mlsanityData to 7 Oct 2026

Structural Feature Engineering for Generative Engine Optimization: How Content Structure Shapes Citation Behavior

arXiv 2603.29979 ↗First posted 31 Mar 2026cs.CL cs.HC cs.IRcited by 5

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Abstract (arXiv)

The proliferation of AI-powered search engines has shifted information discovery from traditional link-based retrieval to direct answer generation with selective source citation, creating new challenges for content visibility. While existing Generative Engine Optimization (GEO) approaches focus primarily on semantic content modification, the role of structural features in influencing citation behavior remains underexplored. In this paper, we propose GEO-SFE, a systematic framework for structural feature engineering in generative engine optimization. Our approach decomposes content structure into three hierarchical levels: macro-structure (document architecture), meso-structure (information chunking), and micro-structure (visual emphasis), and models their impact on citation probability across different generative engine architectures. We develop architecture-aware optimization strategies and predictive models that preserve semantic integrity while improving structural effectiveness. Experimental evaluation across six mainstream generative engines demonstrates consistent improvements in citation rate (17.3 percent) and subjective quality (18.5 percent), validating the effectiveness and generalizability of the proposed framework. This work establishes structural optimization as a foundational component of GEO, providing a data-driven methodology for enhancing content visibility in LLM-powered information ecosystems.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 15 of its 29 references that are in the map. First posted on arXiv 31 Mar 2026.

Builds on

15 of 29 references.

References · 15

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  3. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,383
  4. 2023G-Eval: NLG Evaluation using GPT-4 with Better Human Alignmentcited by 2,205
  5. 2021WebGPT: Browser-assisted question-answering with human feedbackcited by 1,722
  6. 2022LaMDA: Language Models for Dialog Applicationscited by 1,218
  7. 2024M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillationcited by 1,050
  8. 2023Table Meets LLM: Can Large Language Models Understand Structured Table Data? A Benchmark and Empirical Studycited by 116
  9. 2024Attention Heads of Large Language Models: A Surveycited by 65
  10. 2023GEO: Generative Engine Optimizationcited by 53
  11. 2024RAG-Fusion: a New Take on Retrieval-Augmented Generationcited by 42
  12. 2025Retrieval-Augmented Generation: A Comprehensive Survey of Architectures, Enhancements, and Robustness Frontierscited by 26
3 more
  1. 2024Manipulating Large Language Models to Increase Product Visibilitycited by 24
  2. 2025Structured Attention Matters to Multimodal LLMs in Document Understandingcited by 8
  3. 2025Attention Mechanisms Perspective: Exploring LLM Processing of Graph-Structured Datacited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 5.

5 papers.

  1. 2026When Optimization Becomes Manipulation: Defending Generative Search against Malicious Generative Engine Optimizationcited by 1
  2. 2026Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomescited by 0
  3. 2026Scoring Without the Engine: Validating a Deterministic, Manipulation-Resistant Content Score for Generative Engines, End to Endcited by 0
  4. 2026CITECHOICE: A Causal Audit of How Document Presentation Redistributes Citation Credit in Agentic Searchcited by 0
  5. 2026What Drives Citations in Production Large Language Models? An Observational Multi-Method Study of Two Million AI Citations Across Ten Thousand Web Pagescited by 0