Digital Identity Optimization (DIO): A Conceptual Framework for Entity-Based Visibility in the Age of AI-Mediated Search
Authors: Daniel Beránek
Abstract
The emergence of AI-driven search and answer engines has fundamentally disrupted the logic of digital visibility. Traditional Search Engine Optimization (SEO), built on keyword-document matching, and AI Optimization (AIO), focused on answer-engine citation, provide increasingly insufficient responses to a landscape in which artificial intelligence recommends entities rather than ranks pages. This paper theorizes Digital Identity Optimization (DIO) as a third-generation framework for digital visibility. Building on Beránek’s (2026) foundational manifest, it defines DIO as the systematic optimization of an entity’s digital identity—its distributed digital representation across human and machine interpretive systems—so that AI systems, search engines, and human audiences can consistently, correctly, and confidently identify, understand, and recommend that entity. The paper develops the framework along three analytical axes: (1) comparative complexity of SEO, AIO, and DIO; (2) differential value across stakeholder profiles; and (3) multilateral digital identity structures. For large multi-product corporations, the latter is developed as a brand–category/segment–product model; other entity types may exhibit analogous but different structures. Conceptually, DIO is presented as a bottom-up discovery rather than a top-down fabrication. It crystallizes a latent intersection of psychology, semiotics and post-structuralist thought, branding, SEO, structured data, knowledge graphs, and AI retrieval. Practically, it requires alignment across three domains: human-facing interpretation, pre-AI technical representation, and AI-mediated reconstruction.This record contains the identical preprint first published on ResearchGate: Beránek, D. (2026). Digital Identity Optimization (DIO): A Conceptual Framework for Entity-Based Visibility in the Age of AI-Mediated Search. ResearchGate. https://doi.org/10.13140/RG.2.2.17127.41120
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Manifest of Digital Identity Optimization (DIO) and Ontology of Digital Identity (ODI): Canonical Multilingual EditionDaniel Beránek · 2026 · Zenodo (CERN European Organization for Nuclear Research) · AI summary available
The manifest establishes DIO and ODI as frameworks for representing digital identity, which this paper develops into a visibility model for AI-mediated search.
In short
The abstract presents DIO and ODI as a multilingual framework for conceptualizing and maintaining digital identity, but reports no empirical evaluation.
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Distributed Legal Infrastructure for a Trustworthy Agentic WebTomer Jordi Chaffer, Victor Jiawei Zhang, Sante Dino Facchini et al. · 2026 · Artificial Intelligence · preprint, not peer reviewed · AI summary available
Distributed Legal Infrastructure addresses identity, delegation, and accountability among autonomous agents, rather than optimizing entities’ visibility in AI-mediated search.
Abstract
The agentic web marks a structural transition from a human-centered information network to a digital environment populated by artificial intelligence (AI) agents that perceive, decide, and act autonomously. As delegated action unfolds at machine speed, exceeds discrete moments of human judgment, and distributes decision-making across non-human actors, existing legal frameworks face growing strain, creating an urgent need for new mechanisms capable of sustaining legality in this emerging order. A trustworthy agentic web therefore depends on the infrastructuring of legality through interoperable protocols that organize identity, delegation, and accountability across systems, enabling coherent …
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opp/ai: Optimistic Privacy-Preserving AI on BlockchainCathie So, KD Conway, Xiaohang Yu et al. · 2024 · Cryptography and Security · preprint, not peer reviewed · cited 7 times · AI summary available
opp/ai combines zero-knowledge and optimistic machine learning for private, efficient blockchain AI, not entity-based digital visibility.
Abstract
The convergence of Artificial Intelligence (AI) and blockchain technology is reshaping the digital world, offering decentralized, secure, and efficient AI services on blockchain platforms. Despite the promise, the high computational demands of AI on blockchain raise significant privacy and efficiency concerns. The Optimistic Privacy-Preserving AI (opp/ai) framework is introduced as a pioneering solution to these issues, striking a balance between privacy protection and computational efficiency. The framework integrates Zero-Knowledge Machine Learning (zkML) for privacy with Optimistic Machine Learning (opML) for efficiency, creating a hybrid model tailored for blockchain AI services. This st…
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Towards AI Search ParadigmYuchen Li, Hengyi Cai, Rui Kong et al. · 2025 · Computation and Language · preprint, not peer reviewed · cited 21 times · AI summary available
Towards AI Search Paradigm designs agent workflows for answering information needs, while this paper concerns how entities are represented and recommended by AI.
