mlsanityData to 14 Oct 2026

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

arXiv 2608.11390 ↗First posted 11 Aug 2026cs.LGcited by 0

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Abstract (arXiv)

Generative engines are reshaping the web ecosystem by making citations a key mechanism for allocating attention, attribution, and downstream value. This creates a strategic tension: content providers are incentivized to optimize for model citation, while platforms must preserve answer quality and trustworthy attribution. We show that this tension can escalate into citation wars. In repeated simulations, state-of-the-art generative engine optimization (GEO) attacks adapt to conventional defenses by producing citation-seeking rewrites that degrade document quality and introduce unsupported claims. To study this problem, we formulate the supplier--platform interaction as a repeated Stackelberg game with partial monitoring. A local best-response analysis identifies when citation competition approaches an inert stationary outcome. Motivated by this finding, we propose a platform--creator mechanism called VCR based on verifiable-content rewards. Rather than only penalizing suspicious rewrites, the platform also credits rewrites that surface checkable factual substance, aligning creator incentives with answer trustworthiness. Experiments on three benchmarks show that VCR consistently achieves the largest Net defense-utility score, outperforming the strongest baseline by an average of 12.1 percentage points, and produces a win--win outcome under our empirical equivalence criterion.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 40 of its 51 references that are in the map. First posted on arXiv 11 Aug 2026.

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References · 40

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,839
  3. 2021WebGPT: Browser-assisted question-answering with human feedbackcited by 1,745
  4. 2023Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injectioncited by 1,443
  5. 2023FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generationcited by 1,282
  6. 2022Ignore Previous Prompt: Attack Techniques For Language Modelscited by 832
  7. 2023Prompt Injection attack against LLM-integrated Applicationscited by 605
  8. 2023Enabling Large Language Models to Generate Text with Citationscited by 595
  9. 2024The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructionscited by 493
  10. 2024AgentPoison: Red-teaming LLM Agents via Poisoning Memory or Knowledge Basescited by 429
  11. 2023Ragas: Automated Evaluation of Retrieval Augmented Generationcited by 418
  12. 2020Performative Predictioncited by 327
28 more
  1. 2023Evaluating Verifiability in Generative Search Enginescited by 303
  2. 2024PoisonedRAG: Knowledge Corruption Attacks to Retrieval-Augmented Generation of Large Language Modelscited by 267
  3. 2015Strategic Classificationcited by 257
  4. 2023Poisoning Retrieval Corpora by Injecting Adversarial Passagescited by 159
  5. 2024Certifiably Robust RAG against Retrieval Corruptioncited by 151
  6. 2022Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Modelscited by 112
  7. 2024BadRAG: Identifying Vulnerabilities in Retrieval Augmented Generation of Large Language Modelscited by 86
  8. 2018How Do Classifiers Induce Agents To Invest Effort Strategically?cited by 83
  9. 2023GEO: Generative Engine Optimizationcited by 56
  10. 2018A Game-Theoretic Approach to Recommendation Systems with Strategic Content Providerscited by 33
  11. 2022Supply-Side Equilibria in Recommender Systemscited by 33
  12. 2024Manipulating Large Language Models to Increase Product Visibilitycited by 28
  13. 2018Ranking Robustness Under Adversarial Document Manipulationscited by 15
  14. 2024Towards More Robust Retrieval-Augmented Generation: Evaluating RAG Under Adversarial Poisoning Attackscited by 14
  15. 2025What Generative Search Engines Like and How to Optimize Web Content Cooperativelycited by 13
  16. 2025C-SEO Bench: Does Conversational SEO Work?cited by 11
  17. 2025Defending Against Knowledge Poisoning Attacks During Retrieval-Augmented Generationcited by 11
  18. 2025E-GEO: A Testbed for Generative Engine Optimization in E-Commercecited by 8
  19. 2026AgenticGEO: A Self-Evolving Agentic System for Generative Engine Optimizationcited by 6
  20. 2025Role-Augmented Intent-Driven Generative Search Engine Optimizationcited by 5
  21. 2026SAGEO Arena: A Realistic Environment for Evaluating Search-Augmented Generative Engine Optimizationcited by 5
  22. 2026Structural Feature Engineering for Generative Engine Optimization: How Content Structure Shapes Citation Behaviorcited by 5
  23. 2026From Experience to Skill: Multi-Agent Generative Engine Optimization via Reusable Strategy Learningcited by 4
  24. 2025CC-GSEO-Bench: A Content-Centric Benchmark for Measuring Source Influence in Generative Search Enginescited by 3
  25. 2026IF-GEO: Conflict-Aware Instruction Fusion for Multi-Query Generative Engine Optimizationcited by 3
  26. 2026Think Before Writing: Feature-Level Multi-Objective Optimization for Generative Citation Visibilitycited by 3
  27. 2025Caption Injection for Optimization in Generative Search Enginecited by 2
  28. 2024Adversarial Text Rewriting for Text-aware Recommender Systemscited by 1

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