Paper Citation Record · LEDGER

Evaluating the Effectiveness of Black-Box Prompt Optimization as the Scale of LLMs Continues to Grow

As of 20 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:2505.08303.

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

pith.paper-citation-record.v1
2505.08303 v1

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:00:35.771235Z

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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

19 of 19 outbound references displayed

verified exact0
verified fuzzy6
unresolved12
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 21c30bda-37a8-40c5-a0d7-165e01c01abe · outbound

This paper cites Advances in Neural Information Processing Systems , 33:1877–1901.

Evaluating the Effectiveness of Black-Box Prompt Optimization as the Scale of LLMs Continues to Grow Advances in Neural Information Processing Systems , 33:1877–1901

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:00:36.123434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1e92a30c-afaf-4117-89b9-5a774db8aee0 · outbound

This paper cites Black-Box Prompt Optimization: Aligning Large Language Models without Model Training.

Evaluating the Effectiveness of Black-Box Prompt Optimization as the Scale of LLMs Continues to Grow Black-Box Prompt Optimization: Aligning Large Language Models without Model Training

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-15T22:00:35.691748Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 29d4f24f-f989-41f1-a5c5-5b6275d2aaaf · outbound

This paper cites DeepSeek-V3 Technical Report.

Evaluating the Effectiveness of Black-Box Prompt Optimization as the Scale of LLMs Continues to Grow DeepSeek-V3 Technical Report

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T22:00:35.702805Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f4b2cca0-def1-4a54-8e89-adbd6b75bbf7 · outbound

This paper cites EvoPrompt: Connecting LLMs with Evolutionary Algorithms Yields Powerful Prompt Optimizers.

Evaluating the Effectiveness of Black-Box Prompt Optimization as the Scale of LLMs Continues to Grow EvoPrompt: Connecting LLMs with Evolutionary Algorithms Yields Powerful Prompt Optimizers

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T22:00:35.718348Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dd99951a-6e1a-472b-bae5-1d45454ab107 · outbound

This paper cites GPT-4o System Card.

Evaluating the Effectiveness of Black-Box Prompt Optimization as the Scale of LLMs Continues to Grow GPT-4o System Card

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T22:00:35.724296Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06b76652-7bd8-4a69-9cb7-6fb1955cdbc1 · outbound

This paper cites Automatic Prompt Optimization with "Gradient Descent" and Beam Search.

Evaluating the Effectiveness of Black-Box Prompt Optimization as the Scale of LLMs Continues to Grow Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T22:00:35.734403Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d02df2f1-2672-442d-8eb2-bf313066bde7 · outbound

This paper cites In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 4222–4235.

Evaluating the Effectiveness of Black-Box Prompt Optimization as the Scale of LLMs Continues to Grow In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 4222–4235

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:00:36.088293Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c70b25b3-fb70-4e73-82da-dd6286a7f6b2 · outbound

This paper cites Exploring the Robustness of Large Language Models for Solving Programming Problems.

Evaluating the Effectiveness of Black-Box Prompt Optimization as the Scale of LLMs Continues to Grow Exploring the Robustness of Large Language Models for Solving Programming Problems

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T22:00:35.744921Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f89617a7-62b4-4d60-9703-d13bd1c35a4b · outbound

This paper cites Large Language Models as Optimizers.

Evaluating the Effectiveness of Black-Box Prompt Optimization as the Scale of LLMs Continues to Grow Large Language Models as Optimizers

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T22:00:35.755379Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b91c598-544a-4115-a580-5217ae02fb7a · outbound

This paper cites A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Models.

Evaluating the Effectiveness of Black-Box Prompt Optimization as the Scale of LLMs Continues to Grow A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T22:00:35.760770Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4eefcc2e-b170-4cbc-8cc4-2db30c7fbd75 · outbound

This paper cites https://nlp.stanford.edu/sentiment/.

Evaluating the Effectiveness of Black-Box Prompt Optimization as the Scale of LLMs Continues to Grow https://nlp.stanford.edu/sentiment/

Reference 2013

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:00:36.071454Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6eaa0274-49d9-487c-b514-89f8c4e31e6e · outbound

This paper cites https: //github.com/mhjabreel/CharCnn_Keras/ tree/master/data/ag_news_csv.

Evaluating the Effectiveness of Black-Box Prompt Optimization as the Scale of LLMs Continues to Grow https: //github.com/mhjabreel/CharCnn_Keras/ tree/master/data/ag_news_csv

Reference 2015

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:00:36.054680Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 64ee4cc5-d4c1-48d6-a0f7-c847e5fbc570 · outbound

This paper cites SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization.

Evaluating the Effectiveness of Black-Box Prompt Optimization as the Scale of LLMs Continues to Grow SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-15T22:00:35.713153Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f8f0e93c-ddc3-4e70-8ecc-21c633069a20 · outbound

This paper cites In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020), pages 4668–4679.

Evaluating the Effectiveness of Black-Box Prompt Optimization as the Scale of LLMs Continues to Grow In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020), pages 4668–4679

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:00:36.143309Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b84af844-f30c-4162-8cf5-ece95d41197b · outbound

This paper cites In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing , pages 3045–3059.

Evaluating the Effectiveness of Black-Box Prompt Optimization as the Scale of LLMs Continues to Grow In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing , pages 3045–3059

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:00:36.105652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a0a6cf62-eee2-4ed9-8df1-d1c16f70f22f · outbound

This paper cites Large Language Models Are Human-Level Prompt Engineers.

Evaluating the Effectiveness of Black-Box Prompt Optimization as the Scale of LLMs Continues to Grow Large Language Models Are Human-Level Prompt Engineers

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-15T22:00:35.771235Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 205db581-43c3-4e42-a7d0-3fc6e729b5df · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

Evaluating the Effectiveness of Black-Box Prompt Optimization as the Scale of LLMs Continues to Grow Gemini: A Family of Highly Capable Multimodal Models

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-15T22:00:35.681461Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 71db3659-3475-4915-ae0c-9ac1f1412c62 · outbound

This paper cites Ground state nature and nonlinear squeezing of Gottesman-Kitaev-Preskill states.

Evaluating the Effectiveness of Black-Box Prompt Optimization as the Scale of LLMs Continues to Grow Ground state nature and nonlinear squeezing of Gottesman-Kitaev-Preskill states

Reference 2024

Resolution: metadata mismatch; local_arxiv, observed 2026-08-15T22:00:35.997856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4c50983e-821f-486b-820d-8436b2e41f8c · outbound

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

Evaluating the Effectiveness of Black-Box Prompt Optimization as the Scale of LLMs Continues to Grow DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-15T22:00:35.708044Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.