Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs | Paper Citation Record

Observation e56728d6-6922-42a7-8896-6aef5a8a41dd · outbound

This paper cites BABILong: Testing the Limits of LLMs with Long Context Reasoning-in-a-Haystack.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs BABILong: Testing the Limits of LLMs with Long Context Reasoning-in-a-Haystack

Reference 1

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Observation 9392e76b-b182-4655-baa6-d111f6678e85 · outbound

This paper cites Needlebench: Can llms do retrieval and reasoning in 1 million context window?.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Needlebench: Can llms do retrieval and reasoning in 1 million context window?

Reference 2

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Observation 1b681f5a-3dfe-4d30-964b-97071f816c23 · outbound

This paper cites $\infty$Bench: Extending Long Context Evaluation Beyond 100K Tokens.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs $\infty$Bench: Extending Long Context Evaluation Beyond 100K Tokens

Reference 3

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Observation 5c32c017-281b-41cc-b11e-c9fc52f77fb2 · outbound

This paper cites Does the Generator Mind its Contexts? An Analysis of Generative Model Faithfulness under Context Transfer.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Does the Generator Mind its Contexts? An Analysis of Generative Model Faithfulness under Context Transfer

Reference 4

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Observation b93e4054-dbeb-44ae-a758-e39eaad75f5b · outbound

This paper cites Lost in the middle: How language models use long contexts,.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Lost in the middle: How language models use long contexts,

Reference 5

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Observation db891dfb-12f3-469b-aa78-9454e26c06c4 · outbound

This paper cites RULER: What's the Real Context Size of Your Long-Context Language Models?.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs RULER: What's the Real Context Size of Your Long-Context Language Models?

Reference 6

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Observation 89016f02-1806-41d2-a19a-6ee07c167311 · outbound

This paper cites Lv-eval: A balanced long-context benchmark with 5 length levels up to 256k,.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Lv-eval: A balanced long-context benchmark with 5 length levels up to 256k,

Reference 7

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Observation d7e61b3e-d44f-482a-9b4a-8b9a998b34a6 · outbound

This paper cites "Lost-in-the-Later": Framework for Quantifying Contextual Grounding in Large Language Models.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs "Lost-in-the-Later": Framework for Quantifying Contextual Grounding in Large Language Models

Reference 8

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Observation 18c824d7-dab0-431b-aa3a-643d2e5ede78 · outbound

This paper cites DetectBench: Can Large Language Model Detect and Piece Together Implicit Evidence?.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs DetectBench: Can Large Language Model Detect and Piece Together Implicit Evidence?

Reference 9

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Observation 7e8587c5-622e-48d5-bef2-2323dcd70883 · outbound

This paper cites Evaluating Multilingual Long-Context Models for Retrieval and Reasoning.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Evaluating Multilingual Long-Context Models for Retrieval and Reasoning

Reference 10

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Observation 9677f9e4-6276-43cb-9c9b-a068d93fcb63 · outbound

This paper cites The two-hop curse: LLMs trained on 𝐴→𝐵,𝐵→𝐶 fail to learn𝐴→𝐶,.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs The two-hop curse: LLMs trained on 𝐴→𝐵,𝐵→𝐶 fail to learn𝐴→𝐶,

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Observation 317a9de4-fe3b-4838-80bb-0fb85b5bd291 · outbound

This paper cites Do Large Language Models Perform Latent Multi-Hop Reasoning without Exploiting Shortcuts?.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Do Large Language Models Perform Latent Multi-Hop Reasoning without Exploiting Shortcuts?

