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Paper Citation Record · LEDGER

Learning To Retrieve Prompts for In-Context Learning

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 46 inbound Pith citation observations for arXiv:2112.08633.

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

pith.paper-citation-record.v1
2112.08633 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 46 of 46 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:11:44.755343Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T00:29:15.901260Z

Reference resolution

0 of 0 outbound references displayed

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External citation measurements

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Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 55038b63-2f20-4e73-82d5-d7003d441f05 · inbound

Rethinking the Role of Demonstrations: What Makes In-Context Learning Work? cites this paper.

Rethinking the Role of Demonstrations: What Makes In-Context Learning Work? Learning To Retrieve Prompts for In-Context Learning

Reference 236

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arxiv_id, observed 2026-05-15T09:51:46.841055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-05-15T09:51:46.701149Z digest=sha256:93d5ba408dfc22ea812af0b8179f16a853ca78a9193ed66710be0ee7807a0636

Observation 10b89b56-dc5c-4f78-a230-99b65d0d31de · inbound

REPLUG: Retrieval-Augmented Black-Box Language Models cites this paper.

REPLUG: Retrieval-Augmented Black-Box Language Models Learning To Retrieve Prompts for In-Context Learning

Reference 32

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arxiv_id, observed 2026-05-17T12:41:54.101006Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-05-17T12:41:53.833754Z digest=sha256:f0e4785d386ac883c8d0d1498f69a9578b405a22aaea8702360a561d9bde90bc

Observation 9f2c0869-3197-4ea8-bd82-0343c076cbec · inbound

A Comprehensive Overview of Large Language Models cites this paper.

A Comprehensive Overview of Large Language Models Learning To Retrieve Prompts for In-Context Learning

Reference 208

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arxiv_id, observed 2026-05-19T20:32:45.554373Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-19T20:28:38.900026Z digest=sha256:e8cb0b50c0aca7a1c97db1bc50f5f56595a3fbde67daf54091cd6a84321959a9

Observation 30d7e357-07bf-4551-afec-fc71f611ae7e · inbound

Cognitive Architectures for Language Agents cites this paper.

Cognitive Architectures for Language Agents Learning To Retrieve Prompts for In-Context Learning

Reference 68

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verified exact
arxiv_id, observed 2026-05-16T19:33:44.487771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-16T19:33:44.146134Z digest=sha256:a6660c9b4297ce019aeaf6265e177ff6e171437d13025d14fd74896b01623d84

Observation a3b44d7c-f80d-49cb-8a3f-0dfda1ba71ab · inbound

Dynamic Rewarding with Prompt Optimization Enables Tuning-free Self-Alignment of Language Models cites this paper.

Dynamic Rewarding with Prompt Optimization Enables Tuning-free Self-Alignment of Language Models Learning To Retrieve Prompts for In-Context Learning

Reference 2021

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-12T21:28:20.803482Z digest=sha256:eaca989161c67e75e348c48a91de2af1613a7a340926f2554768c3cf2bd1c6b0

Observation 53a3eb7e-80a9-4367-b3e5-885845a6783f · inbound

Detecting Multi-Parameter Constraint Inconsistencies in Python Data Science Libraries cites this paper.

Detecting Multi-Parameter Constraint Inconsistencies in Python Data Science Libraries Learning To Retrieve Prompts for In-Context Learning

Reference 56

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source=pdf_text observed=2026-08-12T18:38:36.899309Z digest=sha256:cfae90795e2a72f4d0e9bfabd1e854c7e395b2a15f9855985c558c2470e759f1

Observation b8c425de-b99d-4d42-b227-4a89295de079 · inbound

Thinking Before Looking: Improving Multimodal LLM Reasoning via Mitigating Visual Hallucination cites this paper.

Thinking Before Looking: Improving Multimodal LLM Reasoning via Mitigating Visual Hallucination Learning To Retrieve Prompts for In-Context Learning

Reference 28

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source=pdf_text observed=2026-08-12T19:35:45.160409Z digest=sha256:9df1409aa4a0784446e8a9c158480dfd93636ee4f88999cb2a351d9a04bc06f8

Observation f6e09c54-bcc9-4ebe-baa6-61459512c9ea · inbound

Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations cites this paper.

Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations Learning To Retrieve Prompts for In-Context Learning

Reference 136

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source=pdf_text observed=2026-08-12T04:51:56.210504Z digest=sha256:0181e4ea9ab43f3f5b27110f6ffc5f32cfbaa99b449b58c34a1c2b0413c09c9f

Observation a2841dcd-05dd-4b57-bf7b-149d7c607ec0 · inbound

Does Few-Shot Learning Help LLM Performance in Code Synthesis? cites this paper.

Does Few-Shot Learning Help LLM Performance in Code Synthesis? Learning To Retrieve Prompts for In-Context Learning

Reference 51

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source=arxiv_source observed=2026-08-11T23:05:38.349467Z digest=sha256:b47b101bc29366540fffd83298ea6304af2de1a93a64856a158a16eb7023fdde

Observation 9ffb394f-821b-40a5-98ec-e2090f57f0ad · inbound

Improving Tool Retrieval by Leveraging Large Language Models for Query Generation cites this paper.

Improving Tool Retrieval by Leveraging Large Language Models for Query Generation Learning To Retrieve Prompts for In-Context Learning

Reference 15

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source=arxiv_source observed=2026-08-12T19:12:18.457785Z digest=sha256:9f6a8371123e7cc4e36b3369846ee355847f39b5751e8d346e72c2b8862a7605

Observation e6f6f6f0-1627-45ce-a051-7cd089d76537 · inbound

PromptRefine: Enhancing Few-Shot Performance on Low-Resource Indic Languages with Example Selection from Related Example Banks cites this paper.

PromptRefine: Enhancing Few-Shot Performance on Low-Resource Indic Languages with Example Selection from Related Example Banks Learning To Retrieve Prompts for In-Context Learning

Reference 39

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source=arxiv_source observed=2026-08-11T20:29:54.049141Z digest=sha256:c84891b4575c84c595903e2a9ca76cc64755bea08a5f7656fbf860403f43c701

Observation 80c3a78a-cd13-4992-9b25-0bfd4a574e6b · inbound

DRUM: Learning Demonstration Retriever for Large MUlti-modal Models cites this paper.

DRUM: Learning Demonstration Retriever for Large MUlti-modal Models Learning To Retrieve Prompts for In-Context Learning

Reference 31

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source=arxiv_source observed=2026-08-11T18:43:39.321834Z digest=sha256:b53e0fa9c036573a7d58d90e79cc334be8565f44241c4818bd0e4d4b211e9563

Observation 17eb3779-3389-45d8-92f7-844cfab44168 · inbound

System-2 Mathematical Reasoning via Enriched Instruction Tuning cites this paper.

System-2 Mathematical Reasoning via Enriched Instruction Tuning Learning To Retrieve Prompts for In-Context Learning

Reference 37

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source=arxiv_source observed=2026-08-11T05:58:55.113484Z digest=sha256:f7aaa9c598001dc3af94a7668cf95e4679654c61ed1caa76c30fef1381940a88

Observation 37ec5fe8-e364-4676-9a39-26ec7b8a2e47 · inbound

Exploring Information Processing in Large Language Models: Insights from Information Bottleneck Theory cites this paper.

Exploring Information Processing in Large Language Models: Insights from Information Bottleneck Theory Learning To Retrieve Prompts for In-Context Learning

Reference 11

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source=pdf_text observed=2026-08-10T22:43:11.717594Z digest=sha256:5c4677dd212914815f1dd843386e851bc46a44c10b326fdc84c7b2a0629e0e6b

Observation 9ce1b31a-f8b2-4bfa-9cf2-bfe1d3e01825 · inbound

Refactoring for Dockerfile Quality: A Dive into Developer Practices and Automation Potential cites this paper.

Refactoring for Dockerfile Quality: A Dive into Developer Practices and Automation Potential Learning To Retrieve Prompts for In-Context Learning

Reference 18

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source=pdf_text observed=2026-08-10T15:26:01.447434Z digest=sha256:e418bd940bafe2501fc19178ea1dcc64e9663c6d6b7960420e7da8953dbd87fc

Observation bb484e09-d461-4392-836c-a372233b6371 · inbound

PARA: Parameter-Efficient Fine-tuning with Prompt Aware Representation Adjustment cites this paper.

