Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
LabelBench: A Comprehensive Framework for Benchmarking Adaptive Label-Efficient Learning
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2306.09910.
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 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T15:39:24.078500Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-06-28T18:42:29.011196Z
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 7e5e6f8e-c09b-4df8-909c-eb08c9507f76 · inbound
To Label or Not to Label: PALM -- A Predictive Model for Evaluating Sample Efficiency in Active Learning Models cites this paper.
To Label or Not to Label: PALM -- A Predictive Model for Evaluating Sample Efficiency in Active Learning Models LabelBench: A Comprehensive Framework for Benchmarking Adaptive Label-Efficient Learning
Reference 40
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T15:39:24.078500Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 40dc412e-3522-4334-a858-9f18bd9bc086 · inbound
Improving Task Diversity in Label Efficient Supervised Finetuning of LLMs cites this paper.
Improving Task Diversity in Label Efficient Supervised Finetuning of LLMs LabelBench: A Comprehensive Framework for Benchmarking Adaptive Label-Efficient Learning
Reference 38
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T12:47:45.142282Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7c269104-4378-48bb-a7b2-9b5ab90bcd3c · inbound
Active Testing of Large Language Models via Approximate Neyman Allocation cites this paper.
Active Testing of Large Language Models via Approximate Neyman Allocation LabelBench: A Comprehensive Framework for Benchmarking Adaptive Label-Efficient Learning
Reference 17
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T06:41:40.548202Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 501e85ec-53eb-4632-9737-d2d5f83229d0 · inbound
Active Testing of Large Language Models via Approximate Neyman Allocation cites this paper.
Active Testing of Large Language Models via Approximate Neyman Allocation LabelBench: A Comprehensive Framework for Benchmarking Adaptive Label-Efficient Learning
Reference 17
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T23:09:12.373784Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 5ebe6224-f73b-43fc-bbd5-895594b508be · inbound
Active Learning with Foundation Model Priors: Efficient Learning under Class Imbalance cites this paper.
Active Learning with Foundation Model Priors: Efficient Learning under Class Imbalance LabelBench: A Comprehensive Framework for Benchmarking Adaptive Label-Efficient Learning
Reference 19
- Resolution
- verified exact
- arxiv_id, observed 2026-06-28T18:42:29.012769Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.