Reference 12

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Observation efb1b6a2-9bda-4029-9076-d882199f5887 · outbound

This paper cites Generating wikipedia by summarizing long sequences,.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Generating wikipedia by summarizing long sequences,

Reference 13

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Observation 3c48e851-65d2-4ad9-97cf-8c65f758a34f · outbound

This paper cites FactBench: A Dynamic Benchmark for In-the-Wild Language Model Factuality Evaluation.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs FactBench: A Dynamic Benchmark for In-the-Wild Language Model Factuality Evaluation

Reference 15

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Observation 1fee4a06-e7f3-4d75-a4b1-4865469298c8 · outbound

This paper cites Personalized Language Modeling from Personalized Human Feedback.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Personalized Language Modeling from Personalized Human Feedback

Reference 16

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Observation f7cad873-edfb-48ef-a837-31c57af71204 · outbound

This paper cites FaithEval: Can Your Language Model Stay Faithful to Context, Even If "The Moon is Made of Marshmallows".

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs FaithEval: Can Your Language Model Stay Faithful to Context, Even If "The Moon is Made of Marshmallows"

Reference 17

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Observation 210c117c-f6b6-4e41-81f6-e8eb94a9f489 · outbound

This paper cites LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding

Reference 18

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Observation 8e835c7a-4d13-4570-86c9-3b8f29da5fc3 · outbound

This paper cites L-Eval: Instituting Standardized Evaluation for Long Context Language Models.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs L-Eval: Instituting Standardized Evaluation for Long Context Language Models

Reference 19

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Observation e24b6808-5b13-4d13-8a8c-6bb1466e176f · outbound

This paper cites GPT-4 Technical Report.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs GPT-4 Technical Report

Reference 20

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Observation 913f1fd9-15cc-44c9-90b9-087529a19240 · outbound

This paper cites LLMEval-Fair: A Large-Scale Longitudinal Study on Robust and Fair Evaluation of Large Language Models.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs LLMEval-Fair: A Large-Scale Longitudinal Study on Robust and Fair Evaluation of Large Language Models

Reference 21

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Observation 455df315-ac27-4f1d-998f-f9a14271a753 · outbound

This paper cites Long-context Language Models Fail in Basic Retrieval Tasks Without Sufficient Reasoning Steps.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Long-context Language Models Fail in Basic Retrieval Tasks Without Sufficient Reasoning Steps

Reference 22

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Observation c5a0722d-0f4b-4756-be1b-ce80bc465473 · outbound

This paper cites FACTORY: A Challenging Human-Verified Prompt Set for Long-Form Factuality.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs FACTORY: A Challenging Human-Verified Prompt Set for Long-Form Factuality

Reference 23

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Observation 57a6ff05-a917-4826-b2cd-0a1107c09ab7 · outbound

This paper cites Investigating factuality in long-form text generation,.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Investigating factuality in long-form text generation,

Reference 24

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Observation 710a47ae-1039-4e15-b53e-07dbf435cd87 · outbound

This paper cites Evaluating Language Model Context Windows: A "Working Memory" Test and Inference-time Correction.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Evaluating Language Model Context Windows: A "Working Memory" Test and Inference-time Correction

Reference 25

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Observation ce664d0d-eed2-4955-aae1-cd1e914745b3 · outbound

This paper cites LLMs Get Lost In Multi-Turn Conversation.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs LLMs Get Lost In Multi-Turn Conversation

Reference 26

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Observation 53e7d78c-1f1a-4838-9deb-91e99737a786 · outbound

This paper cites LongIns: A Challenging Long-context Instruction-based Exam for LLMs.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs LongIns: A Challenging Long-context Instruction-based Exam for LLMs

Reference 27

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Observation 31957f20-d04e-46f0-857e-25d9df01d77e · outbound

This paper cites Needle in a haystack - pressure testing LLMs,.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Needle in a haystack - pressure testing LLMs,

Reference 28

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Observation 460d326a-1aca-42c0-9bdb-7e9f4c86d1c1 · outbound

This paper cites The needle in a haystack test: Evaluating the performance of LLM RAG systems,.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs The needle in a haystack test: Evaluating the performance of LLM RAG systems,

