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Paper Citation Record · LEDGER

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval

As of 6 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 1 inbound Pith citation observation for arXiv:2512.00804.

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

pith.paper-citation-record.v1
2512.00804 v3

Coverage vector

measured 60 of 60 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T19:27:04.162297Z

measured 61 of 61 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-27T16:49:14.243931Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-03T01:07:30.217444Z

Reference resolution

60 of 60 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved59
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1eacbdd7-583b-456b-bb2e-ab4576ca07df · outbound

This paper cites Meta llama,.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval Meta llama,

Reference 1

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source=pdf_text observed=2026-08-03T19:27:04.031419Z digest=sha256:1bcda708c57ad7dfff531de9205134e0fce91c28d89650776226a1f0bd5a0ae5

Observation 716e0328-34b8-4735-ae0a-3a37e0591dde · outbound

This paper cites GPT-4 Technical Report.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval GPT-4 Technical Report

Reference 2

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source=pdf_text observed=2026-08-03T19:27:04.034438Z digest=sha256:d8165f94d228e52e2e3a35b77d57e86e3cdea57362671de606770c7d5f9fe108

Observation 50d289f0-5b4d-4a73-aef3-8f94582b9d04 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 3

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source=pdf_text observed=2026-08-03T19:27:04.037495Z digest=sha256:392ee366291ceeececf4e92cf0542e9e08cbb0e1a43543d833aefaa6eb8ca02c

Observation bb109f6e-5275-4cf6-bb72-7fb3cb41ef4c · outbound

This paper cites Towards Expert-Level Medical Question Answering with Large Language Models.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval Towards Expert-Level Medical Question Answering with Large Language Models

Reference 4

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source=pdf_text observed=2026-08-03T19:27:04.039850Z digest=sha256:cb54db7e410b0407514274a4618a4bd0f45ff8a8c394ce9ca3fa54d7ca48c08c

Observation 6d977c2c-c61b-4866-b2dd-796d58be072b · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval Evaluating Large Language Models Trained on Code

Reference 5

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source=pdf_text observed=2026-08-03T19:27:04.042480Z digest=sha256:d4c03c6bef55e45a09ab9d0c41e6b2e16ea942fc7cee26ddc4f2da45a4113707

Observation 47dfe531-6e12-4fc9-bbf3-d47e91807e78 · outbound

This paper cites Github copilot: Your ai pair programmer,.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval Github copilot: Your ai pair programmer,

Reference 6

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source=pdf_text observed=2026-08-03T19:27:04.045514Z digest=sha256:bd225e0e167959cd65ea97a6de54ff450ce40bd1b31ef6c62c44adee58edfcf9

Observation c1b69bb1-190f-46a5-bb9a-1fb2cb8c20e9 · outbound

This paper cites Cursor: The ai-first code editor,.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval Cursor: The ai-first code editor,

Reference 7

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source=pdf_text observed=2026-08-03T19:27:04.047720Z digest=sha256:9da428b4663a038c077c971b4920e8b76a43fec704b051ae532f7da51634a59a

Observation 4fa05805-fb7c-41d1-9f40-970a9f81d852 · outbound

This paper cites Introducing Deep Research,.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval Introducing Deep Research,

Reference 8

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source=pdf_text observed=2026-08-03T19:27:04.049699Z digest=sha256:444e96ec4b77a8dbaa475f1eec74547c3ab9237bb0d2e01ca44cd1762fc1bbb7

Observation b949802a-2d81-4d71-a21f-f881004aa37b · outbound

This paper cites Retrieval-augmented generation for knowledge-intensive nlp tasks,.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval Retrieval-augmented generation for knowledge-intensive nlp tasks,

Reference 9

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source=pdf_text observed=2026-08-03T19:27:04.051687Z digest=sha256:d52519b3eb0359110124925ef75ddbed4b443b48296f40c9925eed97dca950cf

