Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
MILO: Model-Agnostic Subset Selection Framework for Efficient Model Training and Tuning
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2301.13287.
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 2026-08-07T00:23:25.073638Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-03T19:28:52.619180Z
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 71f34745-f77c-44e6-a476-78f30aaa04a7 · inbound
Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection cites this paper.
Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection MILO: Model-Agnostic Subset Selection Framework for Efficient Model Training and Tuning
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T00:23:25.073638Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a5358a25-b45e-45f8-a604-60fa3b850fb8 · inbound
Rethinking Dataset Distillation for Classification: Do Distilled Sets Outperform Coresets? cites this paper.
Rethinking Dataset Distillation for Classification: Do Distilled Sets Outperform Coresets? MILO: Model-Agnostic Subset Selection Framework for Efficient Model Training and Tuning
Reference 46
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T19:28:52.625310Z
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.