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Paper Citation Record · LEDGER

Prompt Engineering a Prompt Engineer

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 22 inbound Pith citation observations for arXiv:2311.05661.

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

pith.paper-citation-record.v1
2311.05661 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 22 of 22 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T15:02:32.226115Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

12
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 463e57a2-e4c7-4d05-b210-af1acaecb048 · inbound

TextGrad: Automatic "Differentiation" via Text cites this paper.

TextGrad: Automatic "Differentiation" via Text Prompt Engineering a Prompt Engineer

Reference 71

Resolution
verified exact
arxiv_id, observed 2026-05-13T11:27:58.257780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-13T11:27:58.098484Z digest=sha256:82da2756e05f113116e2d4a9ebb294e8ae5fb027850a83f65d29249818359b59

Observation d51d5bf3-521f-4639-bec5-d9930d0682bf · inbound

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning cites this paper.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Prompt Engineering a Prompt Engineer

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-12T15:02:32.226115Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:02:32.226115Z digest=sha256:237ce1d008363d8e9ef6fa8cba6ef217175450360ffceb14b9c7e798242f412b

Observation fa32c350-447f-4d9f-bb6f-bfe65b7c1704 · inbound

GReaTer: Gradients over Reasoning Makes Smaller Language Models Strong Prompt Optimizers cites this paper.

GReaTer: Gradients over Reasoning Makes Smaller Language Models Strong Prompt Optimizers Prompt Engineering a Prompt Engineer

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-11T16:52:28.898335Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T16:52:28.898335Z digest=sha256:d50e87db293048b3ed9598c71f5e124f3b6231f4fa37e999466bf8a43c0a38c2

Observation ac1b34fa-fee3-4217-8af0-4087e76eeaaf · inbound

Aviary: training language agents on challenging scientific tasks cites this paper.

Aviary: training language agents on challenging scientific tasks Prompt Engineering a Prompt Engineer

Reference 78

Resolution
unresolved
no resolver link, observed 2026-08-10T23:07:33.768523Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T23:07:33.768523Z digest=sha256:c542d3d2ea2001392b98135c9bc6b0c248e324dcb4538785684a93d9de073857

Observation 7aa126b7-83df-4a59-a08a-012f1b288dfd · inbound

Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization cites this paper.

Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization Prompt Engineering a Prompt Engineer

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-10T21:23:30.377477Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T21:23:30.377477Z digest=sha256:fec43d0fc0340cdd5cab1e5483c79c070ed35a2a5d0aea18d8b67fe24c626025

Observation 48bd574a-f133-46a3-a46f-b376c212ff4a · inbound

TAPO: Task-Referenced Adaptation for Prompt Optimization cites this paper.

TAPO: Task-Referenced Adaptation for Prompt Optimization Prompt Engineering a Prompt Engineer

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-10T20:57:57.269171Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:57:57.269171Z digest=sha256:371af6700d19f04af1ead93c2e3b8451395152b2238a16a20666945e0806ed43

Observation f022fdee-9d13-466c-b0c4-f8b1d8a09475 · 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 Prompt Engineering a Prompt Engineer

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-07T15:14:06.869855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:14:06.869855Z digest=sha256:1f8f2bed31c786e8b2acea0673c5f37b3b1b88db0d3b2ad0d0a18a681d07e748

Observation ca4836d0-f800-4b1a-b025-9a13bc96e4e0 · inbound

Small Language Models in the Real World: Insights from Industrial Text Classification cites this paper.

Small Language Models in the Real World: Insights from Industrial Text Classification Prompt Engineering a Prompt Engineer

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-07T15:09:56.120237Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:09:56.120237Z digest=sha256:38b680a4c94505bd35ed54c681e069fd35abd6ab3ea8901620bf7f63dd0e3579

Observation bd0449fd-ae43-409c-b0af-b657332086fc · inbound

SI-Agent: An Agentic Framework for Feedback-Driven Generation and Tuning of Human-Readable System Instructions for Large Language Models cites this paper.