Reference 29

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Observation 6d065bd3-eec9-466c-8ff0-24436c8c8603 · outbound

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Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Sequential-NIAH: A needle-in-a-haystack benchmark for extracting sequential needles from long contexts,

Reference 30

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Observation 7fa9bf73-431d-44e8-9143-c25062bc943f · outbound

This paper cites NoLiMa: Long-Context Evaluation Beyond Literal Matching.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs NoLiMa: Long-Context Evaluation Beyond Literal Matching

Reference 31

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Observation 95045da3-68d3-42c6-8b7e-66fa5829bce2 · outbound

This paper cites LongGenBench: Benchmarking Long-Form Generation in Long Context LLMs.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs LongGenBench: Benchmarking Long-Form Generation in Long Context LLMs

Reference 32

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Observation c8cd46c7-c063-4845-b551-2ba0ac7a3b28 · outbound

This paper cites When Context Leads but Parametric Memory Follows in Large Language Models.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs When Context Leads but Parametric Memory Follows in Large Language Models

Reference 33

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Observation f78528d1-f79b-434f-99f4-57b32011e6f2 · outbound

This paper cites Multilingual Needle in a Haystack: Investigating Long-Context Behavior of Multilingual Large Language Models.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Multilingual Needle in a Haystack: Investigating Long-Context Behavior of Multilingual Large Language Models

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:31.786033Z

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Observation 13b2bafb-bb75-4c4e-ac28-63a4bc9516ea · outbound

This paper cites Premise order matters in reasoning with large language models,.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Premise order matters in reasoning with large language models,

Reference 35

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Observation b47b553c-418e-4539-8c10-d01dc8888f7b · outbound

This paper cites A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions,.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions,

Reference 36

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Observation 2ceedd2a-2e2e-4eaf-99a3-7e0fb54901c7 · outbound

This paper cites Long Context RAG Performance of Large Language Models.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Long Context RAG Performance of Large Language Models

Reference 37

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Observation 3c5603f2-039e-4a8a-8bf8-6e05298667fb · outbound

This paper cites Understanding and addressing ai hallucinations in healthcare and life sciences,.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Understanding and addressing ai hallucinations in healthcare and life sciences,

Reference 38

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Observation d795bb48-63df-4703-8818-08e98bb84a8a · outbound

This paper cites A survey on hallucination in large language and foundation models,.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs A survey on hallucination in large language and foundation models,

Reference 39

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Observation b6183e3a-4e35-451d-a55c-690402e9bd76 · outbound

This paper cites Creating Trustworthy LLMs: Dealing with Hallucinations in Healthcare AI.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Creating Trustworthy LLMs: Dealing with Hallucinations in Healthcare AI

Reference 40

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Observation 9e7b3a2b-1aaa-45ef-af49-e63e2c680c8d · outbound

This paper cites Unravelling the mysteries of hallucination in large language models: Strategies for precision in artificial intelligence language generation,.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Unravelling the mysteries of hallucination in large language models: Strategies for precision in artificial intelligence language generation,

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Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:32.949477Z

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Observation 7683181c-f19f-45d2-8798-995906641b62 · outbound

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Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs HALO: Hallucination Analysis and Learning Optimization to Empower LLMs with Retrieval-Augmented Context for Guided Clinical Decision Making

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Observation b2520d3f-e312-4d09-9091-ba8f230c8360 · outbound

This paper cites Dual process theory for large language models: An overview of using psychology to address hallucination and reliability issues,.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Dual process theory for large language models: An overview of using psychology to address hallucination and reliability issues,

Reference 43

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Observation 7accfd72-9f62-41eb-86f6-afb3c53627e6 · outbound

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Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Factchd: Benchmarking fact-conflicting hallucination detection,

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Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:33.498985Z

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Observation 0ce3a900-5ec7-406d-888f-09bb0f7e5ae4 · outbound

