Paper Citation Record · LEDGER

A Sequential Optimal Learning Approach to Automated Prompt Engineering in Large Language Models

As of 13 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 1 inbound Pith citation observation for arXiv:2501.03508.

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

pith.paper-citation-record.v1
2501.03508 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T21:56:59.041953Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:14:08.203086Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T15:14:16.303537Z

Reference resolution

39 of 39 outbound references displayed

verified exact0
verified fuzzy30
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 91d8f365-25ac-4ec6-83f4-2ff757b765d4 · outbound

This paper cites Normalized (pointwise) mutual infor- mation in collocation extraction.

A Sequential Optimal Learning Approach to Automated Prompt Engineering in Large Language Models Normalized (pointwise) mutual infor- mation in collocation extraction

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:59.339960Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 862e55c1-fdad-4045-9f5d-2bc99b998f5e · outbound

This paper cites Language models are few-shot learners.

A Sequential Optimal Learning Approach to Automated Prompt Engineering in Large Language Models Language models are few-shot learners

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:59.333185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ef201629-03ba-490e-aa47-af7dfa7ddbc7 · outbound

This paper cites Instructzero: Efficient instruc- tion optimization for black-box large language mod- els.

A Sequential Optimal Learning Approach to Automated Prompt Engineering in Large Language Models Instructzero: Efficient instruc- tion optimization for black-box large language mod- els

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:59.327008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation dbfd15a3-7013-40de-9e47-ef6aaa1957fb · outbound

This paper cites Bayesian ideas and discrete event sim- ulation: why, what and how.

A Sequential Optimal Learning Approach to Automated Prompt Engineering in Large Language Models Bayesian ideas and discrete event sim- ulation: why, what and how

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:59.320852Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 7ba5d291-01fd-4900-9276-43f2e614376d · outbound

This paper cites Sequential sampling to myopically maximize the expected value of information.

A Sequential Optimal Learning Approach to Automated Prompt Engineering in Large Language Models Sequential sampling to myopically maximize the expected value of information

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:59.314576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 1d83f6f3-8443-4b3e-9ef0-da9c4b9669f3 · outbound

This paper cites Optimal information blending with measurements in the l 2 sphere.

A Sequential Optimal Learning Approach to Automated Prompt Engineering in Large Language Models Optimal information blending with measurements in the l 2 sphere

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:59.308217Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 098f41bb-72db-401e-aa05-f5ca817f90c2 · outbound

This paper cites Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves.

A Sequential Optimal Learning Approach to Automated Prompt Engineering in Large Language Models Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T21:56:58.969175Z

Source-reported events for the cited work

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Observation 5f629928-a52c-4d58-a11a-48b7d21cd1fe · outbound

This paper cites Promptbreeder: Self-referential self-improvement via prompt evolution.

A Sequential Optimal Learning Approach to Automated Prompt Engineering in Large Language Models Promptbreeder: Self-referential self-improvement via prompt evolution

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:59.301767Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b07ce3e7-948f-4c62-a0b7-cf9d4665c525 · outbound

This paper cites The knowledge-gradient policy for correlated normal be- liefs.

A Sequential Optimal Learning Approach to Automated Prompt Engineering in Large Language Models The knowledge-gradient policy for correlated normal be- liefs

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:59.295485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a360d647-9503-4709-8948-4046d24caeef · outbound

This paper cites Consistency of sequential bayesian sampling policies.

A Sequential Optimal Learning Approach to Automated Prompt Engineering in Large Language Models Consistency of sequential bayesian sampling policies

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:59.289251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation fa9b26a8-1eba-4237-aa71-d5b065e6cc84 · outbound

This paper cites A knowledge-gradient policy for sequential information collection.

A Sequential Optimal Learning Approach to Automated Prompt Engineering in Large Language Models A knowledge-gradient policy for sequential information collection

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:59.283413Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f829ea99-75d0-49da-a9f0-4ed0d7a666da · outbound

This paper cites Connecting large language models with evolu- tionary algorithms yields powerful prompt optimizers.

A Sequential Optimal Learning Approach to Automated Prompt Engineering in Large Language Models Connecting large language models with evolu- tionary algorithms yields powerful prompt optimizers

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:59.275837Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f27561b7-f6ed-466f-b43f-6fa72716babc · outbound

This paper cites Bayesian look ahead one-stage sampling allocations for selection of the best population.

