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Paper Citation Record · LEDGER

Bypassing the Simulation-to-reality Gap: Online Reinforcement Learning using a Supervisor

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2209.11082.

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

pith.paper-citation-record.v1
2209.11082 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-12T23:09:48.353960Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T08:11:02.961154Z

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 265f44c0-7338-4a19-98b8-c68b9e22b003 · inbound

New Scheme Adaption Strategy for Hyperbolic Conservation Laws cites this paper.

New Scheme Adaption Strategy for Hyperbolic Conservation Laws Bypassing the Simulation-to-reality Gap: Online Reinforcement Learning using a Supervisor

Reference 8

Resolution
unresolved
no resolver link, observed 2026-07-12T23:09:48.353960Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T23:09:48.353960Z digest=sha256:ed11a8264015280a540c037c8c5e266f3c063874a8e0be73f1203af01e46e185

Observation 67e04f66-3283-45e9-aef0-7643f6348340 · inbound

Physics-Informed Reinforcement Learning of Spatial Density Velocity Potentials for Map-Free Racing cites this paper.

Physics-Informed Reinforcement Learning of Spatial Density Velocity Potentials for Map-Free Racing Bypassing the Simulation-to-reality Gap: Online Reinforcement Learning using a Supervisor

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-05-11T08:11:02.972269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-10T16:47:38.002725Z digest=sha256:9ec883e0f820b608a5198a2d2ed42f30d673b65b21e710f605fb80813aa40f10