PARA: Parameter-Efficient Fine-tuning with Prompt Aware Representation Adjustment Learning To Retrieve Prompts for In-Context Learning

Reference 33

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no resolver link, observed 2026-08-09T16:56:09.527483Z

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source=arxiv_source observed=2026-08-09T16:56:09.527483Z digest=sha256:6dc5bf1f741ad41ebf381e0f2226fcb0351514b8bc57132b72ac1b3ca9ebe838

Observation 9a879c29-553e-4627-897f-0e56e7548acc · inbound

Investigating the Zone of Proximal Development of Language Models for In-Context Learning cites this paper.

Investigating the Zone of Proximal Development of Language Models for In-Context Learning Learning To Retrieve Prompts for In-Context Learning

Reference 32

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source=arxiv_source observed=2026-08-08T14:11:54.591248Z digest=sha256:8dd9fab7bef7c7fc00bc09bba30f12206056e91431677cc44611cd2c37e1fa91

Observation 98cd5e54-aade-4066-aaf2-18f2aaaba7f7 · inbound

From Promising Capability to Pervasive Bias: Assessing Large Language Models for Emergency Department Triage cites this paper.

From Promising Capability to Pervasive Bias: Assessing Large Language Models for Emergency Department Triage Learning To Retrieve Prompts for In-Context Learning

Reference 38

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source=pdf_text observed=2026-08-16T11:11:44.755343Z digest=sha256:2ee34598e515ba698e3abcc20d40abc7a07042a4685d0218eb1c08bcd560464e

Observation c7dfc1e8-b21a-4144-944f-1d9647047035 · inbound

E-InMeMo: Enhanced Prompting for Visual In-Context Learning cites this paper.

E-InMeMo: Enhanced Prompting for Visual In-Context Learning Learning To Retrieve Prompts for In-Context Learning

Reference 6

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source=pdf_text observed=2026-08-16T10:29:47.355204Z digest=sha256:56c08070cc96e4e8308bd0c61d1feae61a1531f0e2eb55c2a4139c9f1f1268e8

Observation ec5cb27e-bdf4-44ae-8b03-3ce2be3f6fe5 · inbound

CDE-Mapper: Using Retrieval-Augmented Language Models for Linking Clinical Data Elements to Controlled Vocabularies cites this paper.

CDE-Mapper: Using Retrieval-Augmented Language Models for Linking Clinical Data Elements to Controlled Vocabularies Learning To Retrieve Prompts for In-Context Learning

Reference 19

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source=pdf_text observed=2026-08-15T23:35:53.441767Z digest=sha256:b963134fed97882ee651a2bbfe460339bc9b4caa661951e9ad1713fb2ceea4af

Observation 95448844-7ab2-4a6b-ac3e-dc7ccc3b023e · inbound

DCG-SQL: Enhancing In-Context Learning for Text-to-SQL with Deep Contextual Schema Link Graph cites this paper.

DCG-SQL: Enhancing In-Context Learning for Text-to-SQL with Deep Contextual Schema Link Graph Learning To Retrieve Prompts for In-Context Learning

Reference 32

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source=arxiv_source observed=2026-08-07T14:09:05.294751Z digest=sha256:c21f89ec25d46beb62a6ecf50200ec7bc4d234fbbefb3e603f9797fcb6783c00

Observation 6497cf4c-a99d-47f7-9e88-f15eea434867 · inbound

Learning to Select In-Context Demonstration Preferred by Large Language Model cites this paper.

Learning to Select In-Context Demonstration Preferred by Large Language Model Learning To Retrieve Prompts for In-Context Learning

Reference 40

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no resolver link, observed 2026-08-07T14:09:24.905579Z

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source=arxiv_source observed=2026-08-07T14:09:24.905579Z digest=sha256:fb19a27bcd4427f9c341b128184a6c3010473fa4422556d2d33c411b6f4da6a3

Observation f8bf5af1-1599-4164-a1b7-7758cc85e4b2 · inbound

Control-R: Towards controllable test-time scaling cites this paper.

Control-R: Towards controllable test-time scaling Learning To Retrieve Prompts for In-Context Learning

Reference 2021

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source=pdf_text observed=2026-08-07T12:14:20.951963Z digest=sha256:20293a10a759a518ca0d4dabe0b1f1155f43a9ec9588955e806c800e1e56791b

Observation d76af8e4-aafd-4869-9b31-541310841d09 · inbound

ConText: Driving In-context Learning for Text Removal and Segmentation cites this paper.

