Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Rethinking Diverse Human Preference Learning through Principal Component Analysis
As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2502.13131.
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 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T14:31:48.050210Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-06T16:34:26.014981Z
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 aa3dae91-b5dd-4f5e-bf93-8c62f453417a · inbound
OpenReview Should be Protected and Leveraged as a Community Asset for Research in the Era of Large Language Models cites this paper.
OpenReview Should be Protected and Leveraged as a Community Asset for Research in the Era of Large Language Models Rethinking Diverse Human Preference Learning through Principal Component Analysis
Reference 152
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:31:48.050210Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 28372cfb-437c-4cf6-9e3c-4a6219d7a720 · inbound
Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities cites this paper.
Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Rethinking Diverse Human Preference Learning through Principal Component Analysis
Reference 56
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T16:34:26.018857Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.