A Sequential Optimal Learning Approach to Automated Prompt Engineering in Large Language Models Bayesian look ahead one-stage sampling allocations for selection of the best population

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:59.268561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation af95272d-cedf-4c00-accd-b430c0f9f910 · outbound

This paper cites Op- timal learning in linear regression with combinatorial feature selection.

A Sequential Optimal Learning Approach to Automated Prompt Engineering in Large Language Models Op- timal learning in linear regression with combinatorial feature selection

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:59.262578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f6fb3c21-8c3d-4131-91c2-b3da15f3f714 · outbound

This paper cites Instruction Induction: From Few Examples to Natural Language Task Descriptions.

A Sequential Optimal Learning Approach to Automated Prompt Engineering in Large Language Models Instruction Induction: From Few Examples to Natural Language Task Descriptions

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-10T21:56:58.985933Z

Source-reported events for the cited work

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Observation 62212b30-e288-4640-bb37-096312f77534 · outbound

This paper cites APEER: Automatic Prompt Engineering Enhances Large Language Model Reranking.

A Sequential Optimal Learning Approach to Automated Prompt Engineering in Large Language Models APEER: Automatic Prompt Engineering Enhances Large Language Model Reranking

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-10T21:56:58.988324Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5b683cb7-9018-4bf6-8893-9a6ea531fac9 · outbound

This paper cites Better zero-shot reasoning with role- play prompting.

A Sequential Optimal Learning Approach to Automated Prompt Engineering in Large Language Models Better zero-shot reasoning with role- play prompting

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:59.256369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 06e4d18d-dc61-4c6c-8215-71eccb3aabf9 · outbound

This paper cites Large Language Models Understand and Can be Enhanced by Emotional Stimuli.

A Sequential Optimal Learning Approach to Automated Prompt Engineering in Large Language Models Large Language Models Understand and Can be Enhanced by Emotional Stimuli

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-10T21:56:58.992852Z

Source-reported events for the cited work

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Observation d1985576-0516-495c-8e00-8d23ab611cd4 · outbound

This paper cites Use your INSTINCT: IN- STruction optimization for LLMs using neural bandits 11 coupled with transformers.

A Sequential Optimal Learning Approach to Automated Prompt Engineering in Large Language Models Use your INSTINCT: IN- STruction optimization for LLMs using neural bandits 11 coupled with transformers

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:59.250118Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation cbbfcfb2-0983-47d0-949a-f94fe417edc0 · outbound

This paper cites Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing.

A Sequential Optimal Learning Approach to Automated Prompt Engineering in Large Language Models Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:59.243285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e9228df1-5a75-44f2-b0f0-040cccb1bea2 · outbound

This paper cites Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity.

A Sequential Optimal Learning Approach to Automated Prompt Engineering in Large Language Models Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:59.236973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 34dd0c31-5ff7-4150-a5a7-7c9132dea439 · outbound

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

A Sequential Optimal Learning Approach to Automated Prompt Engineering in Large Language Models Prompt Engineering Through the Lens of Optimal Control

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-10T21:56:59.001475Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 77bfbd05-7f11-445a-baa8-61e4cc3acc69 · outbound

This paper cites Rlprompt: Optimiz- ing discrete text prompts with reinforcement learning.

A Sequential Optimal Learning Approach to Automated Prompt Engineering in Large Language Models Rlprompt: Optimiz- ing discrete text prompts with reinforcement learning

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:59.230735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ba20e96d-2dcc-4b92-865f-e2259dfdb451 · outbound

This paper cites Sequential learning in designing marketing campaigns for market entry.

A Sequential Optimal Learning Approach to Automated Prompt Engineering in Large Language Models Sequential learning in designing marketing campaigns for market entry

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:59.224568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e06c41a3-c73b-4463-8521-2e7ce05f55cc · outbound

This paper cites Reinforcement Learning and Stochas- tic Optimization: A Unified Framework for Sequential Decisions.

A Sequential Optimal Learning Approach to Automated Prompt Engineering in Large Language Models Reinforcement Learning and Stochas- tic Optimization: A Unified Framework for Sequential Decisions

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:59.218172Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation cd6fab6d-6325-4764-80e3-39c741f77d2f · outbound

This paper cites Optimal learning, volume 841.

A Sequential Optimal Learning Approach to Automated Prompt Engineering in Large Language Models Optimal learning, volume 841

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:59.210546Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 43263a24-ff46-4e41-a922-05fe1be2f1da · outbound

This paper cites GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language Models.