ConText: Driving In-context Learning for Text Removal and Segmentation Learning To Retrieve Prompts for In-Context Learning

Reference 54

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no resolver link, observed 2026-08-07T11:01:11.480737Z

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source=arxiv_source observed=2026-08-07T11:01:11.480737Z digest=sha256:726cedaa46c5c9136690614e1940ecb3d328f3290f559ae4b8219f6d9c9f26f2

Observation 20c64c64-1063-4123-8313-09a6c4885605 · inbound

Mobile Traffic Prediction using LLMs with Efficient In-context Demonstration Selection cites this paper.

Mobile Traffic Prediction using LLMs with Efficient In-context Demonstration Selection Learning To Retrieve Prompts for In-Context Learning

Reference 33

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source=pdf_text observed=2026-08-07T10:28:42.847907Z digest=sha256:495e672f98149057d622992f71f637395a09ed39784d585b3a95e402db0c00f9

Observation 2b9aafad-f054-4ae3-96bd-d83281a2ade0 · inbound

Maximally-Informative Retrieval for State Space Model Generation cites this paper.

Maximally-Informative Retrieval for State Space Model Generation Learning To Retrieve Prompts for In-Context Learning

Reference 25

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source=pdf_text observed=2026-08-07T01:03:46.372538Z digest=sha256:a7317b41ae47e217f7aeec208b2dc603bf8f1d90080c124a2e889266a837ec9a

Observation d5e857ef-d866-49b0-896e-b6354ac4c640 · inbound

Relic: Enhancing Reward Model Generalization for Low-Resource Indic Languages with Few-Shot Examples cites this paper.

Relic: Enhancing Reward Model Generalization for Low-Resource Indic Languages with Few-Shot Examples Learning To Retrieve Prompts for In-Context Learning

Reference 36

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source=arxiv_source observed=2026-08-15T19:32:11.872431Z digest=sha256:8c9e0011242c5e36b5454259576128f6723a0f28802ead33b061195c9abd74c1

Observation a0a682a3-7131-445a-87e9-d9ae8de85397 · inbound

The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future cites this paper.

The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future Learning To Retrieve Prompts for In-Context Learning

Reference 44

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source=pdf_text observed=2026-08-15T19:07:06.845634Z digest=sha256:55a87d8065637b8a2a4dcf6e6ceb3d7ee8da8b9f52b384812c2e7e772fa15889

Observation d99dce78-33cb-4210-a76d-f07bd6333dce · inbound

Prompt Engineering for Requirements Engineering: A Literature Review and Roadmap cites this paper.

Prompt Engineering for Requirements Engineering: A Literature Review and Roadmap Learning To Retrieve Prompts for In-Context Learning

Reference 26

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source=pdf_text observed=2026-08-06T18:40:04.409141Z digest=sha256:817977ca3a0b86611f2c9e4e97965a7819d3c156a0f8326449e5f9670640adef

Observation c9ba00da-4994-472d-96e1-fce69dad8429 · inbound

Unveiling Effective In-Context Configurations for Image Captioning: An External & Internal Analysis cites this paper.

Unveiling Effective In-Context Configurations for Image Captioning: An External & Internal Analysis Learning To Retrieve Prompts for In-Context Learning

Reference 11

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source=pdf_text observed=2026-08-06T19:21:13.290224Z digest=sha256:f4caf4126606d8dfe87b3657efa8517f8552dfa8c670bed02fdaa9e9dc84901a

Observation cf50ee82-a89d-49c5-a8ac-35045c61b7ad · inbound

Failures Are the Stepping Stones to Success: Enhancing Few-Shot In-Context Learning by Leveraging Negative Samples cites this paper.

Failures Are the Stepping Stones to Success: Enhancing Few-Shot In-Context Learning by Leveraging Negative Samples Learning To Retrieve Prompts for In-Context Learning

Reference 22

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source=pdf_text observed=2026-08-06T11:01:54.569887Z digest=sha256:7f601fea0191568ff447bb9ceb5adca9b619d7663379037d8ca8ba4ac91eaf14

Observation 70a1910d-baf9-4567-8f56-75bf46551cbf · inbound

DICE: Dynamic In-Context Example Selection in LLM Agents via Efficient Knowledge Transfer cites this paper.

