Paper Citation Record · LEDGER

The Unsurprising Effectiveness of Pre-Trained Vision Models for Control

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2203.03580.

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

pith.paper-citation-record.v1
2203.03580 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 2026-08-16T06:30:59.297886+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:03:27.190857Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T12:17:01.387957Z

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 b607f72b-a709-47a7-9c6a-9d81ed421a3b · inbound

A Generalist Agent cites this paper.

A Generalist Agent The Unsurprising Effectiveness of Pre-Trained Vision Models for Control

Reference 45

Resolution: verified exact; arxiv_id, observed 2026-05-13T06:24:49.944744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 320bc10d-7a75-4db9-b770-0fb5562cfa94 · inbound

VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training cites this paper.

VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training The Unsurprising Effectiveness of Pre-Trained Vision Models for Control

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-15T04:42:52.778281Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9b659480-c75f-44e1-92b2-175ace08e773 · inbound

Gen2Act: Human Video Generation in Novel Scenarios enables Generalizable Robot Manipulation cites this paper.

Gen2Act: Human Video Generation in Novel Scenarios enables Generalizable Robot Manipulation The Unsurprising Effectiveness of Pre-Trained Vision Models for Control

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-15T12:17:01.390293Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 88c85fd3-c597-4645-a8dd-606a012cd029 · inbound

Object-Centric Representations Improve Policy Generalization in Robot Manipulation cites this paper.

Object-Centric Representations Improve Policy Generalization in Robot Manipulation The Unsurprising Effectiveness of Pre-Trained Vision Models for Control

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T21:03:27.190857Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f28eb48-413b-4598-b594-f5f2ea9d33a4 · inbound

Spotlighting Task-Relevant Features: Object-Centric Representations for Better Generalization in Robotic Manipulation cites this paper.

Spotlighting Task-Relevant Features: Object-Centric Representations for Better Generalization in Robotic Manipulation The Unsurprising Effectiveness of Pre-Trained Vision Models for Control

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-03T07:04:44.950597Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3055674c-0e53-47c6-8ad4-cc498d4dd86f · inbound

Weights or Skills? A Survey of Robot-Learning Techniques: from Action-Predicting Weights to Robots that Write their Own Skills cites this paper.

Weights or Skills? A Survey of Robot-Learning Techniques: from Action-Predicting Weights to Robots that Write their Own Skills The Unsurprising Effectiveness of Pre-Trained Vision Models for Control

Reference 203

Resolution: unresolved; no resolver link, observed 2026-08-04T19:45:35.117701Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.