Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Learning to Drive from Simulation without Real World Labels
As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1812.03823.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-14T13:55:04.778126Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-05-21T18:24:18.213411Z
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 3f4d7fdc-7105-4e0f-9751-b7000ef20501 · inbound
A review on Deep Reinforcement Learning for Fluid Mechanics cites this paper.
A review on Deep Reinforcement Learning for Fluid Mechanics Learning to Drive from Simulation without Real World Labels
Reference 8
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T13:55:04.778126Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 81d7eeda-2766-4cf7-a78a-599efd4e095c · inbound
State-Conditional Adversarial Learning: An Off-Policy Visual Domain Transfer Method for End-to-End Imitation Learning cites this paper.
State-Conditional Adversarial Learning: An Off-Policy Visual Domain Transfer Method for End-to-End Imitation Learning Learning to Drive from Simulation without Real World Labels
Reference 3
- Resolution
- verified exact
- local_arxiv, observed 2026-05-21T18:24:18.214706Z
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.
Observation c7268206-25b3-440c-bcc0-91b3f4c64c5d · inbound
State-Conditional Adversarial Learning: An Off-Policy Visual Domain Transfer Method for End-to-End Imitation Learning cites this paper.
State-Conditional Adversarial Learning: An Off-Policy Visual Domain Transfer Method for End-to-End Imitation Learning Learning to Drive from Simulation without Real World Labels
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T18:30:47.270923Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.