SI-Agent: An Agentic Framework for Feedback-Driven Generation and Tuning of Human-Readable System Instructions for Large Language Models Prompt Engineering a Prompt Engineer

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-06T20:21:21.671226Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:21:21.671226Z digest=sha256:83fff5480e4bbdcd7d152974c4e04a76aa510d136b11c3704314de146878f7ee

Observation 3eb8478f-a9a3-420a-96a5-43afa2ff34ae · inbound

Prompt Smart, Pay Less: Cost-Aware APO for Real-World Applications cites this paper.

Prompt Smart, Pay Less: Cost-Aware APO for Real-World Applications Prompt Engineering a Prompt Engineer

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-06T16:11:26.684587Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:11:26.684587Z digest=sha256:84abcb5c0a7bf8d921e6a1b1b70e430d86cd9d9a12cc1097feade1e1d0d70084

Observation 1cf09c6b-b735-4ac9-9649-bb4cdb4a68c4 · inbound

Retrieval augmented generation based dynamic prompting for few-shot biomedical named entity recognition using large language models cites this paper.

Retrieval augmented generation based dynamic prompting for few-shot biomedical named entity recognition using large language models Prompt Engineering a Prompt Engineer

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-06T14:14:25.414332Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T14:14:25.414332Z digest=sha256:13c727c14290c409c5a8f36393bc770becc3ce93c2f1f7e1306e65e9e2b063e5

Observation cf5ee585-a895-4200-bbb4-7ac01dbecddb · inbound

A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems cites this paper.

A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems Prompt Engineering a Prompt Engineer

Reference 112

Resolution
verified exact
arxiv_id, observed 2026-05-15T23:21:42.235280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-15T23:21:42.029285Z digest=sha256:17213f3537607d46cee9596a1bbed3766b7b6aa66d58cbec06f120a34c868978

Observation f8419caf-a9fe-4909-92fc-e09ff16672e4 · inbound

The Prompt Engineering Report Distilled: Quick Start Guide for Life Sciences cites this paper.

The Prompt Engineering Report Distilled: Quick Start Guide for Life Sciences Prompt Engineering a Prompt Engineer

Reference 138

Resolution
verified exact
arxiv_id, observed 2026-05-18T16:41:38.035596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-18T16:39:03.794436Z digest=sha256:76c7ece9ac7b639898d07af251796811b906c419840221f3bf2c43bb72018fbc

Observation 7b392431-5235-47b6-adfd-cd7a0f9b652e · inbound

The Prompt Engineering Report Distilled: Quick Start Guide for Life Sciences cites this paper.

The Prompt Engineering Report Distilled: Quick Start Guide for Life Sciences Prompt Engineering a Prompt Engineer

Reference 139

Resolution
metadata mismatch
arxiv_id, observed 2026-05-18T16:41:37.410874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-18T16:39:03.794436Z digest=sha256:273da0ad4be51775bf83e4adbade4ca744d136642d8d35fb631edbe1a7d8f7f0

Observation cd96a31f-56a3-4db9-b44c-1f58e95fe128 · inbound

PIAST: Rapid Prompting with In-context Augmentation for Scarce Training data cites this paper.

PIAST: Rapid Prompting with In-context Augmentation for Scarce Training data Prompt Engineering a Prompt Engineer

Reference 26

Resolution
verified exact
arxiv_id, observed 2026-05-16T23:18:40.144110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-16T23:15:52.444217Z digest=sha256:a5a749d2039d68d66ed21429cfca1bbdf153a67fe428e0ecf1822600b1e401a9

Observation 8a6cacef-4389-4f29-8bf2-827358114d2c · inbound

Memory in the Age of AI Agents cites this paper.