Abstract
In this paper, we introduce the AI Search Paradigm, a comprehensive blueprint for next-generation search systems capable of emulating human information processing and decision-making. The paradigm employs a modular architecture of four LLM-powered agents (Master, Planner, Executor and Writer) that dynamically adapt to the full spectrum of information needs, from simple factual queries to complex multi-stage reasoning tasks. These agents collaborate dynamically through coordinated workflows to evaluate query complexity, decompose problems into executable plans, and orchestrate tool usage, task execution, and content synthesis. We systematically present key methodologies for realizing this par…
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SDPO: Segment-Level Direct Preference Optimization for Social AgentsAobo Kong, Wentao Ma, Shiwan Zhao et al. · 2025 · Artificial Intelligence · cited 37 times · AI summary available
SDPO trains social agents for goal-oriented dialogue using segment-level preference optimization, rather than optimizing digital identity for search visibility.
Abstract
Social agents powered by large language models (LLMs) can simulate human social behaviors but fall short in handling complex goal-oriented social dialogues. Direct Preference Optimization (DPO) has proven effective in aligning LLM behavior with human preferences across a variety of agent tasks. Existing DPO-based approaches for multi-turn interactions are divided into turn-level and session-level methods. The turn-level method is overly fine-grained, focusing exclusively on individual turns, while session-level methods are too coarse-grained, often introducing training noise. To address these limitations, we propose Segment-Level Direct Preference Optimization (SDPO), which focuses on specif…
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GEO: Generative Engine OptimizationPranjal Aggarwal, Vishvak Murahari, Tanmay Rajpurohit et al. · 2023 · Machine Learning · cited 127 times · AI summary available
GEO optimizes content for generative engines’ answers, while DIO focuses on the broader digital identity of entities recommended by AI systems.
Abstract
The advent of large language models (LLMs) has ushered in a new paradigm of search engines that use generative models to gather and summarize information to answer user queries. This emerging technology, which we formalize under the unified framework of generative engines (GEs), can generate accurate and personalized responses, rapidly replacing traditional search engines like Google and Bing. Generative Engines typically satisfy queries by synthesizing information from multiple sources and summarizing them using LLMs. While this shift significantly improves $\textit{user}$ utility and $\textit{generative search engine}$ traffic, it poses a huge challenge for the third stakeholder -- website…
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AI Thinking: A framework for rethinking artificial intelligence in practiceDenis Newman-Griffis · 2024 · Computers and Society · cited 14 times · AI summary available
AI Thinking offers an interdisciplinary framework for decisions about AI use, rather than a framework for entity-based visibility in AI search.
Abstract
Artificial intelligence is transforming the way we work with information across disciplines and practical contexts. A growing range of disciplines are now involved in studying, developing, and assessing the use of AI in practice, but these disciplines often employ conflicting understandings of what AI is and what is involved in its use. New, interdisciplinary approaches are needed to bridge competing conceptualisations of AI in practice and help shape the future of AI use. I propose a novel conceptual framework called AI Thinking, which models key decisions and considerations involved in AI use across disciplinary perspectives. The AI Thinking model addresses five practice-based competencies…
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Towards Bidirectional Human-AI Alignment: A Systematic Review for Clarifications, Framework, and Future DirectionsHua Shen, Tiffany Knearem, Reshmi Ghosh et al. · 2024 · Human-Computer Interaction · cited 29 times · AI summary available
Towards Bidirectional Human-AI Alignment reviews alignment definitions and proposes a framework, whereas this paper conceptualizes digital identity optimization for AI-mediated visibility.
Abstract
Recent advancements in general-purpose AI have highlighted the importance of guiding AI systems towards the intended goals, ethical principles, and values of individuals and groups, a concept broadly recognized as alignment. However, the lack of clarified definitions and scopes of human-AI alignment poses a significant obstacle, hampering collaborative efforts across research domains to achieve this alignment. In particular, ML- and philosophy-oriented alignment research often views AI alignment as a static, unidirectional process (i.e., aiming to ensure that AI systems' objectives match humans) rather than an ongoing, mutual alignment problem [429]. This perspective largely neglects the lon…
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