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Paper Citation Record · LEDGER

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval

As of 10 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 2026-08-10T06:31:04.303077+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=sha256:12fca826a6e5d66c43d797356e230c904ff00da4c4a36922b31b8cd01856f360

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=sha256:8655b692d17caf120e6a718f4f3123bb79d57b9c75d201815d4d40c1625e37e0

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:d048012bd036a57ad40a541f4a0542cb14fe9b28ad5fa0b6182a506939cee001

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=sha256:9b0022c7b172c57ca1d86b8222c821ba5483c6f2a173e776fa00461513a66949

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=sha256:0fce5291f7c057374be0157d01c028c5fe86ff30dbde0cae021e76595cb9494c

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=sha256:20db653015244d096d9d41f82fb8330809771bce50c66d03cd8e5268f195b812

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=sha256:143a1e99f6ac91589361cd8a0761122e05c542647da7f980eb80b6f5b96a1b9b

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:e095ad3224810cf10d8e59aed35722a89b4f6264485e7c19f3b4f04e3746a0f1

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:ac79a5a6d76d2e038ee0c0b44e7992d0af888fc49060cbf998d844e525610358

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=sha256:22f06f46165e5a108dd7ead6514b17988d41c461a77f3629f5c5cbf25d39d02c

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=sha256:32c498ea1eaa49297efedbeeba042e576e1446110cc89f9ea53fdb98b3d652db

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=sha256:1044b124520e852a3472b900a3e5869a2751e04bd70ab26752767117302e626e

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:a2a1080833577059ff0e8e6671b7ce53bc0ed0337125a2566e26c142d7808c19

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=sha256:253f59ddd0070006475efb546c590262a22909c3381be21ad4672a2ca2c39060

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:cd9660d2441f4bb5034244f38289cef22676db10f88c386adf300abbbf0bf14a

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:e1151f0d8543b94ffec8dfc7ec1890f796aa4d62be208a31090b485317648d1b

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=sha256:9bb8a8cddfa3233f4f7f22f5da8bc47f71889603d9e3181a783ce89a6c08396a

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:da052b4f15deb9c70700d2595f6fecb753db2f2d1a83d00ff63c913cc832bd01

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:c70534dca973715beedda44d62cd183ce7e67a587c093bbd3e4130f7d9bcb0f9

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=sha256:301e20b02a237c239b02eda6550458b9ca8afc5bfad5da9cc22d8cb9af1e8d11

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=sha256:0c92d10a0251329d5d54d56168f7e1bed801b3fd2a1205275e29bef1bbbbebbe

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=sha256:1f7a0874494ce5c3113b056e18ee890e94c92d0545496bac0be70222ca7ec51e

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:b3dad5f59554ad36ba6a4a80eed2bdad2e83347687af57283fa30599d5800067

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=sha256:122c9909724eeacc854a5393e2c0724815376a21035dc1f9cdb8381f9b5771fe

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:e2db886c855571a1ec91e6303b4937c6c9cb6274315627ac08d7fd432e2fec5d

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:cf35ed3b1fafddb8d378d40cd5cfd72bb2eb005fc4f5c8954513c4b262e1272b

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=sha256:66797c04f852b73ec30dba3ecf174c8f046679cf62c8ed9ef63a7f8c7a996c8f

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:ff87d0ebeac9f1b7fec123836fd2d8318f3cd5d40dd538c8d3ecbf40955a3171

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=sha256:9f99e5ce090243b51eb7fb6bdb8c356cfbde8018266cf186ef74c6a3c10bedc0

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=sha256:8014182efe0b21a0a8de2c926725ac7e826823d7bb73962ba7dd9856fda298c3

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:acba053e09ba2116d6f5b4136b86c38351a9ad574c294ac7d0468a3abd067fd5

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=sha256:2cdd04a3d9317e100421edeb4ef3f0bf576928c2194961b02c40bb5a8f680ca6

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=sha256:9e58fa53b165df680bd00f57e30e03a32391a6da0c3c48c057823d30044edd90

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:da9a14ae1c375a72623c14796445357b33cee4f34ee8ca13dfeebd2086891733

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:d0000d31037524fa06595e60dbe7b82a6ba2c6de00a9063987f2753ef29461a8

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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source=pdf_text observed=2026-08-03T19:27:04.118606Z digest=sha256:ef2e9ac5b8b50ad7b9496fe6edac48c6eba314b3ca50e6de5a96e152f6cdfb6c

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:f8c38c5d8bcdf3c79d6f7ee098812a84d28070728bc7d202f18f0345dfb5422f

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=sha256:644f6fc7dda260bbb16d077db601f9226a20798707ceb3b7a99ffb8a5fbe8073

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=sha256:07f98616d4e023d95bb71ac6b73c17fa150f1b97ccf4d78fca5aab3151f1ffad

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=sha256:71e124cafcdd1f910f3d051756541429eefc565ac9441ef1c084b06bb869ee46

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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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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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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source=pdf_text observed=2026-08-03T19:27:04.148190Z digest=sha256:dabb4891ebbb35e267da5bb9030ed406bd30f8368827c764d30d240028ced358

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=sha256:60208be42b202b3303dc16b1ee937cb9d5d5eb493c7e4976a57d3a29a18da95a

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=sha256:56fea4b92a5b91c5abf192cfc482d1299b1252887d1deb293a5b177d372cc994