Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity | Paper Citation Record

Observation 8823b28d-7bd5-49f0-8a46-935b0098b804 · inbound

Cross-Task Generalization via Natural Language Crowdsourcing Instructions cites this paper.

Cross-Task Generalization via Natural Language Crowdsourcing Instructions Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 97

Resolution: verified exact; arxiv_id, observed 2026-05-18T01:57:29.459734Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9b31c842-8ba8-4e00-acf4-ddce9e7c6ee0 · 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? Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 219

Resolution: verified exact; arxiv_id, observed 2026-05-15T09:51:46.867996Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 61373b27-64e3-4efe-8ae5-2875223f14c0 · inbound

OPT: Open Pre-trained Transformer Language Models cites this paper.

OPT: Open Pre-trained Transformer Language Models Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 300

Resolution: verified exact; arxiv_id, observed 2026-05-10T20:53:17.147333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d199b6c5-52bb-407b-b19d-74e56be0373e · inbound

Large Language Models Are Human-Level Prompt Engineers cites this paper.

Large Language Models Are Human-Level Prompt Engineers Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-24T09:43:26.436212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 406b0a85-918a-41c0-9434-985db23e4a34 · inbound

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

REPLUG: Retrieval-Augmented Black-Box Language Models Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 31

Resolution: metadata mismatch; arxiv_id, observed 2026-05-17T12:41:54.090375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation cc8aa427-c429-4aa0-ab67-bb2f2726c4b7 · inbound

A Survey on Multimodal Large Language Models cites this paper.

A Survey on Multimodal Large Language Models Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 173

Resolution: verified exact; arxiv_id, observed 2026-05-16T02:56:42.082763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b2966a9d-ee47-44b3-b31f-2b4f93893782 · inbound

Simple synthetic data reduces sycophancy in large language models cites this paper.

Simple synthetic data reduces sycophancy in large language models Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-16T14:48:08.613630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b977f68a-85a5-4d30-a1d5-a653cefa4adb · inbound

Large Language Models as Optimizers cites this paper.

Large Language Models as Optimizers Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-15T00:04:31.292337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 14c2d6a3-7291-4e88-ade2-344cbdc376f4 · inbound

UGMathBench: A Diverse and Dynamic Benchmark for Undergraduate-Level Mathematical Reasoning with Large Language Models cites this paper.

UGMathBench: A Diverse and Dynamic Benchmark for Undergraduate-Level Mathematical Reasoning with Large Language Models Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-10T15:40:39.727254Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 84970ba3-1d16-42c9-a581-ef888474d7ab · inbound

OptiSeq: Ordering Examples On-The-Fly for In-Context Learning cites this paper.

OptiSeq: Ordering Examples On-The-Fly for In-Context Learning Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-10T14:44:33.624762Z

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Unavailable: canonical work link unavailable.

Observation 4466abd0-8e18-4022-b9f4-262250e9da78 · inbound

StaICC: Standardized Evaluation for Classification Task in In-context Learning cites this paper.

StaICC: Standardized Evaluation for Classification Task in In-context Learning Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-10T14:06:14.365894Z

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Unavailable: canonical work link unavailable.

Observation 1e2df27d-6e39-4457-af29-dda49117160f · inbound

Label Anything: An Interpretable, High-Fidelity and Prompt-Free Annotator cites this paper.

Label Anything: An Interpretable, High-Fidelity and Prompt-Free Annotator Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-09T10:29:14.898680Z

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Observation 053c5857-31a0-43b4-bb52-3c877b440be8 · inbound

Hedging and Non-Affirmation: Quantifying LLM Alignment on Questions of Human Rights cites this paper.

Hedging and Non-Affirmation: Quantifying LLM Alignment on Questions of Human Rights Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-23T02:25:19.240859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 36da8c88-e6c4-4bb9-8a2f-87f5bef07f64 · inbound

A Survey of Scaling in Large Language Model Reasoning cites this paper.

A Survey of Scaling in Large Language Model Reasoning Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 126

Resolution: metadata mismatch; arxiv_id, observed 2026-05-22T21:22:08.881272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 94fa8163-c4d3-4191-bb24-5167f9610be8 · inbound

MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning cites this paper.

MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T15:09:44.074542Z

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Unavailable: canonical work link unavailable.

Observation 50cb13da-e179-4fa6-8988-c82e89e7f0e9 · inbound

Personalizing Student-Agent Interactions Using Log-Contextualized Retrieval-Augmented Generation (RAG) cites this paper.

Personalizing Student-Agent Interactions Using Log-Contextualized Retrieval-Augmented Generation (RAG) Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 17

Resolution: metadata mismatch; arxiv_id, observed 2026-05-22T13:01:34.313172Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 74447f7f-b3ad-428c-b8a8-a0e74910c951 · inbound

Recalibrating the Compass: Integrating Large Language Models into Classical Research Methods cites this paper.