This paper cites Explainable hallucination mitigation in large language models: A survey,.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Explainable hallucination mitigation in large language models: A survey,

Reference 45

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Observation 74617589-6273-4fe9-ac58-33ef90206025 · outbound

This paper cites Zero-resource hallucination detection for text generation via graph- based contextual knowledge triples modeling,.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Zero-resource hallucination detection for text generation via graph- based contextual knowledge triples modeling,

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:33.631475Z

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Observation 0ae3a80a-4ac6-4f8d-8820-f1d83c47f1e0 · outbound

This paper cites Chainpoll: A high efficacy method for LLM hallucination detection.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Chainpoll: A high efficacy method for LLM hallucination detection

Reference 47

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Observation 9617f5ce-a0f7-4fc5-909e-ca9d96e5c604 · outbound

This paper cites Zero-knowledge llm hallucination detection and mitigation through fine-grained cross-model consistency,.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Zero-knowledge llm hallucination detection and mitigation through fine-grained cross-model consistency,

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Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:33.945972Z

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Observation 81e22626-cffb-4f92-b4ed-388a4f111ae6 · outbound

This paper cites Detecting and preventing hallucinations in large vision language models,.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Detecting and preventing hallucinations in large vision language models,

Reference 49

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Observation e108ea99-376a-4986-a8fb-fb9b66f873e2 · outbound

This paper cites Beyond probabilities: Unveiling the delicate dance of large language models (llms) and ai-hallucination,.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Beyond probabilities: Unveiling the delicate dance of large language models (llms) and ai-hallucination,

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:34.203036Z

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Observation 9376b2d0-9f36-402f-bc3c-1787b8668d95 · outbound

This paper cites KEA Explain: Explanations of Hallucinations using Graph Kernel Analysis.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs KEA Explain: Explanations of Hallucinations using Graph Kernel Analysis

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:34.323746Z

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Observation c9297b5a-1651-43c4-99a1-6141320b9419 · outbound

This paper cites Mitigating hallucinations in large language models for educational application,.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Mitigating hallucinations in large language models for educational application,

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:34.491923Z

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Observation b25b8640-ec49-4c07-b20c-3c002161f5c8 · outbound

This paper cites The Illusion of Progress: Re-evaluating Hallucination Detection in LLMs.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs The Illusion of Progress: Re-evaluating Hallucination Detection in LLMs

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:34.626786Z

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Observation cf3c88d3-4efa-48d9-9c0e-e882d09a07a0 · outbound

This paper cites Hallucinations in large language models (llm’s): challenges in mitigation, trust, and future directions,.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Hallucinations in large language models (llm’s): challenges in mitigation, trust, and future directions,

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:34.734659Z

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Observation 50405243-a0fd-4d5e-83e4-3e7539aa92c7 · outbound

This paper cites Detecting llm hallucinations using monte carlo simulations on token probabilities,.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Detecting llm hallucinations using monte carlo simulations on token probabilities,

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:34.966239Z

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Observation c14198a1-4884-413e-8e8d-e25c7366b5f9 · outbound

This paper cites HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:35.085906Z

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Observation 4191dd39-8dc3-407a-8495-1529dbfdc863 · outbound

This paper cites Loki's Dance of Illusions: A Comprehensive Survey of Hallucination in Large Language Models.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Loki's Dance of Illusions: A Comprehensive Survey of Hallucination in Large Language Models

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:35.151076Z

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Observation d9a1085d-4a43-466b-8935-08c6268c7b47 · outbound

This paper cites Learning to Trust Your Feelings: Leveraging Self-awareness in LLMs for Hallucination Mitigation.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Learning to Trust Your Feelings: Leveraging Self-awareness in LLMs for Hallucination Mitigation

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:35.249637Z

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Observation 8f36fb3c-99e2-42b4-b491-6bdc6cb7abac · outbound