Observation 8446b069-6eac-4c5f-a985-b53f8ef5e059 · outbound

This paper cites PoisonedRAG: Knowledge Corruption Attacks to Retrieval-Augmented Generation of Large Language Models.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval PoisonedRAG: Knowledge Corruption Attacks to Retrieval-Augmented Generation of Large Language Models

Reference 10

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source=pdf_text observed=2026-08-03T19:27:04.053807Z digest=sha256:43ec83e5c5e0a82450926e0f329aa7b3fd96bf94ced119ef27d8270aa31014bb

Observation d356bdc1-be44-41b7-b443-d3ac88d87d01 · outbound

This paper cites BadRAG: Identifying Vulnerabilities in Retrieval Augmented Generation of Large Language Models.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval BadRAG: Identifying Vulnerabilities in Retrieval Augmented Generation of Large Language Models

Reference 11

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source=pdf_text observed=2026-08-03T19:27:04.056271Z digest=sha256:931ffe9bf5fdc2cc27295632d13c902c20955f50c67c2586f705e9485817ec37

Observation db6e3e4c-3b05-48bc-82d6-1c4d5f3ae411 · outbound

This paper cites The good and the bad: Exploring privacy issues in retrieval-augmented generation (rag),.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval The good and the bad: Exploring privacy issues in retrieval-augmented generation (rag),

Reference 12

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source=pdf_text observed=2026-08-03T19:27:04.058515Z digest=sha256:70ecb7345c68527e678c8aa3484a78650bc63e31269ac233b79c49031d4e9c7b

Observation dbb0f792-0b11-49f2-95f8-67d845919187 · outbound

This paper cites InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents

Reference 13

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source=pdf_text observed=2026-08-03T19:27:04.063455Z digest=sha256:e15d81c69fc4f5a3f81a202a70c3f20ffddc4de3cc25153afbbf546f3dc69135

Observation 2e23f020-e946-48df-adb3-03daa42fd303 · outbound

This paper cites Agentpoison: Red-teaming llm agents via poisoning memory or knowledge bases,.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval Agentpoison: Red-teaming llm agents via poisoning memory or knowledge bases,

Reference 14

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source=pdf_text observed=2026-08-03T19:27:04.065986Z digest=sha256:c307e578a457d17210f7769e5b77e7d4916dbeb96313182e22554af0eb45e33d

Observation ede62264-a3ca-4b53-9dba-be3bcda9dab7 · outbound

This paper cites Not what you’ve signed up for: Compromising real- world llm-integrated applications with indirect prompt injection,.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval Not what you’ve signed up for: Compromising real- world llm-integrated applications with indirect prompt injection,

Reference 15

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source=pdf_text observed=2026-08-03T19:27:04.070151Z digest=sha256:7b61f9d8af8ed5d4aa8e10ba3c211f97bc579046c224feabc7e660751f8c0115

Observation 00be42cd-3382-4fa4-9220-cd11eb44916b · outbound

This paper cites AgentPoison: Red-teaming LLM Agents via Poisoning Memory or Knowledge Bases.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval AgentPoison: Red-teaming LLM Agents via Poisoning Memory or Knowledge Bases

Reference 16

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source=pdf_text observed=2026-08-03T19:27:04.067874Z digest=sha256:0fc3aa29a2e83f254cad31264096d292e88d4209440365067591b9886c6e2079

Observation b244a89d-ca33-449c-87ef-7ed631c519a9 · outbound

This paper cites Formalizing and benchmarking prompt injection attacks and defenses,.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval Formalizing and benchmarking prompt injection attacks and defenses,

Reference 17

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source=pdf_text observed=2026-08-03T19:27:04.074191Z digest=sha256:e5c28608cb42ded586fbd609e09e39a1068b0ad42ec06f68b8a1e687f7d406c8

Observation 8d0669c1-23c1-49e2-aade-0208752f07b3 · outbound

This paper cites Machine-Made Media: Monitoring the Mobilization of Machine-Generated Articles on Misinformation and Mainstream News Websites.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval Machine-Made Media: Monitoring the Mobilization of Machine-Generated Articles on Misinformation and Mainstream News Websites

