Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes | Paper Citation Record

Observation 4a36c58e-2347-4dc5-b07b-87477a77cb56 · outbound

This paper cites Geo: Generative engine optimization.

Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes Geo: Generative engine optimization

Reference 1

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This paper cites Self-rag: Learning to retrieve, generate, and critique through self-reflection.

Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes Self-rag: Learning to retrieve, generate, and critique through self-reflection

Reference 2

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This paper cites E-GEO: A Testbed for Generative Engine Optimization in E-Commerce.

Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes E-GEO: A Testbed for Generative Engine Optimization in E-Commerce

Reference 3

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This paper cites Baeza-Yates.

Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes Baeza-Yates

Reference 4

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This paper cites A game-theoretic approach to recommendation systems with strategic content providers.

Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes A game-theoretic approach to recommendation systems with strategic content providers

Reference 5

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This paper cites Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models.

Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models

Reference 6

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This paper cites Adversarial web search.Foundations and trends in Information Retrieval, 4(5):377–486, 2011.

Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes Adversarial web search.Foundations and trends in Information Retrieval, 4(5):377–486, 2011

Reference 7

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This paper cites Cc-gseo-bench: A content-centric benchmark for measuring source influence in generative search engines.arXiv preprint arXiv:2509.05607, 2025.

Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes Cc-gseo-bench: A content-centric benchmark for measuring source influence in generative search engines.arXiv preprint arXiv:2509.05607, 2025

Reference 8

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Observation c71c6802-aaff-47a1-bff8-31a7acdeb50b · outbound

This paper cites Caption Injection for Optimization in Generative Search Engine.

Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes Caption Injection for Optimization in Generative Search Engine

Reference 9

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This paper cites Role-augmented intent-driven generative search engine optimization.arXiv preprint arXiv:2508.11158, 2025.

Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes Role-augmented intent-driven generative search engine optimization.arXiv preprint arXiv:2508.11158, 2025

Reference 10

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

Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes Agentpoison: Red-teaming llm agents via poisoning memory or knowledge bases

Reference 11

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This paper cites Shashidhar, Micheal Tuape, Dan Abudu, Beakcheol Jang, and Jong Wook Kim.

Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes Shashidhar, Micheal Tuape, Dan Abudu, Beakcheol Jang, and Jong Wook Kim

Reference 12

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This paper cites Ragas: Automated evaluation of retrieval augmented generation.

Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes Ragas: Automated evaluation of retrieval augmented generation

Reference 13

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Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes Unresolved cited work

Reference 14

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Observation 06a5c6e0-1781-4394-ad53-a8148d8503c2 · outbound

This paper cites Enabling large language models to generate text with citations.

Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes Enabling large language models to generate text with citations

Reference 15

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This paper cites Ranking robustness under adversarial document manipulations.

Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes Ranking robustness under adversarial document manipulations

Reference 16

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Observation 571a4f12-486c-4afa-a17c-6be8f7a2f72d · outbound

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

Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes Not what you’ve signed up for: Compromising real-world llm-integrated applications with indirect prompt injection

Reference 17

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This paper cites Link spam alliances.

Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes Link spam alliances

Reference 18

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This paper cites Web spam taxonomy.

Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes Web spam taxonomy

Reference 19

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This paper cites Strategic classification.

Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes Strategic classification

Reference 20

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Observation 8f79ca88-d58c-4ef8-ba98-4c3383f2fb14 · outbound

This paper cites Supply-side equilibria in recommender systems.

Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes Supply-side equilibria in recommender systems

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:17:46.953995Z

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This paper cites SAGEO Arena: A Realistic Environment for Evaluating Search-Augmented Generative Engine Optimization.

Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes SAGEO Arena: A Realistic Environment for Evaluating Search-Augmented Generative Engine Optimization

Reference 22

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This paper cites How do classifiers induce agents to invest effort strategi- cally? InProceedings of the 2019 ACM Conference on Economics and Computation (EC), 2019.

Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes How do classifiers induce agents to invest effort strategi- cally? InProceedings of the 2019 ACM Conference on Economics and Computation (EC), 2019

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:17:46.940507Z

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This paper cites Manipulating Large Language Models to Increase Product Visibility.

Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes Manipulating Large Language Models to Increase Product Visibility

Reference 24

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

Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes Retrieval-augmented generation for knowledge-intensive nlp tasks

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:17:46.927302Z

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Observation f32c07db-7e47-4750-84aa-11a61b3afc87 · outbound

This paper cites Liu, Tianyi Zhang, and Percy Liang.

Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes Liu, Tianyi Zhang, and Percy Liang

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:17:46.913605Z

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Observation 07870220-8e56-45e2-9e58-0ffd52236350 · outbound

This paper cites Prompt Injection attack against LLM-integrated Applications.

Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes Prompt Injection attack against LLM-integrated Applications

Reference 27

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This paper cites Think Before Writing: Feature-Level Multi-Objective Optimization for Generative Citation Visibility.

Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes Think Before Writing: Feature-Level Multi-Objective Optimization for Generative Citation Visibility

Reference 28

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This paper cites Playing repeated stackelberg games with unknown opponents.

Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes Playing repeated stackelberg games with unknown opponents

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:17:46.900610Z

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This paper cites Factscore: Fine-grained atomic evaluation of factual precision in long form text generation.

Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes Factscore: Fine-grained atomic evaluation of factual precision in long form text generation

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:17:46.886784Z

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Observation 60805782-73f0-4e2a-8ba3-c22d9b75aaa5 · outbound

This paper cites WebGPT: Browser-assisted question-answering with human feedback.

Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes WebGPT: Browser-assisted question-answering with human feedback

Reference 31

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This paper cites Algorithmic mechanism design.Games and Economic Behavior, 35(1–2):166–196, 2001.

Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes Algorithmic mechanism design.Games and Economic Behavior, 35(1–2):166–196, 2001

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:17:46.872695Z

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Observation ad9aa6e9-abb6-4196-a30d-a4b275006139 · outbound

This paper cites What’s new on the web? the evolution of the web from a search engine perspective.

Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes What’s new on the web? the evolution of the web from a search engine perspective

Reference 33

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Observation 1a469a85-a221-4e73-bfdc-e70777ac8890 · outbound

This paper cites Detecting spam web pages through content analysis.

Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes Detecting spam web pages through content analysis

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:17:46.847709Z

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Observation 5a5d4bde-f8dc-4d7d-8824-de94f0147fcd · outbound

This paper cites Adversarial text rewriting for text-aware recommender systems.

Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes Adversarial text rewriting for text-aware recommender systems

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:17:46.834210Z

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Observation 90863c0f-513d-47bb-a20a-a556b597b8e7 · outbound

This paper cites Perdomo, Tijana Zrnic, Celestine Mendler-Dünner, and Moritz Hardt.

Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes Perdomo, Tijana Zrnic, Celestine Mendler-Dünner, and Moritz Hardt

Reference 36

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Observation 9c56951d-0d03-431f-abd3-45905c0593fa · outbound

This paper cites Ignore Previous Prompt: Attack Techniques For Language Models.

Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes Ignore Previous Prompt: Attack Techniques For Language Models

Reference 37

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This paper cites C-seo bench: Does conversational seo work?arXiv preprint arXiv:2506.11097, 2025.

Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes C-seo bench: Does conversational seo work?arXiv preprint arXiv:2506.11097, 2025

Reference 38

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

Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes Towards More Robust Retrieval-Augmented Generation: Evaluating RAG Under Adversarial Poisoning Attacks

Reference 39

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This paper cites Springer Science & Business Media, 2010.

Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes Springer Science & Business Media, 2010

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:17:46.807632Z

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Observation cc70d001-2471-4130-9099-8e40f09a2847 · outbound

This paper cites The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions.

Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions

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Observation 0b3a0af2-c03d-4374-9136-66f9536ce5b9 · outbound

This paper cites From Experience to Skill: Multi-Agent Generative Engine Optimization via Reusable Strategy Learning.

Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes From Experience to Skill: Multi-Agent Generative Engine Optimization via Reusable Strategy Learning

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Resolution: verified exact; local_arxiv, observed 2026-08-15T14:17:46.388302Z

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Observation 87b3ccdf-7cbe-4576-8efb-bb92265879e5 · outbound

This paper cites What generative search engines like and how to optimize web content cooperatively.arXiv preprint arXiv:2510.11438, 2025.

Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes What generative search engines like and how to optimize web content cooperatively.arXiv preprint arXiv:2510.11438, 2025

Reference 43

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Observation 098d11d2-b13d-4472-ab05-f4d59b682cbf · outbound

This paper cites Certifiably robust rag against retrieval corruption.arXiv preprint arXiv:2405.15556, 2024.

Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes Certifiably robust rag against retrieval corruption.arXiv preprint arXiv:2405.15556, 2024

Reference 44

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Observation ef5ad5f2-7b23-4350-808d-0ac5132f80f3 · outbound

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

Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes BadRAG: Identifying Vulnerabilities in Retrieval Augmented Generation of Large Language Models

Reference 45

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Observation d5f33fa8-e0a4-48af-aff2-4d22cf5f61c3 · outbound

This paper cites Structural feature engineering for generative engine optimization: How content structure shapes citation behavior.arXiv preprint arXiv:2603.29979, 2026.

Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes Structural feature engineering for generative engine optimization: How content structure shapes citation behavior.arXiv preprint arXiv:2603.29979, 2026

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Resolution: unresolved; no resolver link, observed 2026-08-15T14:17:45.919026Z

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Observation b7adf0cb-4c70-4eea-ab4c-1eb4ba058fba · outbound

This paper cites Agenticgeo: A self-evolving agentic system for generative engine optimization.arXiv preprint arXiv:2603.20213, 2026.

Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes Agenticgeo: A self-evolving agentic system for generative engine optimization.arXiv preprint arXiv:2603.20213, 2026

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Observation ce9c2330-ae29-428c-9156-4aa79a6d6fa2 · outbound

This paper cites Poisoning retrieval corpora by injecting adversarial passages.

Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes Poisoning retrieval corpora by injecting adversarial passages

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:17:46.792999Z

Source-reported events for the cited work

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Observation 1cd819b7-8489-4017-9112-a3518797b846 · outbound

This paper cites If-geo: Conflict- aware instruction fusion for multi-query generative engine optimization.arXiv preprint arXiv:2601.13938, 2026.

Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes If-geo: Conflict- aware instruction fusion for multi-query generative engine optimization.arXiv preprint arXiv:2601.13938, 2026

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Resolution: verified exact; raw_fallback, observed 2026-08-15T14:17:46.123620Z

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Observation 0eeda9df-1f99-495a-9f2b-6490bea5fe6b · outbound

This paper cites optimized for GEO.

Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes optimized for GEO

Reference 50

Resolution: malformed identifier; raw_fallback, observed 2026-08-15T14:17:46.058895Z

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Observation d1b4abff-3de6-4983-a7d6-24a6a1abd2da · outbound

This paper cites Listing 13|Quality judge for target-document selection.

Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes Listing 13|Quality judge for target-document selection

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:17:46.779868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Reference resolution

Outbound references

Pith citing papers