Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
DataDecide: How to Predict Best Pretraining Data with Small Experiments
As of 24 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2504.11393.
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 2026-08-23T06:30:58.430688+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T19:25:24.256379Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-03T05:17:40.210239Z
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 09c9c98c-958e-41bd-a2f9-16422b9b76e7 · inbound
Judging Quality Across Languages: A Multilingual Approach to Pretraining Data Filtering with Language Models cites this paper.
Judging Quality Across Languages: A Multilingual Approach to Pretraining Data Filtering with Language Models DataDecide: How to Predict Best Pretraining Data with Small Experiments
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:19:30.902227Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7b80c2bb-db38-4371-8bc0-d7b1e7857db7 · inbound
How to Train your Text-to-Image Model: Evaluating Design Choices for Synthetic Training Captions cites this paper.
How to Train your Text-to-Image Model: Evaluating Design Choices for Synthetic Training Captions DataDecide: How to Predict Best Pretraining Data with Small Experiments
Reference 28
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T19:25:24.256379Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ae2ccad7-69e2-4d31-8b2c-bf19e6d83711 · inbound
Language Models Improve When Pretraining Data Matches Target Tasks cites this paper.
Language Models Improve When Pretraining Data Matches Target Tasks DataDecide: How to Predict Best Pretraining Data with Small Experiments
Reference 61
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T16:53:11.514648Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cf1d38eb-a514-43f7-9cc3-7fb4574ecc31 · inbound
A Critical Look at Targeted Instruction Selection: Disentangling What Matters (and What Doesn't) cites this paper.
A Critical Look at Targeted Instruction Selection: Disentangling What Matters (and What Doesn't) DataDecide: How to Predict Best Pretraining Data with Small Experiments
Reference 44
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T23:11:03.148848Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7b9de1d4-0dee-4613-90d8-98f86f0b72fa · inbound
Validity Threats for Foundation Model Research cites this paper.
Validity Threats for Foundation Model Research DataDecide: How to Predict Best Pretraining Data with Small Experiments
Reference 64
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-02T07:36:44.884445Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 7929d72c-ea1a-4822-b48e-47c15d0e4895 · inbound
Item Response Scaling Laws: A Measurement Theory Approach for Efficient and Generalizable Neural Scaling Estimation cites this paper.
Item Response Scaling Laws: A Measurement Theory Approach for Efficient and Generalizable Neural Scaling Estimation DataDecide: How to Predict Best Pretraining Data with Small Experiments
Reference 19
- Resolution
- verified exact
- arxiv_id, observed 2026-06-28T22:52:45.627096Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation baf43501-86a2-4479-b116-8abcaa119d67 · inbound
Small Experiments, Cheaper Decisions: A Case Study in Staged Promotion for Micro-Pretraining cites this paper.
Small Experiments, Cheaper Decisions: A Case Study in Staged Promotion for Micro-Pretraining DataDecide: How to Predict Best Pretraining Data with Small Experiments
Reference 6
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T05:17:40.212442Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation fc358018-e038-4a34-b346-0d2b169b2c71 · inbound
Domain-Aware Scaling Laws Uncover Data Synergy cites this paper.
Domain-Aware Scaling Laws Uncover Data Synergy DataDecide: How to Predict Best Pretraining Data with Small Experiments
Reference 25
- Resolution
- unresolved
- no resolver link, observed 2026-07-14T07:24:27.255815Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1b2d7a09-31c8-4455-a1fa-1df0d0750217 · inbound
Bridging Compute- and Data-Optimal Pretraining cites this paper.
Bridging Compute- and Data-Optimal Pretraining DataDecide: How to Predict Best Pretraining Data with Small Experiments
Reference 74
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T03:02:04.586864Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.