Paper Citation Record · LEDGER

Efficient Prompting Methods for Large Language Models: A Survey

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 inbound Pith citation observations for arXiv:2404.01077.

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

pith.paper-citation-record.v1
2404.01077 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 14 of 14 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:59:04.118839Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T09:59:45.539148Z

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 f934b144-9853-40ae-86b3-7094c03a3f45 · inbound

From Cool Demos to Production-Ready FMware: Core Challenges and a Technology Roadmap cites this paper.

From Cool Demos to Production-Ready FMware: Core Challenges and a Technology Roadmap Efficient Prompting Methods for Large Language Models: A Survey

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-23T19:08:20.945968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6e6e00ad-ff19-4ae1-a808-b64054722cf7 · inbound

CODEPROMPTZIP: Code-specific Prompt Compression for Retrieval-Augmented Generation in Coding Tasks with LMs cites this paper.

CODEPROMPTZIP: Code-specific Prompt Compression for Retrieval-Augmented Generation in Coding Tasks with LMs Efficient Prompting Methods for Large Language Models: A Survey

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-23T02:02:23.065100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 89b38246-6a44-40ee-8285-e49792d6e606 · inbound

MAARTA:Multi-Agentic Adaptive Radiology Teaching Assistant cites this paper.

MAARTA:Multi-Agentic Adaptive Radiology Teaching Assistant Efficient Prompting Methods for Large Language Models: A Survey

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T23:59:04.118839Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7fc4d2e9-804c-448a-a848-083968139365 · inbound

A Modular Taxonomy for Hate Speech Definitions and Its Impact on Zero-Shot LLM Classification Performance cites this paper.

A Modular Taxonomy for Hate Speech Definitions and Its Impact on Zero-Shot LLM Classification Performance Efficient Prompting Methods for Large Language Models: A Survey

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-06T23:21:35.925270Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5fca162a-3463-441f-8eb9-a4c88a2b70b0 · 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 Efficient Prompting Methods for Large Language Models: A Survey

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T20:21:21.987576Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0888cbf8-91aa-4517-a4cf-70e55c111605 · inbound

Step-level Verifier-guided Hybrid Test-Time Scaling for Large Language Models cites this paper.

Step-level Verifier-guided Hybrid Test-Time Scaling for Large Language Models Efficient Prompting Methods for Large Language Models: A Survey

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T15:37:24.863525Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f454de97-b3ee-4342-982d-cf58491fce0f · inbound

Assessing Coherency and Consistency of Code Execution Reasoning by Large Language Models cites this paper.

Assessing Coherency and Consistency of Code Execution Reasoning by Large Language Models Efficient Prompting Methods for Large Language Models: A Survey

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-18T06:00:57.265483Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6276c615-b950-4740-8e91-c94d9da3544e · inbound

Knowing How to Edit: Reliable Evaluation Signals for Diagnosing and Optimizing Prompts at Query Level cites this paper.

Knowing How to Edit: Reliable Evaluation Signals for Diagnosing and Optimizing Prompts at Query Level Efficient Prompting Methods for Large Language Models: A Survey

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-03T20:29:13.345645Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 44ac342f-3907-4345-818d-5e929550c85a · 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 Efficient Prompting Methods for Large Language Models: A Survey

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-03T09:35:18.428819Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c2d0ff71-b605-4517-bd5b-e4a14f828eab · inbound

PragLocker: Protecting Agent Intellectual Property in Untrusted Deployments via Non-Portable Prompts cites this paper.

PragLocker: Protecting Agent Intellectual Property in Untrusted Deployments via Non-Portable Prompts Efficient Prompting Methods for Large Language Models: A Survey

Reference 120

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:21:10.847340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ff6ee498-8da4-4b7c-bf8c-178e807aaf37 · inbound

PragLocker: Protecting Agent Intellectual Property in Untrusted Deployments via Non-Portable Prompts cites this paper.

PragLocker: Protecting Agent Intellectual Property in Untrusted Deployments via Non-Portable Prompts Efficient Prompting Methods for Large Language Models: A Survey

Reference 120

Resolution: verified exact; arxiv_id, observed 2026-05-20T23:33:50.569226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 145e7b85-72ff-48b1-8e19-bc0ca9040d65 · inbound

Understanding Conversational Patterns in Multi-agent Programming: A Case Study on Fibonacci Game Development cites this paper.

Understanding Conversational Patterns in Multi-agent Programming: A Case Study on Fibonacci Game Development Efficient Prompting Methods for Large Language Models: A Survey

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-06-30T15:04:46.355124Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation de4f85ee-2045-4e64-9417-a30294b2e586 · inbound

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

CRAFT: Cost-aware Refinement And Front-aware Tuning of Prompts Efficient Prompting Methods for Large Language Models: A Survey

Reference 220

Resolution: verified exact; arxiv_id, observed 2026-07-02T07:46:46.418282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d5647f70-ad13-453d-b760-939a6784146d · inbound

MAS-PromptBench: When Does Prompt Optimization Improve Multi-Agent LLM Systems? cites this paper.

MAS-PromptBench: When Does Prompt Optimization Improve Multi-Agent LLM Systems? Efficient Prompting Methods for Large Language Models: A Survey

Reference 45

Resolution: verified exact; arxiv_id, observed 2026-07-04T09:59:45.541220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.