DICE: Dynamic In-Context Example Selection in LLM Agents via Efficient Knowledge Transfer Learning To Retrieve Prompts for In-Context Learning

Reference 30

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source=pdf_text observed=2026-08-06T10:43:49.140696Z digest=sha256:b59e327720165449489e4f1354a854f0051f3eb625fa4ab5d58b04d926a71ba0

Observation 27ca42a2-efbc-4b65-9387-3f6b144a30f7 · inbound

ICM-Fusion: In-Context Meta-Optimized LoRA Fusion for Multi-Task Adaptation cites this paper.

ICM-Fusion: In-Context Meta-Optimized LoRA Fusion for Multi-Task Adaptation Learning To Retrieve Prompts for In-Context Learning

Reference 40

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source=arxiv_source observed=2026-08-06T00:55:59.253741Z digest=sha256:088729b8e086798d09084f5cb1bb27b88679c4ed8e33024fd51b3fba1487bdcd

Observation 050e2942-5ebe-43e6-8be5-c6b8d60d8e73 · inbound

Human-in-Context: Unified Cross-Domain 3D Human Motion Modeling via In-Context Learning cites this paper.

Human-in-Context: Unified Cross-Domain 3D Human Motion Modeling via In-Context Learning Learning To Retrieve Prompts for In-Context Learning

Reference 81

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source=pdf_text observed=2026-08-15T17:36:33.167288Z digest=sha256:a215d2f348e3ca5d66babe980f3d82beb04b1025d37584dddba4a4c47c0721e0

Observation a2e624ae-b3e2-47dd-af59-528ffff44133 · inbound

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity cites this paper.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity Learning To Retrieve Prompts for In-Context Learning

Reference 9

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source=pdf_text observed=2026-08-05T14:44:28.004611Z digest=sha256:1676fcc67b17592204288051442ba3e6be88b80094471e37b5a34509f3510c7b

Observation 5210e8c4-d998-4f3a-b071-d7b17369784b · inbound

Context Engineering for Trustworthiness: Rescorla Wagner Steering Under Mixed and Inappropriate Contexts cites this paper.

Context Engineering for Trustworthiness: Rescorla Wagner Steering Under Mixed and Inappropriate Contexts Learning To Retrieve Prompts for In-Context Learning

Reference 2022

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source=pdf_text observed=2026-08-05T12:14:09.492335Z digest=sha256:1de08d04de6a30dd3b8a307bacb7d88f8dd8cd4aa6237036b1bd433bf3fefb4c

Observation ab8ca629-6982-459c-beff-f01a7fe2aa1d · inbound

MicLog: Towards Accurate and Efficient LLM-based Log Parsing via Progressive Meta In-Context Learning cites this paper.

MicLog: Towards Accurate and Efficient LLM-based Log Parsing via Progressive Meta In-Context Learning Learning To Retrieve Prompts for In-Context Learning

Reference 39

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unresolved
no resolver link, observed 2026-08-03T11:17:08.489749Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T11:17:08.489749Z digest=sha256:a81514143e00576067d1cedcd827c81d05ad41862a67a944149109816265a7a0

Observation 9b1246d3-ca2b-4fae-9a5c-2a5ceaf75dda · inbound

Learning to Select Visual In-Context Demonstrations cites this paper.

Learning to Select Visual In-Context Demonstrations Learning To Retrieve Prompts for In-Context Learning

Reference 31

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unresolved
no resolver link, observed 2026-08-04T05:46:07.412855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T05:46:07.412855Z digest=sha256:78a6e452d7753f72c5e4500fba1f71a257b022665e5c9bcef9942c61eeb99146

Observation 054f281a-1581-4d4e-93c4-b26d71bece85 · inbound

Towards Effective In-context Cross-domain Knowledge Transfer via Domain-invariant-neurons-based Retrieval cites this paper.

Towards Effective In-context Cross-domain Knowledge Transfer via Domain-invariant-neurons-based Retrieval Learning To Retrieve Prompts for In-Context Learning

Reference 5

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metadata mismatch
arxiv_id, observed 2026-05-10T22:50:49.968284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-10T19:31:53.895388Z digest=sha256:e6478a4fe885aad53532be44afd30fd6dedbb0a1699b0a1fb1040ca3fe195c38

Observation ad53a678-a2d0-4081-821b-1ac42069ee96 · inbound

Reason Analogically via Cross-domain Prior Knowledge: An Empirical Study of Cross-domain Knowledge Transfer for In-Context Learning cites this paper.

