Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Can Recommender Systems Teach Themselves? A Recursive Self-Improving Framework with Fidelity Control
As of 4 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2602.15659.
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 2026-08-04T06:34:03.388597+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-02T22:31:38.330653Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-10T10:49:55.767916Z
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 9e599fee-de9b-459b-a36d-c217ddf39dde · inbound
Why Thinking Hurts: Diagnosing and Rectifying Linguistic Inertia in Large Language Models for Recommendation cites this paper.
Why Thinking Hurts: Diagnosing and Rectifying Linguistic Inertia in Large Language Models for Recommendation Can Recommender Systems Teach Themselves? A Recursive Self-Improving Framework with Fidelity Control
Reference 46
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T22:31:38.330653Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e1041a64-4e68-4a38-a970-b5bbbe9b0131 · inbound
IE as Cache: Information Extraction Enhanced Agentic Reasoning cites this paper.
IE as Cache: Information Extraction Enhanced Agentic Reasoning Can Recommender Systems Teach Themselves? A Recursive Self-Improving Framework with Fidelity Control
Reference 62
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T01:43:57.960923Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.