Paper Citation Record · LEDGER

Automatic Prompt Optimization Techniques: Exploring the Potential for Synthetic Data Generation

As of 9 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2502.03078.

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

pith.paper-citation-record.v1
2502.03078 v2

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T06:02:12.958542Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+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

31 of 31 outbound references displayed

verified exact0
verified fuzzy9
unresolved21
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 41ee3c4f-339b-43ae-8f67-fdf56809ff13 · outbound

This paper cites Ap- plied Sciences13(12) (2023), https://www.mdpi.com/2076-3417/13/12/7082.

Automatic Prompt Optimization Techniques: Exploring the Potential for Synthetic Data Generation Ap- plied Sciences13(12) (2023), https://www.mdpi.com/2076-3417/13/12/7082

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T06:02:28.640662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation bc8ebb9f-7d32-4073-b1af-97dfc7d5d6cd · outbound

This paper cites Prompt Design and Engineering: Introduction and Advanced Methods.

Automatic Prompt Optimization Techniques: Exploring the Potential for Synthetic Data Generation Prompt Design and Engineering: Introduction and Advanced Methods

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-09T06:02:12.774490Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 69d5314f-c7bb-455e-85fe-9c74a3b89d64 · outbound

This paper cites Language Models are Few-Shot Learners.

Automatic Prompt Optimization Techniques: Exploring the Potential for Synthetic Data Generation Language Models are Few-Shot Learners

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-09T06:02:12.780604Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0226b738-5d0a-45aa-a05d-cb2eb5427370 · outbound

This paper cites Frontiers in Neuroscience17 (2023), https://www.frontiersin.org/journals/ neuroscience/articles/10.3389/fnins.2023.1219133.

Automatic Prompt Optimization Techniques: Exploring the Potential for Synthetic Data Generation Frontiers in Neuroscience17 (2023), https://www.frontiersin.org/journals/ neuroscience/articles/10.3389/fnins.2023.1219133

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-09T06:02:12.787405Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2058d82e-759f-4c17-a8ce-c82181870ad8 · outbound

This paper cites an unresolved cited work.

Automatic Prompt Optimization Techniques: Exploring the Potential for Synthetic Data Generation Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-09T06:02:28.623152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 102a1fe0-395a-48eb-a993-82af97b5f787 · outbound

This paper cites an unresolved cited work.

Automatic Prompt Optimization Techniques: Exploring the Potential for Synthetic Data Generation Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-09T06:02:28.605381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation cfeeb9c1-3799-461c-b45e-3478c92d5026 · outbound

This paper cites IEEE Journal of Biomedical and Health Informatics 28(7), 4084–4093 (2024).

Automatic Prompt Optimization Techniques: Exploring the Potential for Synthetic Data Generation IEEE Journal of Biomedical and Health Informatics 28(7), 4084–4093 (2024)

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T06:02:28.585347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ae8fd77e-7e24-47f1-935e-eabeac352ba5 · outbound

This paper cites Neurocomputing321, 321–331 (Dec 2018).

Automatic Prompt Optimization Techniques: Exploring the Potential for Synthetic Data Generation Neurocomputing321, 321–331 (Dec 2018)

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-09T06:02:12.813408Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53b0437e-7834-445f-9216-6e9950f536d5 · outbound

This paper cites Generative Adversarial Networks.

Automatic Prompt Optimization Techniques: Exploring the Potential for Synthetic Data Generation Generative Adversarial Networks

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-09T06:02:12.819566Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 46917925-acd3-4637-99cc-c65c36424262 · outbound

This paper cites IEEE Intelligent Systems24(2), 8–12 (2009).

Automatic Prompt Optimization Techniques: Exploring the Potential for Synthetic Data Generation IEEE Intelligent Systems24(2), 8–12 (2009)

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T06:02:28.551779Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b78d9a31-0157-4404-9b66-a4a272ab8959 · outbound

This paper cites Denoising Diffusion Probabilistic Models.

Automatic Prompt Optimization Techniques: Exploring the Potential for Synthetic Data Generation Denoising Diffusion Probabilistic Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-09T06:02:12.831087Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0caa2560-8023-4f23-b0dc-1eee1bc559c4 · outbound

This paper cites Automatic Engineering of Long Prompts.

Automatic Prompt Optimization Techniques: Exploring the Potential for Synthetic Data Generation Automatic Engineering of Long Prompts

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-09T06:02:12.837173Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f5125ac-e081-44e5-a4c9-9a6609f863b4 · outbound

This paper cites Autonomous Prompt Engineering in Large Language Models.

Automatic Prompt Optimization Techniques: Exploring the Potential for Synthetic Data Generation Autonomous Prompt Engineering in Large Language Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-09T06:02:12.844249Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 223f8abd-853f-4cd5-bbc7-a3334188274c · outbound

This paper cites Auto-Encoding Variational Bayes.

Automatic Prompt Optimization Techniques: Exploring the Potential for Synthetic Data Generation Auto-Encoding Variational Bayes

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-09T06:02:12.850507Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ff69ef02-8608-433f-a826-84a5f90840db · outbound

This paper cites In: Machine Learning and Knowledge Discovery in Databases.

Automatic Prompt Optimization Techniques: Exploring the Potential for Synthetic Data Generation In: Machine Learning and Knowledge Discovery in Databases

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T06:02:28.530684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 20a8a0e9-effa-4476-ad97-fab8dd8ac4bb · outbound

This paper cites Annals of internal medicine151(4), W–65 (2009).

