What decides citation in AI answer engines is content structure, not design. A March 2026 paper, "Structural Feature Engineering for GEO," found that structural optimization raised citation rate by 17.3% and quality ratings by 18.5% across six mainstream generative engines. It designs structure across three levels: macro, meso, and micro. ASAP summarizes how this meshes with our earlier analysis.

Structure raises citation rate by 17.3%

The paper found that optimizing content structure raises citation without changing meaning. Across six mainstream generative engines, it showed a 17.3% lift in citation rate and an 18.5% lift in subjective quality. Touching only the structure, while leaving content as is, raises citation visibility.

It splits structure into three levels

The paper's structure model is three levels: macro, meso, and micro. Macro is document architecture, meso is information chunking, and micro is visual emphasis. The key is designing these three levels so answer engines can parse them.

"Formatting" and "structure" are different

The structure here is different from pretty formatting. ASAP's "What Gets Cited" (arXiv 2605.25517) found that surface formatting like bold or ratings has little effect. The structure this paper means is the semantic structure a machine chunks and parses, which is a different layer.

The two studies do not conflict

The two studies are complementary rather than in conflict when put together. Surface decoration like color and weight has small effect, but the information structure a machine parses, like chunking and architecture, changes citation by 17.3%. The conclusion is to move effort from design to structural design.

What it means: the next step in AEO is structural design

The paper's lesson is that answer-engine optimization, or AEO, is shifting from presentation to structure. Structure a machine can chunk and read, more than a page pretty to human eyes, is what earns citation. Designing content across macro, meso, and micro is the new gateway.

Wrap-up

"Structural Feature Engineering for GEO" shows, with numbers, that structure decides AI citation. A 17.3% lift in citation and 18.5% in quality across six engines, plus a macro-meso-micro three-level design, are the core. Not pretty formatting but machine-parsable structure is the lever of citation.

Source: ASAP summary of "Structural Feature Engineering for Generative Engine Optimization" (arXiv 2603.29979, March 31, 2026; Junwei Yu et al., six generative engines, +17.3% citation and +18.5% quality, macro-meso-micro three levels).