Recalibrating the Compass: Integrating Large Language Models into Classical Research Methods Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T14:17:35.751572Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e401767f-6d2e-4966-82dc-b9e9234289df · inbound

The Role of Diversity in In-Context Learning for Large Language Models cites this paper.

The Role of Diversity in In-Context Learning for Large Language Models Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T14:21:37.503651Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e68473c-9269-4f8e-aed7-b43a9b6d44e6 · 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 Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T14:09:23.327777Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6478804e-696c-44ba-8834-95af48be77af · inbound

SV-TrustEval-C: Evaluating Structure and Semantic Reasoning in Large Language Models for Source Code Vulnerability Analysis cites this paper.

SV-TrustEval-C: Evaluating Structure and Semantic Reasoning in Large Language Models for Source Code Vulnerability Analysis Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-07T13:55:19.341254Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c80c705-bae6-4d13-92c7-4f5a9196cdfa · inbound

Adaptive Task Vectors for Large Language Models cites this paper.

Adaptive Task Vectors for Large Language Models Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T11:13:00.135436Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c0aaa8f4-3a91-48ec-bd60-2e6d8caaa537 · inbound

MesaNet: Sequence Modeling by Locally Optimal Test-Time Training cites this paper.

MesaNet: Sequence Modeling by Locally Optimal Test-Time Training Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-07T10:30:13.776560Z

Source-reported events for the cited work

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Observation 5dec3fb1-275b-453f-9942-c784217cfcd5 · inbound

Extending Epistemic Uncertainty Beyond Parameters Would Assist in Designing Reliable LLMs cites this paper.

Extending Epistemic Uncertainty Beyond Parameters Would Assist in Designing Reliable LLMs Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T05:41:36.377660Z

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Unavailable: canonical work link unavailable.

Observation ee0fcb41-a038-4742-940b-4121a9091338 · inbound

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models cites this paper.

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T05:25:30.388110Z

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Observation 5c6c06ce-24f4-4142-9437-0e89fdb880fc · inbound

RAILS: Retrieval-Augmented Intelligence for Learning Software Development cites this paper.

RAILS: Retrieval-Augmented Intelligence for Learning Software Development Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T22:04:05.177465Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 36376b3a-063f-4a71-a1a2-87009f16dc0b · inbound

Fine-tuning on simulated data outperforms prompting for agent tone of voice cites this paper.

Fine-tuning on simulated data outperforms prompting for agent tone of voice Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T19:42:01.929370Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 986e443e-6339-4439-b819-3f296cfc6c1f · 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 Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T19:21:13.309183Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 44a92604-5952-43f8-9f37-f07e9ace23df · inbound

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests cites this paper.

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-06T17:21:33.662178Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bc357808-fbfe-4ff9-9d76-f30aa6eff074 · inbound

LLMs are Bayesian, In Expectation, Not in Realization cites this paper.

LLMs are Bayesian, In Expectation, Not in Realization Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T17:10:01.933065Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 663f9b1d-843f-487b-9467-f4eb66cccf2e · inbound

Improving Data and Parameter Efficiency of Neural Language Models Using Representation Analysis cites this paper.

Improving Data and Parameter Efficiency of Neural Language Models Using Representation Analysis Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 128

Resolution: unresolved; no resolver link, observed 2026-08-06T17:03:44.621100Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f0e8e6e-cd91-4271-ac9d-d513b53a2bd2 · inbound

PromptSuite: A Task-Agnostic Framework for Multi-Prompt Generation cites this paper.

PromptSuite: A Task-Agnostic Framework for Multi-Prompt Generation Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-19T03:47:02.401637Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 38e2fd36-bfde-4cf9-83f7-72e3ab7bfee1 · inbound

LLM Economist: Large Population Models and Mechanism Design in Multi-Agent Generative Simulacra cites this paper.

LLM Economist: Large Population Models and Mechanism Design in Multi-Agent Generative Simulacra Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-06T15:28:44.717890Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 346b5bb3-711d-48d8-8423-0d7143312ac8 · inbound

Towards Compute-Optimal Many-Shot In-Context Learning cites this paper.

Towards Compute-Optimal Many-Shot In-Context Learning Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-06T15:20:38.685258Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f88829ff-e1a4-4b47-8de0-0de731f8ed83 · 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 Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T11:01:54.523163Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 95110452-93b8-4379-9351-f270e8ca2702 · 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 Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T10:43:47.403606Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 96dacc8d-e0e6-49a1-afd8-02c270846a23 · inbound

DTKG: Dual-Track Knowledge Graph-Verified Reasoning Framework for Multi-Hop QA cites this paper.