A Sequential Optimal Learning Approach to Automated Prompt Engineering in Large Language Models GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language Models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-10T21:56:59.012110Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 189f5e9c-d1f0-4a2d-b60a-5291d7f526a2 · outbound

This paper cites Automatic prompt op- timization with ”gradient descent” and beam search.

A Sequential Optimal Learning Approach to Automated Prompt Engineering in Large Language Models Automatic prompt op- timization with ”gradient descent” and beam search

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:59.202959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 86943dd7-7bbb-49b0-8eb3-4b07d162f45f · outbound

This paper cites Markov decision processes: dis- crete stochastic dynamic programming.

A Sequential Optimal Learning Approach to Automated Prompt Engineering in Large Language Models Markov decision processes: dis- crete stochastic dynamic programming

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:59.195476Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 50ccc1f6-6101-440e-84b2-c9c276e4249e · outbound

This paper cites Language mod- els are unsupervised multitask learners.

A Sequential Optimal Learning Approach to Automated Prompt Engineering in Large Language Models Language mod- els are unsupervised multitask learners

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:59.188842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 60e5f92d-0b15-4022-8df0-8f15fa76c40d · outbound

This paper cites A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications.

A Sequential Optimal Learning Approach to Automated Prompt Engineering in Large Language Models A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-10T21:56:59.020953Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 215c0e6f-b769-4c85-863d-4ea2280ec94d · outbound

This paper cites Quantifying language models’ sensitivity to spu- rious features in prompt design or: How i learned to start worrying about prompt formatting.

A Sequential Optimal Learning Approach to Automated Prompt Engineering in Large Language Models Quantifying language models’ sensitivity to spu- rious features in prompt design or: How i learned to start worrying about prompt formatting

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:59.181966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b5a37a9c-27ba-4c0f-a295-cf2efd6c3ee9 · outbound

This paper cites Efficient Prompt Optimization Through the Lens of Best Arm Identification.

A Sequential Optimal Learning Approach to Automated Prompt Engineering in Large Language Models Efficient Prompt Optimization Through the Lens of Best Arm Identification

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-10T21:56:59.026152Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 790e5a9f-22c5-4599-8cbf-6daceda40e07 · outbound

This paper cites Toward human readable prompt tuning: Kubrick’s the shining is a good movie, and a good prompt too? In The 2023 Conference on Empirical Methods in Natural Language Processing, 2023.

A Sequential Optimal Learning Approach to Automated Prompt Engineering in Large Language Models Toward human readable prompt tuning: Kubrick’s the shining is a good movie, and a good prompt too? In The 2023 Conference on Empirical Methods in Natural Language Processing, 2023

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:59.174598Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c38e902d-f9b7-4fe0-b7e9-059f82a79338 · outbound

This paper cites AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts.

A Sequential Optimal Learning Approach to Automated Prompt Engineering in Large Language Models AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-10T21:56:59.031520Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e2e11fa-4ac8-48e1-a79f-c507a75362b3 · outbound

This paper cites Hard prompts made easy: Gradient-based discrete optimization for prompt tuning and discovery.

A Sequential Optimal Learning Approach to Automated Prompt Engineering in Large Language Models Hard prompts made easy: Gradient-based discrete optimization for prompt tuning and discovery

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:59.167145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation df378670-86d8-4c1d-b488-0881e3716239 · outbound

This paper cites Large language models are diverse role- players for summarization evaluation.

A Sequential Optimal Learning Approach to Automated Prompt Engineering in Large Language Models Large language models are diverse role- players for summarization evaluation

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:59.159960Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 847dd0e3-e9de-44b9-8ade-4eaca59c4392 · outbound

This paper cites Calibrate before use: Improving few- shot performance of language models.

A Sequential Optimal Learning Approach to Automated Prompt Engineering in Large Language Models Calibrate before use: Improving few- shot performance of language models

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:59.153131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 4a152f67-929c-485d-87ef-a0b5d7e4756a · outbound

This paper cites Large language models are human-level prompt engi- neers.

A Sequential Optimal Learning Approach to Automated Prompt Engineering in Large Language Models Large language models are human-level prompt engi- neers

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:59.145828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Pith citing papers

Observation f246afcf-f705-442f-af5e-dd6c204f4df2 · inbound

Evolutionary Computation and Large Language Models: A Survey of Methods, Synergies, and Applications cites this paper.

Evolutionary Computation and Large Language Models: A Survey of Methods, Synergies, and Applications A Sequential Optimal Learning Approach to Automated Prompt Engineering in Large Language Models

Reference 42

Resolution: verified exact; local_arxiv, observed 2026-08-07T15:14:16.309596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.