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Paper Citation Record · LEDGER

CtRL-Sim: Reactive and Controllable Driving Agents with Offline Reinforcement Learning

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2403.19918.

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

pith.paper-citation-record.v1
2403.19918 v3

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 2026-08-09T06:31:02.800959+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:45:49.004620Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T06:26:27.476196Z

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 1664f1b8-9a79-46ab-8d2f-bc27a15d5c26 · inbound

Autoregressive Meta-Actions for Unified Controllable Trajectory Generation cites this paper.

Autoregressive Meta-Actions for Unified Controllable Trajectory Generation CtRL-Sim: Reactive and Controllable Driving Agents with Offline Reinforcement Learning

Reference 2021

Resolution
unresolved
no resolver link, observed 2026-08-07T12:45:49.004620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:45:49.004620Z digest=sha256:0d4b927144e32ae3779a5f27b409c89762bbf0485bcd5b3dcf00d8f9608b5ace

Observation 1b39542c-097d-4ebb-a0b6-c88a55ef9e0e · inbound

Do LLM Modules Generalize? A Study on Motion Generation for Autonomous Driving cites this paper.

Do LLM Modules Generalize? A Study on Motion Generation for Autonomous Driving CtRL-Sim: Reactive and Controllable Driving Agents with Offline Reinforcement Learning

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-05T11:30:39.782378Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T11:30:39.782378Z digest=sha256:f4cc0453cd78fe45f661b1f91b59dcf051742494760c40182dc1fbfbe6059aad

Observation 6c33fcff-3904-4bd4-ac7f-17087b4d6980 · inbound

ScenarioControl: Vision-Language Controllable Vectorized Latent Scenario Generation cites this paper.

ScenarioControl: Vision-Language Controllable Vectorized Latent Scenario Generation CtRL-Sim: Reactive and Controllable Driving Agents with Offline Reinforcement Learning

Reference 39

Resolution
verified exact
arxiv_id, observed 2026-05-10T06:26:27.477670Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-10T06:23:22.058330Z digest=sha256:7ef2290f7e747fef14051f5d6b05391da395458adcabc5e1020768aea31bc550

Observation 784126e3-268d-4345-9833-13bf45830ae6 · inbound

Agent-driven Long-tail Simulation for Autonomous Driving cites this paper.

Agent-driven Long-tail Simulation for Autonomous Driving CtRL-Sim: Reactive and Controllable Driving Agents with Offline Reinforcement Learning

Reference 27

Resolution
unresolved
no resolver link, observed 2026-07-11T20:01:39.628512Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-11T20:01:39.628512Z digest=sha256:96e15fa65fde2f09f30389b94863a9307f721542957c48f7451baa58918b2b59

Observation c0c29fc7-2eb5-4bc7-8b48-2249570384b8 · inbound

End-to-end Conditional Diffusion for Realistic and Controllable Visual Traffic Scenario Generation cites this paper.

End-to-end Conditional Diffusion for Realistic and Controllable Visual Traffic Scenario Generation CtRL-Sim: Reactive and Controllable Driving Agents with Offline Reinforcement Learning

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-01T14:53:33.349941Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T14:53:33.349941Z digest=sha256:23a12e3638408662d6560a7d9a02d44cfc5788e652539ce5956d310e5ac53100