Reason Analogically via Cross-domain Prior Knowledge: An Empirical Study of Cross-domain Knowledge Transfer for In-Context Learning Learning To Retrieve Prompts for In-Context Learning

Reference 5

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metadata mismatch
arxiv_id, observed 2026-05-10T23:10:49.975472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-10T19:19:47.474410Z digest=sha256:dbd2ef112f1fbd3c74f522ff5017c4420540d29e99029d6d1ad4fea9c2a7286c

Observation 02f032ba-3793-4fcc-8ef1-19a0967c2abc · inbound

Supplement Generation Training for Enhancing Agentic Task Performance cites this paper.

Supplement Generation Training for Enhancing Agentic Task Performance Learning To Retrieve Prompts for In-Context Learning

Reference 32

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metadata mismatch
arxiv_id, observed 2026-05-10T00:59:49.539205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-05-10T00:58:27.655909Z digest=sha256:8f908610811275918242f2fe13b1ecbe56337af863a653f830459185487e4b26

Observation 4ed25435-fff1-489d-a903-75a1652d4837 · inbound

Decompose and Recompose: Reasoning New Skills from Existing Abilities for Cross-Task Robotic Manipulation cites this paper.

Decompose and Recompose: Reasoning New Skills from Existing Abilities for Cross-Task Robotic Manipulation Learning To Retrieve Prompts for In-Context Learning

Reference 18

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metadata mismatch
arxiv_id, observed 2026-05-11T16:56:06.935153Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-09T14:30:00.890117Z digest=sha256:cd82643798841aa3a4a6293dd992647a36b0f1eb5848cbe6a37f528ca0faaef9

Observation 73116037-2264-4055-9562-0a4fdfe3cc05 · inbound

Rethinking Data Curation in LLM Training: Online Reweighting Offers Better Generalization than Offline Methods cites this paper.

Rethinking Data Curation in LLM Training: Online Reweighting Offers Better Generalization than Offline Methods Learning To Retrieve Prompts for In-Context Learning

Reference 36

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metadata mismatch
arxiv_id, observed 2026-05-10T07:11:53.395139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-10T07:09:21.652035Z digest=sha256:95bc74048b0839e53bd3a1af677638a5928eadbd0f77c2908a84b1e02d8ddb3f

Observation d1fa87e3-a775-4014-a724-1da2662c9c98 · inbound

BCL: Bayesian In-Context Learning Framework for Information Extraction cites this paper.

BCL: Bayesian In-Context Learning Framework for Information Extraction Learning To Retrieve Prompts for In-Context Learning

Reference 50

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metadata mismatch
arxiv_id, observed 2026-07-04T00:29:15.903955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-06-26T21:11:23.042546Z digest=sha256:8c46d1affe3eb1a42f8bbf711badab09424ca61f2485b435947adfed4e954b9d

Observation d18705d0-4fd7-4f03-a0c1-4005b8a8159e · inbound

Neuron-Aware Active Few-Shot Learning for LLMs cites this paper.

Neuron-Aware Active Few-Shot Learning for LLMs Learning To Retrieve Prompts for In-Context Learning

Reference 13

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metadata mismatch
arxiv_id, observed 2026-07-03T16:28:38.234706Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-07-03T16:27:57.985267Z digest=sha256:f2018e024e69a1d0685d3dfa382c32376b875be1388e3b096d3c768444b8827e

Observation af5f4225-26c2-4989-933c-6ab976174797 · inbound

ThinkRetrieve: Retrieval-Augmented Reasoning Traces for Test-Time Scaling cites this paper.

ThinkRetrieve: Retrieval-Augmented Reasoning Traces for Test-Time Scaling Learning To Retrieve Prompts for In-Context Learning

Reference 42

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unresolved
no resolver link, observed 2026-08-12T14:10:44.989481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T14:10:44.989481Z digest=sha256:0f2927232c0a5b1d6277a0eef0c74d8bf23dd7de2ab32789fe74f105e82d06fb