AI-Native Brand Identity: From Visual Recognition to Cryptographic Verification

arXiv (Cornell University)

Abstract

Brand identity verification technologies have historically changed discontinuously in response to shifts in the primary observer type. This paper formalizes the observer-driven evolution thesis and extends it to the current transition from human to AI agents. The Brand Function is defined as f(query, context, observer_type, time) → response, the complete behavioral specification machine observers require. Cryptographic signatures on this function are predicted to replace logos as the primary identity mechanism for text-primary AI agents. The framework introduces behavioral metamerism – brands converging on statistically indistinguishable profiles while differing in structurally relevant behavioral contingencies – an equivalence that statistical optimization, generative engine optimization included, cannot resolve. Six falsifiable propositions link observer type to identity technology, specification to coherence, and cryptographic attestation to computational trust. Existing protocols address discovery and transaction but omit perception measurement, behavioral specification, and coherence verification. The Brand Function fills these gaps by integrating spectral perception dimensions, observer-contingent decision rules, and computable coherence metrics. The analysis implies that brands must develop dual identity infrastructure: visual for humans, cryptographically attested behavioral specifications for machines. Theoretical contributions reposition brand equity as observer-contingent; managerial implications center on verifiable behavioral contracts that reduce adverse selection and moral hazard in delegated purchasing. Includes zharnikov-2026x-ai-native-brand-identity.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 github.com/spectralbranding/paper-spec for the standard. This PDF is generated programmatically from that machine-first source under a research-as-repository model.

Authors 5

1. Xiaoyu Zhang

   Shandong University

   Affiliation as printed

   Shandong University

2. Lingyong Yan

   Baidu (China)

   Affiliation as printed

   Baidu Inc

3. Maarten de Rijke

   University of Amsterdam

   Affiliation as printed

   University of Amsterdam

4. Zhaochun Ren Aachen

   Leiden University

   Affiliation as printed

   Leiden University

5. Xiuying Chen

   Mohamed bin Zayed University of Artificial Intelligence

   Affiliation as printed

   Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)