GEO algorithm thesis — how LLMs score and synthesize sources
What GEO is and why it differs from SEO
Generative Engine Optimization is the practice of shaping how AI language models represent your entity when producing a collapsed answer — not a ranked list of links, but the single synthesized response a user receives. Winning GEO means becoming the answer itself. The source proposes a seven-factor scoring model that LLMs implicitly apply, with weights summing to 100%: Source Authority (28%), Cross-Source Consensus (22%), Entity Coherence (18%), Structural Parsability (14%), Recency Signal (10%), Community Validation (5%), and Semantic Density (3%). These figures are the authors' analytical framework, not published model documentation, but the underlying dynamics align with what we observe in ai/chatgpt-citation-signals and overview/visibility-readiness.
The four factors that move the needle most
Source authority is the single heaviest weight because LLMs are trained on tiered corpora — Wikipedia, .gov, and peer-reviewed publications sit at Tier 1 and anchor a model's base representation of any entity. Tier 2 outlets (major national publications) serve as recency anchors at retrieval time. Generic web content is essentially noise that cannot establish recognition on its own. Cross-source consensus is the mechanism that removes hedging: when three or more independent Tier 1–2 sources describe your entity in consistent terms, the model shifts from "some sources suggest…" to a confident, unqualified answer. Entity coherence — the consistency of your name, category, and description across every source — determines whether the model treats you as one stable node or fragments your identity across ambiguous variants. This is the GEO equivalent of concepts/nap-consistency for local SEO, and the stakes are higher because inconsistency produces absent or hedged answers rather than just a ranking drop. Structural parsability rewards content built with clear headers, tables, and schema markup — the same structural discipline described in geo/schema-jsonld and geo/answer-first-content.
How the three major models differ
Each LLM applies its own weight profile on top of the shared framework. ChatGPT weights consensus most aggressively — three aligned sources across a Tier 1 outlet, a major publication, and Reddit is described as its "GEO moat." Gemini is unique in that Google's PageRank heritage and E-E-A-T criteria are embedded in its training, meaning strong Google Search performance compounds into Gemini visibility; it also weighs YouTube transcripts and video metadata as a signal unavailable to any other model, which is worth noting for agents building video content tied to entities/google-business-profile. Claude is the most selective: its Constitutional AI curation filtered out low-quality training content, so one genuinely authoritative, well-structured source moves it more than many thin ones, and it places higher relative weight on entity coherence (22%) than the other two models do.
Practical priority stack for real estate agents
The source's recommended action stack, translated to a real estate context: (1) establish or maintain a structured, cited presence on high-authority reference sources; (2) earn named attribution in at least three Tier 2 outlets to trigger consensus signals; (3) build organic community presence on forums like Reddit, which functions as a human-trust proxy in training data; (4) publish long-form structured content — the kind with defined headers and data tables that geo/schema-jsonld markup makes even more extractable; and (5) for Gemini specifically, optimize any YouTube presence with consistent entity naming in titles, descriptions, and transcripts. Recency matters most for new or rapidly changing entities; established agents with coherent, widely cited profiles benefit from the training-weight anchor that freshness cannot easily displace. All of this feeds into the broader scoring logic described in overview/visibility-readiness.