Memory in the Age of AI Agents Prompt Engineering a Prompt Engineer

Reference 105

Resolution
verified exact
arxiv_id, observed 2026-05-11T18:18:20.477520Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-05-11T18:18:19.911342Z digest=sha256:098f8577fff35b28a40fd884cb215f3dafd5a99ccd628b6c0625fec3e7143759

Observation dbc03b97-0f4c-4cf5-9dc3-9c511e6876a0 · inbound

Towards Efficient and Robust Linguistic Emotion Diagnosis for Mental Health via Multi-Agent Instruction Refinement cites this paper.

Towards Efficient and Robust Linguistic Emotion Diagnosis for Mental Health via Multi-Agent Instruction Refinement Prompt Engineering a Prompt Engineer

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-03T09:35:18.822942Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:35:18.822942Z digest=sha256:df4593db4fc5237a6378801790bf15562dea83cd005ab89d82af8af35893e097

Observation 996b7447-606f-4e49-ab2f-9ca56d26a8bd · inbound

ISTQB Certifications Under the Lens: Their Contributions to the Software-Testing Profession; and AI-assisted Synthesis of Practitioners' Endorsements and Criticisms cites this paper.

ISTQB Certifications Under the Lens: Their Contributions to the Software-Testing Profession; and AI-assisted Synthesis of Practitioners' Endorsements and Criticisms Prompt Engineering a Prompt Engineer

Reference 46

Resolution
unresolved
no resolver link, observed 2026-07-14T21:06:47.709726Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T21:06:47.709726Z digest=sha256:12fc731d6b9e51c4ad7855fa4a47ad6de34f71abf08b3ce39ca506d51dadbfb6

Observation 4c502048-7a86-492a-a626-b53dca458a3e · inbound

Dynamic Infilling Anchors for Format-Constrained Generation in Diffusion Large Language Models cites this paper.

Dynamic Infilling Anchors for Format-Constrained Generation in Diffusion Large Language Models Prompt Engineering a Prompt Engineer

Reference 81

Resolution
verified exact
arxiv_id, observed 2026-07-02T08:06:48.412250Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-06-28T06:20:27.041099Z digest=sha256:d8a1e6de6a781b1b6c49b6322577063f13911063af5e660806cc5947e82d7af1

Observation 2d3bd371-bee8-4126-88b5-b63938c5e0c8 · inbound

LLM4MTLs: Automated Generation and Empirical Evaluation of Model Transformation Languages cites this paper.

LLM4MTLs: Automated Generation and Empirical Evaluation of Model Transformation Languages Prompt Engineering a Prompt Engineer

Reference 51

Resolution
verified exact
arxiv_id, observed 2026-07-04T18:40:03.096383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-06-25T22:38:37.806421Z digest=sha256:b4f21e432a5e1b0fecaf1cddf1142931d2eaba847afe7cfea78a5cd61c51dad3

Observation 40d491da-70d9-49e5-97bb-5920d3259d98 · inbound

A French OSCE Dialogue Dataset and Controllable Virtual Patient System for Clinical Training cites this paper.

A French OSCE Dialogue Dataset and Controllable Virtual Patient System for Clinical Training Prompt Engineering a Prompt Engineer

Reference 58

Resolution
verified exact
arxiv_id, observed 2026-06-30T01:34:09.821404Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-06-30T01:29:19.877774Z digest=sha256:7b63a9f4d88857c36488d7ef538f0680d6d82a860d86c46fad512da0dd61e487

Observation e8eab3b0-1e9c-471a-948f-c91874214ba7 · inbound

BT-APE: A Computationally Light Backtracking Approach to Automatic Prompt Engineering for Requirements Classification cites this paper.

BT-APE: A Computationally Light Backtracking Approach to Automatic Prompt Engineering for Requirements Classification Prompt Engineering a Prompt Engineer

Reference 53

Resolution
verified exact
arxiv_id, observed 2026-07-02T09:16:48.653833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-07-02T09:12:59.039529Z digest=sha256:369a29c1c521de06a517b65e9d06b4610b765c8328785df3aa284a1d72fd5f5c