Reference 18

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source=pdf_text observed=2026-08-03T19:27:04.072154Z digest=sha256:f4c5b42927afb5ef4f5e467628ecc29bbe3e2db41dff76fa83147dfbbd78efa5

Observation 8fc58323-08a0-437e-8d7d-569358932fcf · outbound

This paper cites Wikicheck: An end-to- end open source automatic fact-checking api based on wikipedia,.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval Wikicheck: An end-to- end open source automatic fact-checking api based on wikipedia,

Reference 19

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source=pdf_text observed=2026-08-03T19:27:04.077854Z digest=sha256:5907ee342e6edadc688110ce4f34adb3c52cefcbba8c31e046300bd4f4762908

Observation fa03b8d1-2752-4e57-9822-129aefc68160 · outbound

This paper cites Datasentinel: A game-theoretic detection of prompt injection attacks,.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval Datasentinel: A game-theoretic detection of prompt injection attacks,

Reference 20

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source=pdf_text observed=2026-08-03T19:27:04.075976Z digest=sha256:7bea48f880147ec3762261397aaec77425c8fac8b57e73aef20e70cfca19239f

Observation 81d2968a-c5e3-499a-b4cf-032c5f5808d2 · outbound

This paper cites Towards More Robust Retrieval-Augmented Generation: Evaluating RAG Under Adversarial Poisoning Attacks.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval Towards More Robust Retrieval-Augmented Generation: Evaluating RAG Under Adversarial Poisoning Attacks

Reference 21

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source=pdf_text observed=2026-08-03T19:27:04.082197Z digest=sha256:593a51dd3783f3b718638eefba828ef42a85b40f0f04143056430004820c39c3

Observation 5db3ac60-8940-449b-911e-0b0e7f088ed2 · outbound

This paper cites Real AI Agents with Fake Memories: Fatal Context Manipulation Attacks on Web3 Agents.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval Real AI Agents with Fake Memories: Fatal Context Manipulation Attacks on Web3 Agents

Reference 22

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source=pdf_text observed=2026-08-03T19:27:04.079950Z digest=sha256:e11e0ed1c3352d4210055b112d9b150a4d8ee670c887aa79c6b7eb942c1a3776

Observation 8b499d5b-9f57-4f1e-aee2-4801a559ada5 · outbound

This paper cites Exploring the Escalation of Source Bias in User, Data, and Recommender System Feedback Loop.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval Exploring the Escalation of Source Bias in User, Data, and Recommender System Feedback Loop

Reference 23

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source=pdf_text observed=2026-08-03T19:27:04.086490Z digest=sha256:d37b8edcd34df750a1998a0bd5a55e6cc344fb747b566fb7f21aab017ef2b91e

Observation 73752f4d-2ef0-4cc5-924a-e0cbac77bcdc · outbound

This paper cites Invisible Relevance Bias: Text-Image Retrieval Models Prefer AI-Generated Images.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval Invisible Relevance Bias: Text-Image Retrieval Models Prefer AI-Generated Images

Reference 24

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source=pdf_text observed=2026-08-03T19:27:04.084301Z digest=sha256:dc5685381a365a7347ae13d8de32f0869af8759a7e48d70fda9f2ff9fcac6622

Observation a3d594c0-dd24-4b1f-838d-9ee164cdc873 · outbound

This paper cites The use of mmr, diversity-based reranking for reordering documents and producing summaries,.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval The use of mmr, diversity-based reranking for reordering documents and producing summaries,

Reference 25

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source=pdf_text observed=2026-08-03T19:27:04.090852Z digest=sha256:59dc4f44b5ba166d77fa33b631f42f2531a075dbb8bd498936edaca501b1c231

