Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Learning to Repair: Repairing model output errors after deployment using a dynamic memory of feedback
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2112.09737.
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 2026-08-06T16:34:25.220429Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-10T17:34:42.702729Z
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 95f1099e-b607-4ed9-9c06-de84e5482d24 · inbound
LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code cites this paper.
LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code Learning to Repair: Repairing model output errors after deployment using a dynamic memory of feedback
Reference 136
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T17:34:42.704377Z
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.
Observation 36944cbd-6b9a-4553-9207-488a235ed765 · 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 Learning to Repair: Repairing model output errors after deployment using a dynamic memory of feedback
Reference 92
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T16:34:25.220429Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.