Paper Citation Record · LEDGER

Are Large Language Models Good Prompt Optimizers?

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2402.02101.

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

pith.paper-citation-record.v1
2402.02101 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T06:02:12.885407Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T07:46:46.350662Z

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 0ae4f46c-96d0-4df8-a068-e73c72248a09 · inbound

Automatic Prompt Optimization Techniques: Exploring the Potential for Synthetic Data Generation cites this paper.

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 91b3e1b9-12ac-4838-af70-ffc9c0e8bcb1 · inbound

Optimizing LLM-Based Multi-Agent System with Textual Feedback: A Case Study on Software Development cites this paper.

Optimizing LLM-Based Multi-Agent System with Textual Feedback: A Case Study on Software Development Are Large Language Models Good Prompt Optimizers?

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-07T15:12:35.988542Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e85cb496-4538-4a10-a1fc-58c3a14a3b4d · inbound

From Standalone LLMs to Integrated Intelligence: A Survey of Compound Al Systems cites this paper.

From Standalone LLMs to Integrated Intelligence: A Survey of Compound Al Systems Are Large Language Models Good Prompt Optimizers?

Reference 114

Resolution: verified exact; arxiv_id, observed 2026-05-19T11:52:16.290885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 17c71aa8-be3f-4bb1-8258-d9a466ab742b · inbound

Enhancing Reasoning Capabilities of Small Language Models with Blueprints and Prompt Template Search cites this paper.

Enhancing Reasoning Capabilities of Small Language Models with Blueprints and Prompt Template Search Are Large Language Models Good Prompt Optimizers?

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T05:10:39.537689Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20bc0b4b-0d70-4559-9c40-e016ec50a2bb · inbound

APIO: Automatic Prompt Induction and Optimization for Grammatical Error Correction and Text Simplification cites this paper.

APIO: Automatic Prompt Induction and Optimization for Grammatical Error Correction and Text Simplification Are Large Language Models Good Prompt Optimizers?

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-05T21:09:00.807379Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e58de785-7650-4109-96bf-69e4fdcd2154 · inbound

CRAFT: Cost-aware Refinement And Front-aware Tuning of Prompts cites this paper.

CRAFT: Cost-aware Refinement And Front-aware Tuning of Prompts Are Large Language Models Good Prompt Optimizers?

Reference 211

Resolution: verified exact; arxiv_id, observed 2026-07-02T07:46:46.351901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.