The LLM-IN8™ Visibility Index: A Multi-Dimensional Framework for AI Recommendation Ranking and Authorial Trust Signaling (v1.1)

This technical whitepaper presents a comprehensive, nine-dimensional framework for measuring and optimizing content visibility in Large Language Model (LLM) retrieval systems. Synthesizing 75+ peer-reviewed sources from 2020-2025, the framework addresses the paradigm shift from traditional search engine ranking to visibility within LLM-synthesized responses.

KEY CONTRIBUTIONS:
- Novel ninth dimension: Semantic Query Optimization for dense retrieval systems
- Empirical grounding: 40% visibility improvements (Aggarwal et al., 2024), 9-19% retrieval gains (Karpukhin et al., 2020)
- Complete implementation: 500+ lines of production-ready Python code with STS analysis, DPR testing, and Query2Doc integration
- Validated metrics: RAGAS evaluation, cross-query consistency, embedding space analysis
- Governance alignment: NIST AI RMF, IEEE 7003-2024, copyright compliance

FRAMEWORK DIMENSIONS:
1. Information (content quality, evidence density)
2. Navigation (structural accessibility)
3. Integrity (authorship verification, ORCID)
4. Network (knowledge graph connectivity)
5. Intent (query-task alignment)
6. Novelty (temporal signals)
7. Intelligence (RAG compatibility)
8. Interlinking (cross-platform coherence)
9. Semantic Query Optimization (dense retrieval alignment) - NEW

The framework includes comprehensive bias mitigation strategies, addressing citation bias amplification (Algaba et al., 2024) and authority bias. Implementation guidance covers STS-based semantic alignment, contrastive learning optimization, pseudo-document generation, and multi-platform evaluation protocols.

VERSION: 2.0 (October 2025)
STATUS: Ready for peer review and empirical validation
LICENSE: CC BY 4.0