DTKG: Dual-Track Knowledge Graph-Verified Reasoning Framework for Multi-Hop QA Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-04T09:18:05.308839Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 73d9cd96-d2f4-49f8-b3d0-44f1fddf22c1 · inbound

Online In-Context Distillation for Low-Resource Vision Language Models cites this paper.

Online In-Context Distillation for Low-Resource Vision Language Models Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-18T05:40:55.999548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation cbee16fe-5d71-41f3-a59a-77f22b4753db · inbound

Real Faults in Model Context Protocol (MCP) Software: a Comprehensive Taxonomy cites this paper.

Real Faults in Model Context Protocol (MCP) Software: a Comprehensive Taxonomy Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 103

Resolution: unresolved; no resolver link, observed 2026-08-04T05:55:44.713410Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a3540f2-368c-4b82-9371-c66c26d1428f · inbound

CoALFake: Collaborative Active Learning with Human-LLM Co-Annotation for Cross-Domain Fake News Detection cites this paper.

CoALFake: Collaborative Active Learning with Human-LLM Co-Annotation for Cross-Domain Fake News Detection Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 8

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T16:48:03.096726Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 96f166a8-4d96-480e-9c31-f17a0bd6d57a · inbound

When Do We Need LLMs? A Diagnostic for Language-Driven Bandits cites this paper.

When Do We Need LLMs? A Diagnostic for Language-Driven Bandits Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-11T00:25:51.280296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 27442221-ac7f-4ade-947b-93884109cc22 · inbound

When Context Sticks: Studying Interference in In-Context Learning cites this paper.

When Context Sticks: Studying Interference in In-Context Learning Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:36:09.603959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b8f30bbe-6309-4f39-9523-9ca9cdf291b9 · inbound

Rethinking Dense Sequential Chains: Reasoning Language Models Can Extract Answers from Sparse, Order-Shuffling Chain-of-Thoughts cites this paper.

Rethinking Dense Sequential Chains: Reasoning Language Models Can Extract Answers from Sparse, Order-Shuffling Chain-of-Thoughts Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 22

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T03:50:57.556336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 715a7c07-3b98-4c7b-8bb8-015fc3b1621d · inbound

BLAgent: Agentic RAG for File-Level Bug Localization cites this paper.

BLAgent: Agentic RAG for File-Level Bug Localization Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-20T09:33:10.665567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1324b1ce-65b1-4f7f-9a2b-1f32876be72b · inbound

BLAgent: Agentic RAG for File-Level Bug Localization cites this paper.

BLAgent: Agentic RAG for File-Level Bug Localization Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-07-04T01:09:18.443745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 98ed886d-34a0-4edd-a673-d7e7864ff275 · inbound

DICE: Entropy-Regularized Equilibrium Selection for Stable Multi-Agent LLM Coordination cites this paper.

DICE: Entropy-Regularized Equilibrium Selection for Stable Multi-Agent LLM Coordination Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 117

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T20:57:23.878212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 427bfbd6-abbe-4e3a-8ce1-a0c172ee9aa3 · inbound

The Hitchhiker's Guide to Agentic AI: From Foundations to Systems cites this paper.

The Hitchhiker's Guide to Agentic AI: From Foundations to Systems Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 129

Resolution: verified exact; arxiv_id, observed 2026-07-04T11:09:46.512373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8118c5fa-ba54-4a8b-b581-f57c75689de4 · inbound

The Hitchhiker's Guide to Agentic AI: From Foundations to Systems cites this paper.

The Hitchhiker's Guide to Agentic AI: From Foundations to Systems Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 129

Resolution: unresolved; no resolver link, observed 2026-08-02T10:27:18.233294Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1f671541-6cea-4542-a5ae-64519f0f1e16 · inbound

Do LLM-Generated Skills Make Better AI Data Scientists? A Component Ablation Across Data-Science Workflows cites this paper.

Do LLM-Generated Skills Make Better AI Data Scientists? A Component Ablation Across Data-Science Workflows Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-07-09T09:06:06.141613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 92e0e376-9fa1-41cc-b61d-317097a6fd04 · inbound

Measuring and Evaluating the Performance of Generative AI Models for Scam Detection cites this paper.

Measuring and Evaluating the Performance of Generative AI Models for Scam Detection Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-01T18:19:27.751672Z

Source-reported events for the cited work

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Observation 0cfbf370-8663-4870-bbf6-caf7a772d722 · inbound

Visual Grounding in Zero-Shot Vision-Language Control cites this paper.

Visual Grounding in Zero-Shot Vision-Language Control Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T13:57:11.698424Z

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Unavailable: canonical work link unavailable.

Coverage vector

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