Observation 5164d2d3-79ba-4a74-a880-8b9bbf275098 · outbound

This paper cites Bias Amplification in RAG: Poisoning Knowledge Retrieval to Steer LLMs.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval Bias Amplification in RAG: Poisoning Knowledge Retrieval to Steer LLMs

Reference 26

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source=pdf_text observed=2026-08-03T19:27:04.088604Z digest=sha256:4ddfd798419876d828d4fe32a988f06f699269b7a71d93982d222e16286cada1

Observation d70cc48f-51d5-4cef-8ebe-07db56999fa5 · outbound

This paper cites Through the stealth lens: Rethinking attacks and defenses in rag,.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval Through the stealth lens: Rethinking attacks and defenses in rag,

Reference 27

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source=pdf_text observed=2026-08-03T19:27:04.094815Z digest=sha256:e1b0de65b3ba2ec26d9c3530fb09e847ce834ddfd2992bc9a5087526280521f0

Observation be21f0b1-8525-42fd-af13-2167eb98a1d5 · outbound

This paper cites SMART-RAG: Selection using Determinantal Matrices for Augmented Retrieval.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval SMART-RAG: Selection using Determinantal Matrices for Augmented Retrieval

Reference 28

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source=pdf_text observed=2026-08-03T19:27:04.092818Z digest=sha256:8b6a71f6b701a434a709e32715ee7f8bf1e0391b6a687cd0db37e36942f5fc8d

Observation 345a7593-4c2c-4ecc-b815-3f4911ca2736 · outbound

This paper cites HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering

Reference 29

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source=pdf_text observed=2026-08-03T19:27:04.101375Z digest=sha256:97499a1801017bf43258b82907fd29a57343797fbdbc48ae528914634ff25afd

Observation f7c3e50d-94e8-4354-9421-30caf17e0528 · outbound

This paper cites A dataset for sentence retrieval for open-ended dialogues,.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval A dataset for sentence retrieval for open-ended dialogues,

Reference 30

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source=pdf_text observed=2026-08-03T19:27:04.103527Z digest=sha256:5af760ae2a131d8df3f1220e4b484e5dd4cd7f1e67f30be11c0ec6fa7caef78e

Observation a7f7fecb-c7d7-4b05-bd68-f21039ba1cf1 · outbound

This paper cites Measuring and addressing indexical bias in information retrieval,.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval Measuring and addressing indexical bias in information retrieval,

Reference 31

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source=pdf_text observed=2026-08-03T19:27:04.099287Z digest=sha256:2b0a574f7b4836023e97d64e4dbd98ccd4b4f9282dae07ba866345bb127e3a0a

Observation bdfdc68a-c4be-4eb7-ae03-a5cdf7f3fb3f · outbound

This paper cites The faiss library,.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval The faiss library,

Reference 32

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source=pdf_text observed=2026-08-03T19:27:04.107966Z digest=sha256:dbd59ed71a35444121bbbf9eeba42ebb44cb6cb68cbd32caa54f92a1f430263b

Observation 433c8d0d-00e8-4b96-8b49-06b6cbd0afe7 · outbound

This paper cites Retrieval-augmented generation for knowledge-intensive nlp tasks,.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval Retrieval-augmented generation for knowledge-intensive nlp tasks,

Reference 33

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source=pdf_text observed=2026-08-03T19:27:04.112434Z digest=sha256:3d2e6a766c0058fe6caefada56c565e2b45b32e084bb173dc877fb17297ec68a

Observation af11025b-3c1f-4917-ae99-925d7a6f868f · outbound

This paper cites Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Reference 34

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source=pdf_text observed=2026-08-03T19:27:04.105560Z digest=sha256:2e141a85cb02190e8db0d8047d5c5355d7845ad7f85c68f22da9b5a9e2bc9cd6

Observation cf9b4b37-e337-46e8-9ada-97c4059e5897 · outbound

This paper cites Bias and unfairness in information retrieval systems: New challenges in the llm era,.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval Bias and unfairness in information retrieval systems: New challenges in the llm era,

