1c7824bbb3735ff8e022aa10ca

Observation 188e879f-53e1-421a-ba3a-3ee124aa92cf · inbound

AR-VLA: True Autoregressive Action Expert for Vision-Language-Action Models cites this paper.

AR-VLA: True Autoregressive Action Expert for Vision-Language-Action Models ReVLA: Reverting Visual Domain Limitation of Robotic Foundation Models

Reference 11

Resolution
verified exact
arxiv_id, observed 2026-05-15T12:50:37.164197Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-15T12:50:02.670780Z digest=sha256:5aa55451f933f9711f5840f5fe8dbb6979ddabb8342c4e88c4e044497be93124

Observation 6ac53b75-ae09-4210-910a-542fa16b2d87 · inbound

VLA-GSE: Boosting Parameter-Efficient Fine-Tuning in VLA with Generalized and Specialized Experts cites this paper.

VLA-GSE: Boosting Parameter-Efficient Fine-Tuning in VLA with Generalized and Specialized Experts ReVLA: Reverting Visual Domain Limitation of Robotic Foundation Models

Reference 56

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T20:26:10.346003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-05-08T09:11:21.715023Z digest=sha256:b52a3fe34d756d01fb0a02caf1c0a2a19752befd123115fc465f060047aa1cc0

Observation efbdb5b4-7b52-47b2-a260-236ef9541c4e · inbound

VLA-GSE: Boosting Parameter-Efficient Fine-Tuning in VLA with Generalized and Specialized Experts cites this paper.

VLA-GSE: Boosting Parameter-Efficient Fine-Tuning in VLA with Generalized and Specialized Experts ReVLA: Reverting Visual Domain Limitation of Robotic Foundation Models

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-05-11T04:45:59.368359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-11T01:05:07.509291Z digest=sha256:36f20dead3596756fdd67d5b4f84558a551ae94f3b44d12d90083c5347b0bfde

Observation 3d94d2ed-2fa2-4fd3-aafb-a9685a87bd23 · inbound

World Pilot: Steering Vision-Language-Action Models with World-Action Priors cites this paper.

World Pilot: Steering Vision-Language-Action Models with World-Action Priors ReVLA: Reverting Visual Domain Limitation of Robotic Foundation Models

Reference 45

Resolution
verified exact
arxiv_id, observed 2026-07-03T11:18:03.379744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-06-27T09:40:02.137152Z digest=sha256:8f609efd37679ccac9979eb514ff6ffe4a463d400deb9e4a17ddc904b9e1d1c0