Automatic Prompt Optimization Techniques: Exploring the Potential for Synthetic Data Generation Annals of internal medicine151(4), W–65 (2009)

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T06:02:28.512979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2014a049-a461-4e49-ac6a-3de4574edd0b · outbound

This paper cites Is Translation All You Need? A Study on Solving Multilingual Tasks with Large Language Models.

Automatic Prompt Optimization Techniques: Exploring the Potential for Synthetic Data Generation Is Translation All You Need? A Study on Solving Multilingual Tasks with Large Language Models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-09T06:02:12.873103Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ace2957-b069-4f4c-b382-25951cfa8c6e · outbound

This paper cites Prompt Engineering Through the Lens of Optimal Control.

Automatic Prompt Optimization Techniques: Exploring the Potential for Synthetic Data Generation Prompt Engineering Through the Lens of Optimal Control

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-09T06:02:12.879383Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ae4f46c-96d0-4df8-a068-e73c72248a09 · outbound

This paper cites Are Large Language Models Good Prompt Optimizers?.

Automatic Prompt Optimization Techniques: Exploring the Potential for Synthetic Data Generation Are Large Language Models Good Prompt Optimizers?

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-09T06:02:12.885407Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f15052e-563d-4cc1-9d03-2ba6648bb012 · outbound

This paper cites an unresolved cited work.

Automatic Prompt Optimization Techniques: Exploring the Potential for Synthetic Data Generation Unresolved cited work

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-09T06:02:12.890919Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 47ed8715-85f1-4167-8426-eb66397f3757 · outbound

This paper cites OpenAI (2023), https://openai.com/index/ gpt-4/.

Automatic Prompt Optimization Techniques: Exploring the Potential for Synthetic Data Generation OpenAI (2023), https://openai.com/index/ gpt-4/

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T06:02:28.482966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation cb51dc98-15e8-401e-adca-398b3e90f232 · outbound

This paper cites Stochastic Backpropagation and Approximate Inference in Deep Generative Models.

Automatic Prompt Optimization Techniques: Exploring the Potential for Synthetic Data Generation Stochastic Backpropagation and Approximate Inference in Deep Generative Models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-09T06:02:12.904890Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 82402f1b-b0eb-4779-b392-b8316dc30949 · outbound

This paper cites The Prompt Report: A Systematic Survey of Prompt Engineering Techniques.

Automatic Prompt Optimization Techniques: Exploring the Potential for Synthetic Data Generation The Prompt Report: A Systematic Survey of Prompt Engineering Techniques

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-09T06:02:12.910542Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b02a76bc-3f21-4c9a-9d1a-beaac02a5aa4 · outbound

This paper cites JMIR Hum Factors11, e47031 (Jan 2024), https://humanfactors.jmir.org/2024/1/e47031.

Automatic Prompt Optimization Techniques: Exploring the Potential for Synthetic Data Generation JMIR Hum Factors11, e47031 (Jan 2024), https://humanfactors.jmir.org/2024/1/e47031

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T06:02:28.461577Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4a4cf2a3-c6ea-4075-a30d-37951ef30c80 · outbound

This paper cites Information15(5) (2024), https://www.mdpi.com/2078-2489/15/5/264 Automatic Prompt Optimization Techniques 13.

Automatic Prompt Optimization Techniques: Exploring the Potential for Synthetic Data Generation Information15(5) (2024), https://www.mdpi.com/2078-2489/15/5/264 Automatic Prompt Optimization Techniques 13

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T06:02:28.443709Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8eee3cc2-a2a9-481b-87fa-aa121e191c8c · outbound

This paper cites http://www.incompleteideas.net/IncIdeas/ (2019), accessed: December 2024.

Automatic Prompt Optimization Techniques: Exploring the Potential for Synthetic Data Generation http://www.incompleteideas.net/IncIdeas/ (2019), accessed: December 2024

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T06:02:28.425068Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 71db4a02-309c-492e-bb3b-e10898329697 · outbound

This paper cites an unresolved cited work.

Automatic Prompt Optimization Techniques: Exploring the Potential for Synthetic Data Generation Unresolved cited work

Reference 27

Resolution: unresolved; raw_fallback, observed 2026-08-09T06:02:28.408002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0a077335-b5e2-4a81-9273-ded640af5515 · outbound

This paper cites an unresolved cited work.

Automatic Prompt Optimization Techniques: Exploring the Potential for Synthetic Data Generation Unresolved cited work

Reference 28

Resolution: unresolved; raw_fallback, observed 2026-08-09T06:02:28.390519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5487edbe-39c4-4bf9-a205-dd65097ec556 · outbound

This paper cites StraGo: Harnessing Strategic Guidance for Prompt Optimization.

Automatic Prompt Optimization Techniques: Exploring the Potential for Synthetic Data Generation StraGo: Harnessing Strategic Guidance for Prompt Optimization

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-09T06:02:12.946864Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 98d767b9-7cd4-4c05-a2e5-f5bdc9a058b6 · outbound

This paper cites Practical Applications of Advanced Cloud Services and Generative AI Systems in Medical Image Analysis.

Automatic Prompt Optimization Techniques: Exploring the Potential for Synthetic Data Generation Practical Applications of Advanced Cloud Services and Generative AI Systems in Medical Image Analysis

Reference 30

Resolution: metadata mismatch; local_arxiv, observed 2026-08-09T06:02:13.044848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0104a585-faeb-4244-aff0-e901fd362277 · outbound

This paper cites Large Language Models as Optimizers.

Automatic Prompt Optimization Techniques: Exploring the Potential for Synthetic Data Generation Large Language Models as Optimizers

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-09T06:02:12.958542Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.