Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo
As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2305.18246.
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 2026-08-06T06:34:29.942622+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-05-21T13:06:54.002248Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-21T13:10:10.452691Z
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 bca89287-66a4-4e47-aedb-4b090b1bce2c · inbound
Provably avoiding over-optimization in Direct Preference Optimization without knowing the data distribution cites this paper.
Provably avoiding over-optimization in Direct Preference Optimization without knowing the data distribution Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo
Reference 14
- Resolution
- verified exact
- arxiv_id, observed 2026-05-16T06:37:28.546455Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 9ad30eac-c650-4799-99bd-1da34f9cfd97 · inbound
Provably avoiding over-optimization in Direct Preference Optimization without knowing the data distribution cites this paper.
Provably avoiding over-optimization in Direct Preference Optimization without knowing the data distribution Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo
Reference 14
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T13:10:10.454778Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.