Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Reward-Consistent Dynamics Models are Strongly Generalizable for Offline Reinforcement Learning
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2310.05422.
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 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T14:14:45.930801Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-07T14:14:48.168307Z
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 56770b7a-9f6a-4c27-a856-48e770a0cb14 · inbound
Learning to Trust Bellman Updates: Selective State-Adaptive Regularization for Offline RL cites this paper.
Learning to Trust Bellman Updates: Selective State-Adaptive Regularization for Offline RL Reward-Consistent Dynamics Models are Strongly Generalizable for Offline Reinforcement Learning
Reference 11
- Resolution
- verified exact
- local_arxiv, observed 2026-08-07T14:14:48.307564Z
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.