Reference 35

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source=pdf_text observed=2026-08-03T19:27:04.116598Z digest=sha256:04e901d7e8099084ed02315aa6105aab3efa50e4499e5166e6d7b63113f8a05a

Observation 810a4279-4519-4ea4-b777-a78c8c5da435 · outbound

This paper cites The Faiss library.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval The Faiss library

Reference 36

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Observation d3a35d94-fb6f-4b9b-b9a5-e43432bd1a9f · outbound

This paper cites Neural retrievers are biased towards llm-generated content,.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval Neural retrievers are biased towards llm-generated content,

Reference 37

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Observation 4a94a936-49b4-4a3a-b521-11ed7af71e75 · outbound

This paper cites Large dual encoders are generalizable retrievers,.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval Large dual encoders are generalizable retrievers,

Reference 38

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Observation 09ce7ab1-f6c3-4346-b146-3f03c4437c84 · outbound

This paper cites Principal component analysis: A natural approach to data exploration,.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval Principal component analysis: A natural approach to data exploration,

Reference 40

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Observation aa6a0d91-3a38-4219-bd6d-11d174616bdd · outbound

This paper cites Vershynin,High-Dimensional Probability: An Introduction with Applications in Data Science, ser.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval Vershynin,High-Dimensional Probability: An Introduction with Applications in Data Science, ser

Reference 41

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source=pdf_text observed=2026-08-03T19:27:04.129485Z digest=sha256:ea1e9bd546917d2a0120c5097cf797937836a3240401ac422e5d5b48a7ecb56b

Observation a62c5377-3747-40a1-bfd2-55ce8ed49a14 · outbound

This paper cites The Curious Case of Neural Text Degeneration.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval The Curious Case of Neural Text Degeneration

Reference 42

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source=pdf_text observed=2026-08-03T19:27:04.122373Z digest=sha256:ac14cafce13d7ef9cd1cbf915f39bdaedb114d25a94b9c7ac9b61af39da1feea

Observation 9029093e-f9fe-487a-abd6-78b516191d7e · outbound

This paper cites meta-llama/meta-llama-3-8b,.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval meta-llama/meta-llama-3-8b,

Reference 43

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source=pdf_text observed=2026-08-03T19:27:04.133497Z digest=sha256:02eb1f4056dc59a3ef3affedad51ce973635dae96b22379f90f77d226b4b4431

Observation 2fefa637-ea17-4883-85ba-0bb701c5e769 · outbound

This paper cites Determinantal point processes for machine learning,.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval Determinantal point processes for machine learning,

Reference 44

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source=pdf_text observed=2026-08-03T19:27:04.127310Z digest=sha256:69255b8dddfd41fbae4db817f575115bc163eb40b727bf9dfd2a9e5f57b49008

Observation b0ece307-28a0-4c2b-b302-92bad2fd0acb · outbound

This paper cites Efficient memory management for large language model serving with pagedattention,.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval Efficient memory management for large language model serving with pagedattention,

Reference 45

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source=pdf_text observed=2026-08-03T19:27:04.137806Z digest=sha256:d90d0a5dba0a42a66e5a63108ac74d64c22df877017428370e1c52828b78766b

Observation 21e7e325-f861-4d8f-bbab-b6817877390d · outbound

This paper cites sentence-transformers/msmarco- distilbert-base-tas-b,.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval sentence-transformers/msmarco- distilbert-base-tas-b,

Reference 46

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Observation 24ca1ca2-6c60-42e5-82fe-3d623ef8e3ef · outbound

This paper cites Dense Passage Retrieval for Open-Domain Question Answering.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval Dense Passage Retrieval for Open-Domain Question Answering

Reference 47

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source=pdf_text observed=2026-08-03T19:27:04.142059Z digest=sha256:6cfd112a9f49bd67114f41dbcd2aacb5b3f2d8903f0fc62b2fe3c203c0598e0d

