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Paper Citation Record · LEDGER

Keypoints into the Future: Self-Supervised Correspondence in Model-Based Reinforcement Learning

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2009.05085.

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

pith.paper-citation-record.v1
2009.05085 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 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 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:58:46.127638Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T22:13:59.837793Z

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 ca4e5e31-80da-4f51-a6af-829770ab8383 · inbound

ReKep: Spatio-Temporal Reasoning of Relational Keypoint Constraints for Robotic Manipulation cites this paper.

ReKep: Spatio-Temporal Reasoning of Relational Keypoint Constraints for Robotic Manipulation Keypoints into the Future: Self-Supervised Correspondence in Model-Based Reinforcement Learning

Reference 47

Resolution
verified exact
arxiv_id, observed 2026-05-16T08:25:18.004867Z

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=2026-05-16T08:25:17.847571Z digest=sha256:4504f9a336a8573405cae5000d4e5c4756d16e393e34788b040d629387fd80ee

Observation 62845579-1b71-4cbd-93f7-5ba61f1acf45 · inbound

UAD: Unsupervised Affordance Distillation for Generalization in Robotic Manipulation cites this paper.

UAD: Unsupervised Affordance Distillation for Generalization in Robotic Manipulation Keypoints into the Future: Self-Supervised Correspondence in Model-Based Reinforcement Learning

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-07T04:58:46.127638Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:58:46.127638Z digest=sha256:aafa1d7beb4ce5223fdbfafc90bf387b63156bb84bd2c268e82b819e799959c0

Observation 06953f0c-1aeb-41ad-a154-2c2e310e8590 · inbound

V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning cites this paper.

V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning Keypoints into the Future: Self-Supervised Correspondence in Model-Based Reinforcement Learning

Reference 41

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T00:33:51.120897Z

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=2026-05-11T00:33:50.471804Z digest=sha256:878a6877e25ec5d0639e0a67dcc4a5b657e0a856ba9cc4bd8235280248fea667

Observation 0c1603e1-3ea5-4ead-b761-20e81323856a · inbound

Constraint-Preserving Data Generation for Visuomotor Policy Learning cites this paper.

Constraint-Preserving Data Generation for Visuomotor Policy Learning Keypoints into the Future: Self-Supervised Correspondence in Model-Based Reinforcement Learning

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-06T01:05:51.400119Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T01:05:51.400119Z digest=sha256:b50dd524f2e5b0d75c0220bf4a581b324b635f99954b98d01202eabaf79be3ab

Observation 4531aadc-617f-4dbb-9325-2205648d5426 · inbound

Parallel Differentiable Reachability for Learning and Planning with Certified Neural Dynamics and Controllers cites this paper.

Parallel Differentiable Reachability for Learning and Planning with Certified Neural Dynamics and Controllers Keypoints into the Future: Self-Supervised Correspondence in Model-Based Reinforcement Learning

Reference 41

Resolution
metadata mismatch
arxiv_id, observed 2026-06-29T22:13:59.839502Z

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=2026-06-29T22:10:50.600849Z digest=sha256:bd174c562e3b06534c3577e271e0b897f718a2f30850ad8e3cee8d1394bdb0fd