This paper cites Attention-guided self-reflection for zero-shot hallucination detection in large language models,.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Attention-guided self-reflection for zero-shot hallucination detection in large language models,

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:35.399305Z

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Observation b1a65246-1597-4ee3-84f3-c95157cf0a39 · outbound

This paper cites Roberta with low-rank adaptation and hierarchical attention for hallucination detection in llms,.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Roberta with low-rank adaptation and hierarchical attention for hallucination detection in llms,

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:35.553414Z

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Observation 0ec7bc13-6cb7-47da-807f-717a4d7628b6 · outbound

This paper cites SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:35.719806Z

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Observation 1901cc84-ce60-4eed-a353-0407e564061b · outbound

This paper cites Hallucination detox: Sensitivity dropout (send) for large language model training,.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Hallucination detox: Sensitivity dropout (send) for large language model training,

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:35.825354Z

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Observation 0cacecfb-a64f-4919-b4c7-672f89c4fe15 · outbound

This paper cites Fakes of Varying Shades: How Warning Affects Human Perception and Engagement Regarding LLM Hallucinations.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Fakes of Varying Shades: How Warning Affects Human Perception and Engagement Regarding LLM Hallucinations

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:35.972895Z

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Observation 1e342b18-ccee-4a2e-98c7-95758ef00614 · outbound

This paper cites Leveraging Graph Structures to Detect Hallucinations in Large Language Models.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Leveraging Graph Structures to Detect Hallucinations in Large Language Models

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:36.166326Z

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Observation 14a96be2-2df9-4db1-95d5-0b386e248d02 · outbound

This paper cites ERBench: An Entity-Relationship based Automatically Verifiable Hallucination Benchmark for Large Language Models.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs ERBench: An Entity-Relationship based Automatically Verifiable Hallucination Benchmark for Large Language Models

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:36.293010Z

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Observation 39be5c72-f95e-40b1-abd9-c7337be3e6cc · outbound

This paper cites LLMs Know More Than They Show: On the Intrinsic Representation of LLM Hallucinations.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs LLMs Know More Than They Show: On the Intrinsic Representation of LLM Hallucinations

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:36.406342Z

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Observation 8e835405-9176-466d-aa02-eb6008f81574 · outbound

This paper cites Mitigating hallucinations in large language models via semantic enrichment of prompts: Insights from biobert and ontological integration,.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Mitigating hallucinations in large language models via semantic enrichment of prompts: Insights from biobert and ontological integration,

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:36.595582Z

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Observation 1d251080-1a8c-478c-a026-4caf55bb4658 · outbound

This paper cites Hallusafe at semeval-2024 task 6: An nli-based approach to make llms safer by better detecting hallucinations and overgeneration mistakes,.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Hallusafe at semeval-2024 task 6: An nli-based approach to make llms safer by better detecting hallucinations and overgeneration mistakes,

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:36.773630Z

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Observation 8c5be357-b6cc-48e1-ac63-46c62cd8489c · outbound

This paper cites A Survey of Hallucination in Large Foundation Models.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs A Survey of Hallucination in Large Foundation Models

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:36.885996Z

Source-reported events for the cited work

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Observation c4349128-d1a8-4b29-9ad9-c0f7c9c7acf5 · outbound

This paper cites Delucionqa: Detecting hallucinations in domain-specific question answering,.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Delucionqa: Detecting hallucinations in domain-specific question answering,

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:36.925471Z

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Observation 982f03dd-a1ad-4405-bec3-2e2c39c5f620 · outbound

This paper cites Mitigation of hallucinations in language models in education: A new approach of comparative and cross-verification,.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Mitigation of hallucinations in language models in education: A new approach of comparative and cross-verification,

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:37.018426Z

Source-reported events for the cited work

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Observation 144a7740-1d41-47d6-9000-e267cbaf9457 · outbound

This paper cites Unsupervised Real-Time Hallucination Detection based on the Internal States of Large Language Models.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Unsupervised Real-Time Hallucination Detection based on the Internal States of Large Language Models

