Brand portfolio theory predicts perceptual interference when observers recognize shared corporate ownership, requiring only an open awareness gate. Large language models (LLMs), whose training data permanently saturate this gate, provide a critical test. This paper formalizes spectral interference across eight perceptual dimensions and tests three propositions in a fully crossed experiment with 13 LLMs from seven training traditions, 40 brands, seven portfolio archetypes, and four prompt modalities (N = 9, 925). Using the Dimensional Concentration Index and two-one-sided equivalence tests, the results reveal near-zero portfolio-induced change (mean \ (||\) =. 26; equivalence holds for 18/20 brands). Variance decomposition attributes. 1% of perceptual concentration to portfolio framing versus 37. 4% to brand identity. The sole exception occurs in multi-turn conversation for a reverse-aspiration structure (Geely Auto, d = -1. 11), where extended context converts coordination information into output inferences. These results resolve the awareness-gate paradox: awareness is necessary but insufficient. A bandwidth constraint rooted in rate-distortion theory and rational inattention is also required. General-purpose LLMs are rationally inattentive to organizational-coordination signals because these contribute negligibly to output-fidelity reconstruction. As AI mediation of brand interactions grows, portfolio architecture becomes strategically invisible to the general-purpose LLM class tested here. Includes zharnikov-2026ac-r21. yaml (Paper Spec v0. 1. 0) – a machine-readable specification of the paper's claims, assumptions, and dependencies. The paper's full machine-first bundle (the SPINE claim/dependency graph and the ONTOLOGY term module) lives in the public repository; see https: //github. com/spectralbranding/paper-spec for the standard. This PDF is generated programmatically from that machine-first source under a research-as-repository model.
Dmitry Zharnikov (Sat,) studied this question.
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