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Paper Citation Record · LEDGER

Efficient Prompting Methods for Large Language Models: A Survey

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 17 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 17 of 17 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 17 of 17 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T13:02:53.117215Z

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

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  • verified fuzzy0
  • unresolved0
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  • malformed identifier0
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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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-23T19:07:21.016824Z digest=sha256:fa9f2bc910bf7359e23f2499f7a00d336897ac3659a0990701818810c7448718

Observation 801e1e14-6cfa-4c15-8837-ade8c9730517 · inbound

When Dimensionality Hurts: The Role of LLM Embedding Compression for Noisy Regression Tasks cites this paper.

When Dimensionality Hurts: The Role of LLM Embedding Compression for Noisy Regression Tasks Efficient Prompting Methods for Large Language Models: A Survey

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-09T13:02:53.117215Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T13:02:53.117215Z digest=sha256:99cd2459d216c1dcff21b5a2d040f566377c6a2698cfe23c8145de7733978f8d

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-23T01:59:18.546405Z digest=sha256:e715b92341041315a9747dc4a3445d7a79201aeb17f2c3b0ab25a155ea6b360f

Observation a60ca776-5527-44d1-9f42-342092d3dbba · inbound

Beyond Hard and Soft: Hybrid Context Compression for Balancing Local and Global Information Retention cites this paper.

Beyond Hard and Soft: Hybrid Context Compression for Balancing Local and Global Information Retention Efficient Prompting Methods for Large Language Models: A Survey

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-07T15:15:52.359951Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:15:52.359951Z digest=sha256:359ea8aa11cbc5180068667a1e0f5bcff1e4c98bbdf35b42a6fe43df85055b2b

Observation d7e33eb9-ae13-4d9e-abf5-7371e8ae0dae · inbound

Improved Representation Steering for Language Models cites this paper.

Improved Representation Steering for Language Models Efficient Prompting Methods for Large Language Models: A Survey

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-07T13:51:28.142183Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:51:28.142183Z digest=sha256:5b07f7292461ae8dda48acd8577d42c4fab02ed00e5dd3e08487d53716f07134

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.

source=pdf_text observed=2026-08-06T23:59:04.118839Z digest=sha256:bad48bde3913019533a3f5cc22e6bb983b52b63f7e8e8d60489db4ce91332c30

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.

source=pdf_text observed=2026-08-06T23:21:35.925270Z digest=sha256:a3a3a839edea2e35cc1bb854ee7e754ebbc60028cf23e469e3e04ef11df8740e

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.

source=pdf_text observed=2026-08-06T20:21:21.987576Z digest=sha256:be1211621ecc09b6c5eb0c70ad57e0be11f03c6e859d217e9928b5cbd1cb7af3

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.

source=arxiv_source observed=2026-08-06T15:37:24.863525Z digest=sha256:86bfebb6a14a96bef1ead384edf2d7b246d4b1004e140b532319d550436fdb8c

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-18T05:59:00.400429Z digest=sha256:695387de051892f70440236e3e2db3eea24e0f23cc6f885f268510b3650ad0e0

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.

source=pdf_text observed=2026-08-03T20:29:13.345645Z digest=sha256:4e3f3cbd0bc1b19c47528fef031af357afc91945cd888d69a9c63e43dd50697d

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.

source=pdf_text observed=2026-08-03T09:35:18.428819Z digest=sha256:e8cd6dad9d3ed83669e6ab8a1887858fdd97782248b0e6eb9ddd5fa18056e127

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 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-08T09:24:27.977204Z digest=sha256:ec0ea2585a07c9b0d762aae3e1a42014296238d8331d3fc9f5f5f91c6f230599

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 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-20T23:32:48.878074Z digest=sha256:8291d25d38f2d74d5ca683b1c0d3238297a7ea21d0479a85a4aa5f9d67c94a83

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-30T14:57:40.568265Z digest=sha256:f9a35c2bb7ecb60ce28fed1ad600cc99e60ce87088c43b7cdfa439c2df6d7044

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 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-06-28T06:39:17.268337Z digest=sha256:d7ed99660167d57820972f8ada18499af97d7ba6422819472653e1084711c9bc

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 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-06-26T09:15:50.722199Z digest=sha256:7ad3c4821299fd859a67dac17af6a293a884cfb5899693bfe419aeafea3ddaca