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:37.137620Z

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Observation e9f584bd-ba29-419a-8e9d-782d0d6915c2 · outbound

This paper cites Confabulation: The Surprising Value of Large Language Model Hallucinations.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Confabulation: The Surprising Value of Large Language Model Hallucinations

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:37.288954Z

Source-reported events for the cited work

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Observation 432dcd8d-2352-4c01-9de2-16185e1c96cb · outbound

This paper cites A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:37.377620Z

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Observation 49ca31cc-19e8-49fc-89c9-85f742d66937 · outbound

This paper cites Investigating hallucination tendencies of large language models in japanese and english,.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Investigating hallucination tendencies of large language models in japanese and english,

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:37.478912Z

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Observation dbadf06a-0d02-4769-9d21-68a0e55def89 · outbound

This paper cites A Stitch in Time Saves Nine: Detecting and Mitigating Hallucinations of LLMs by Validating Low-Confidence Generation.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs A Stitch in Time Saves Nine: Detecting and Mitigating Hallucinations of LLMs by Validating Low-Confidence Generation

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:37.546861Z

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Observation 382aaa0c-f068-4db3-9fcc-8eead70e1569 · outbound

This paper cites Mitigating Hallucinations in Large Vision-Language Models with Instruction Contrastive Decoding.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Mitigating Hallucinations in Large Vision-Language Models with Instruction Contrastive Decoding

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:37.680719Z

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Observation 7112456d-5ecc-4f9e-b9bc-87a377499d5e · outbound

This paper cites LaMsS: When Large Language Models Meet Self-Skepticism.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs LaMsS: When Large Language Models Meet Self-Skepticism

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:37.789483Z

Source-reported events for the cited work

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Observation 689a9a5d-4c70-422a-818a-57ea9a3c432c · outbound

This paper cites Detecting and reducing the factual hallucinations of large language models with metamorphic testing,.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Detecting and reducing the factual hallucinations of large language models with metamorphic testing,

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:37.900100Z

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Observation 0a52ba67-cec8-4bea-a320-f134e0de08ec · outbound

This paper cites EFUF: Efficient Fine-grained Unlearning Framework for Mitigating Hallucinations in Multimodal Large Language Models.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs EFUF: Efficient Fine-grained Unlearning Framework for Mitigating Hallucinations in Multimodal Large Language Models

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:38.006298Z

Source-reported events for the cited work

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Observation fb68b6f6-33e3-4286-976a-35847502be34 · outbound

This paper cites InterrogateLLM: Zero-Resource Hallucination Detection in LLM-Generated Answers.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs InterrogateLLM: Zero-Resource Hallucination Detection in LLM-Generated Answers

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:38.096256Z

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Observation 5dba7273-2578-4879-b2c8-f6029720d81b · outbound

This paper cites Siren’s song in the ai ocean: A survey on hallucination in large language models,.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Siren’s song in the ai ocean: A survey on hallucination in large language models,

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:38.175846Z

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Observation 835c536e-5752-4126-b27a-d1e8a7a512c0 · outbound

This paper cites Hop, Skip, and Overthink: Diagnosing Why Reasoning Models Fumble during Multi-Hop Analysis.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Hop, Skip, and Overthink: Diagnosing Why Reasoning Models Fumble during Multi-Hop Analysis

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:38.241011Z

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Observation 954c3029-27b7-42b8-a9fd-14374147bc1f · outbound

This paper cites Prompting for faithfulness: When “don’t make it up.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Prompting for faithfulness: When “don’t make it up

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:38.324828Z

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Observation 63bf5cec-390a-4172-bda1-ace5d7036eb9 · outbound

This paper cites Aspects of human memory and large language models,.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Aspects of human memory and large language models,

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:38.448802Z

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Observation b927ae67-16c3-4d6f-b788-0e3c66fcb232 · outbound

