Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Combining RL and IL using a dynamic, performance-based modulation over learning signals and its application to local planning
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2405.09760.
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 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-03T05:43:22.549083Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-15T11:59:59.571308Z
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 8cb4232b-bf5d-4e45-b960-8f5ee97c5a13 · inbound
Sem-NaVAE: Semantically-Guided Outdoor Mapless Navigation via Generative Trajectory Priors cites this paper.
Sem-NaVAE: Semantically-Guided Outdoor Mapless Navigation via Generative Trajectory Priors Combining RL and IL using a dynamic, performance-based modulation over learning signals and its application to local planning
Reference 40
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T05:43:22.549083Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ed589956-b097-4391-8dad-73b97b863ebd · inbound
Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving cites this paper.
Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving Combining RL and IL using a dynamic, performance-based modulation over learning signals and its application to local planning
Reference 26
- Resolution
- verified exact
- arxiv_id, observed 2026-05-15T11:59:59.573331Z
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.