Observation 512dd947-6950-4f2e-ae17-24c291a0cb45 · outbound

This paper cites deepseek-ai/deepseek-r1-distill-qwen-14b,.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval deepseek-ai/deepseek-r1-distill-qwen-14b,

Reference 48

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Observation 4224f31c-7ce1-4f43-a3fd-d3de2402345e · outbound

This paper cites Mahalanobis distance,.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval Mahalanobis distance,

Reference 49

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Observation 451d78c2-51fb-45dd-a5ba-e206a8e47aa2 · outbound

This paper cites sentence-transformers/sentence-t5-xl,.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval sentence-transformers/sentence-t5-xl,

Reference 50

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Observation 3e6f474f-d03e-43c0-8212-54c279fb0d5d · outbound

This paper cites A practical and secure byzantine robust aggregator,.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval A practical and secure byzantine robust aggregator,

Reference 51

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Observation 010ab32a-5d9b-416a-86b3-e3ffe57278f8 · outbound

This paper cites On Information and Sufficiency,.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval On Information and Sufficiency,

Reference 52

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source=pdf_text observed=2026-08-03T19:27:04.144063Z digest=sha256:86241333933a876c28a0de0e6de0fd45d8119692a9daee645e5b1699adb3d1b4

Observation c11d2e9a-d3cf-4401-9822-9645489a8627 · outbound

This paper cites Backdoor Attacks on Dense Retrieval via Public and Unintentional Triggers.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval Backdoor Attacks on Dense Retrieval via Public and Unintentional Triggers

Reference 53

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Observation 3060d36e-d190-468e-880e-c52eb1b3965b · outbound

This paper cites Attacks against abstractive text sum- marization models through lead bias and influence functions,.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval Attacks against abstractive text sum- marization models through lead bias and influence functions,

Reference 54

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Observation d9c48c89-2607-4ad0-9639-00d9283c0957 · outbound

This paper cites A simple unified framework for detecting out-of-distribution samples and adversarial attacks,.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval A simple unified framework for detecting out-of-distribution samples and adversarial attacks,

Reference 55

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Observation ad59bcd2-8b36-43ef-9dff-11f4bdd67e95 · outbound

This paper cites The probabilistic relevance framework: Bm25 and beyond,.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval The probabilistic relevance framework: Bm25 and beyond,

Reference 56

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Observation 5797fa05-e7c6-433f-b87a-930943a3654e · outbound

This paper cites How not to detect prompt injections with an llm,.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval How not to detect prompt injections with an llm,

Reference 57

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Observation 97bc7342-7258-4773-959c-277d2a846893 · outbound

This paper cites Beyond Relevance: Evaluate and Improve Retrievers on Perspective Awareness.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval Beyond Relevance: Evaluate and Improve Retrievers on Perspective Awareness

Reference 60

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Observation d5225bf4-bc36-4e15-a44e-82b970cd8464 · outbound

This paper cites Available: https://doi.org/10.1214/aoms/1177729694.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval Available: https://doi.org/10.1214/aoms/1177729694

Reference 1951

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Observation 4c252345-a8df-475e-a4a1-d96b2d63ea73 · outbound

This paper cites The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG).

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG)

Reference 2024

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Observation da57fdc1-1c2c-477a-ba01-84eaee264bf0 · outbound

This paper cites Through the Stealth Lens: Attention-Aware Defenses Against Poisoning in RAG.

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval Through the Stealth Lens: Attention-Aware Defenses Against Poisoning in RAG

Reference 2025

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Pith citing papers

Observation 0d3767f7-6d11-4cb0-99df-d183a53892d7 · inbound

Personalization Meets Safety:Mechanisms,Risks,and Mitigations in Personalized LLMs cites this paper.

Personalization Meets Safety:Mechanisms,Risks,and Mitigations in Personalized LLMs Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval

Reference 125

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source=pdf_text observed=2026-06-27T16:49:14.243931Z digest=sha256:3ddd9f1a444060fde738a2eab41cbbdc79da30cb408f123f552742eb56a9e14b