This paper cites More is less: Increased processing of unwanted memories facilitates forgetting,.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs More is less: Increased processing of unwanted memories facilitates forgetting,

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:38.513703Z

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Observation 013e6555-b428-4ea6-90f3-0a00f04d1e91 · outbound

This paper cites Towards a Holistic and Automated Evaluation Framework for Multi-Level Comprehension of LLMs in Book-Length Contexts.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Towards a Holistic and Automated Evaluation Framework for Multi-Level Comprehension of LLMs in Book-Length Contexts

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:38.614626Z

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Observation e0036ca5-c46d-44df-82b1-a931b6c71c16 · outbound

This paper cites Abductive commonsense reasoning,.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Abductive commonsense reasoning,

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:38.715156Z

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Observation 93cf701d-1912-4476-9664-f2189ee9d5b2 · outbound

This paper cites OR-Bench: An Over-Refusal Benchmark for Large Language Models.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs OR-Bench: An Over-Refusal Benchmark for Large Language Models

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:38.838889Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 05c81e98-d1aa-4fca-8bfb-4d122a477b71 · outbound

This paper cites Evaluating long-context language models on distributed evidence reasoning,.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Evaluating long-context language models on distributed evidence reasoning,

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:38.944759Z

Source-reported events for the cited work

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Observation 9463721d-2880-4c77-be62-dc231d4e55c5 · outbound

This paper cites Context rot: How increasing input tokens impacts llm performance,.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Context rot: How increasing input tokens impacts llm performance,

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:39.045955Z

Source-reported events for the cited work

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Observation 0fd7e218-193c-41e9-be39-319faf7e1ddc · outbound

This paper cites Scrolls: Standardized comparison over long language sequences,.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Scrolls: Standardized comparison over long language sequences,

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:39.137754Z

Source-reported events for the cited work

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Observation 709b7117-57a4-4aac-9e3a-d5769017d943 · outbound

This paper cites KoLA: Carefully Benchmarking World Knowledge of Large Language Models.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs KoLA: Carefully Benchmarking World Knowledge of Large Language Models

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:39.212896Z

Source-reported events for the cited work

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Observation 06f3ae5c-4724-401b-b88b-67541c20c0eb · outbound

This paper cites Provide your answers in the following format: Question 1: [YOUR ANSWER] Question 2: [YOUR ANSWER].

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Provide your answers in the following format: Question 1: [YOUR ANSWER] Question 2: [YOUR ANSWER]

Reference 96

Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:39.485621Z

Source-reported events for the cited work

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Observation d1967022-c456-4ee6-9cb6-d90fe852f863 · outbound

This paper cites an unresolved cited work.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Unresolved cited work

Reference 97

Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:39.554368Z

Source-reported events for the cited work

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Observation b5f6ecbd-b53c-43a6-b952-7081c963156c · outbound

This paper cites an unresolved cited work.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Unresolved cited work

Reference 98

Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:39.617906Z

Source-reported events for the cited work

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Observation 550d1c51-808d-4baa-8004-5ea19886332d · outbound

This paper cites an unresolved cited work.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Unresolved cited work

Reference 99

Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:39.721849Z

Source-reported events for the cited work

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Observation cf550189-204f-4515-af23-9ba96030bbce · outbound

This paper cites Not mentioned in the text or story.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Not mentioned in the text or story

Reference 100

Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:39.811946Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b7054414-b5d4-437a-8abb-e74c3f18aea8 · outbound

This paper cites an unresolved cited work.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs Unresolved cited work

Reference 101

Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:39.848743Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2de45785-57b0-42c6-8c68-dd0a2f86fd2f · outbound

This paper cites don’t make it up.

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs don’t make it up

Reference 102

Resolution: unresolved; no resolver link, observed 2026-08-03T12:42:39.941106Z

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Coverage vector

Reference resolution

Outbound references

Pith citing papers