Paper Citation Record · LEDGER

Meta-Learning and Synthetic Data for Automated Pretraining and Finetuning

As of 8 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 0 inbound Pith citation observations for arXiv:2506.12161.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.12161 v1

Coverage vector

measured 18 of 18 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:50:49.316599Z

measured 18 of 18 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

18 of 18 outbound references displayed

verified exact0
verified fuzzy10
unresolved8
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d8a62f20-ca31-4b04-a4e3-4196ece3e923 · outbound

This paper cites GPT-4 Technical Report.

Meta-Learning and Synthetic Data for Automated Pretraining and Finetuning GPT-4 Technical Report

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T04:50:49.263542Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8da2ce6f-d91d-4d52-ab6b-98f6d64650e9 · outbound

This paper cites Automated Reinforcement Learning (AutoRL): A Survey and Open Problems.

Meta-Learning and Synthetic Data for Automated Pretraining and Finetuning Automated Reinforcement Learning (AutoRL): A Survey and Open Problems

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:50:49.462335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b7b0ecac-0fe9-4861-9bea-b06e85256f3a · outbound

This paper cites Learning from synthetic data: Addressing domain shift for semantic segmentation.

Meta-Learning and Synthetic Data for Automated Pretraining and Finetuning Learning from synthetic data: Addressing domain shift for semantic segmentation

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:50:49.443287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a4d9c420-f8d8-42cd-bc9f-1702676fab93 · outbound

This paper cites A survey on image data augmentation for deep learning.

Meta-Learning and Synthetic Data for Automated Pretraining and Finetuning A survey on image data augmentation for deep learning

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:50:49.422525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0840aa39-45bf-418a-933a-53fc23764f09 · outbound

This paper cites Solving math word problems with process- and outcome-based feedback.

Meta-Learning and Synthetic Data for Automated Pretraining and Finetuning Solving math word problems with process- and outcome-based feedback

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T04:50:49.310173Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 651552c5-9d50-43ad-8ab0-4a3cdb26d2a2 · outbound

This paper cites Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutions.

Meta-Learning and Synthetic Data for Automated Pretraining and Finetuning Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutions

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T04:50:49.313319Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 04e8ea27-2f56-4f2f-a1ce-9cbf311094a1 · outbound

This paper cites Chapter Title (e.g., Trends in AI Development).

Meta-Learning and Synthetic Data for Automated Pretraining and Finetuning Chapter Title (e.g., Trends in AI Development)

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:50:49.490926Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6cbecbd1-8c5e-42c6-a327-b11e3f59c6a9 · outbound

This paper cites Evolutionary Principles in Self-Referential Learning. On Learning now to Learn: The Meta-Meta-Meta...-Hook.

Meta-Learning and Synthetic Data for Automated Pretraining and Finetuning Evolutionary Principles in Self-Referential Learning. On Learning now to Learn: The Meta-Meta-Meta...-Hook

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:50:49.432872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4381670c-c86b-4184-99e2-b278d87aae71 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Meta-Learning and Synthetic Data for Automated Pretraining and Finetuning DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-07T04:50:49.277128Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 838f2ad4-62b7-4582-aaf3-5ccb7ec6858e · outbound

This paper cites Training Compute-Optimal Large Language Models.

Meta-Learning and Synthetic Data for Automated Pretraining and Finetuning Training Compute-Optimal Large Language Models

Reference 97

Resolution: unresolved; no resolver link, observed 2026-08-07T04:50:49.280531Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56144d85-6bbc-471f-9446-6105c6285826 · outbound

This paper cites Improved Regularization of Convolutional Neural Networks with Cutout.

Meta-Learning and Synthetic Data for Automated Pretraining and Finetuning Improved Regularization of Convolutional Neural Networks with Cutout

Reference 98

Resolution: unresolved; no resolver link, observed 2026-08-07T04:50:49.270603Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 369451cd-e398-4bc9-a0b6-8c681eedb612 · outbound

This paper cites Robust Speech Recognition via Large-Scale Weak Supervision.

Meta-Learning and Synthetic Data for Automated Pretraining and Finetuning Robust Speech Recognition via Large-Scale Weak Supervision

Reference 139

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:50:49.452816Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a256adde-8811-46b4-960d-fddbb73dbcdc · outbound

This paper cites A Survey on Transfer Learning.

Meta-Learning and Synthetic Data for Automated Pretraining and Finetuning A Survey on Transfer Learning

Reference 162

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:50:49.472314Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bd391034-5bd7-4e0d-8da9-dab6051c4ce3 · outbound

This paper cites SAM 2: Segment Anything in Images and Videos.

Meta-Learning and Synthetic Data for Automated Pretraining and Finetuning SAM 2: Segment Anything in Images and Videos

Reference 202

Resolution: unresolved; no resolver link, observed 2026-08-07T04:50:49.298593Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b535d0f-b897-4e1a-a60f-87b728dfaec9 · outbound

This paper cites Learning Synthetic Environments and Reward Networks for Reinforcement Learning.

Meta-Learning and Synthetic Data for Automated Pretraining and Finetuning Learning Synthetic Environments and Reward Networks for Reinforcement Learning

Reference 251

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:50:49.500221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f243319a-6374-450b-b60a-d1e5339e0b36 · outbound

This paper cites A Simple Framework for Contrastive Learning of Visual Representations.

Meta-Learning and Synthetic Data for Automated Pretraining and Finetuning A Simple Framework for Contrastive Learning of Visual Representations

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:50:49.509737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e1e00d76-0dd1-491b-84be-985ebe9c1cca · outbound

This paper cites Transformers Can Do Bayesian Inference.

Meta-Learning and Synthetic Data for Automated Pretraining and Finetuning Transformers Can Do Bayesian Inference

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:50:49.481777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d7e353ef-eb8f-4b70-93ff-5c596118c4bd · outbound

This paper cites Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment.

Meta-Learning and Synthetic Data for Automated Pretraining and Finetuning Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 7317

Resolution: unresolved; no resolver link, observed 2026-08-07T04:50:49.316599Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.