Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models
As of 4 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2502.09003.
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 2026-08-01T09:31:59.865124Z
A source-named dated measurement, never combined with another source.
Source: cited_works
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 9f32c2bc-feee-4828-9510-00a671ebd2bd · inbound
GaugeQuant: Online Learning of Quantization-Optimal Bases from LLM Symmetries cites this paper.
GaugeQuant: Online Learning of Quantization-Optimal Bases from LLM Symmetries RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models
Reference 22
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T09:31:59.865124Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.