Manifest of Digital Identity Optimization (DIO) and Ontology of Digital Identity (ODI): Canonical Multilingual Edition
Authors: Daniel Beránek
Abstract
This repository establishes the definitive, canonical, and persistent archival edition of the Manifest of Digital Identity Optimization (DIO) and its theoretical core, the Ontology of Digital Identity (ODI). DIO and ODI represent a fundamental shift in how digital presence is conceptualized, structured, and maintained. Moving beyond legacy SEO (Search Engine Optimization) and fragmented technical identifiers, the framework introduces an autonomous, holistic system for the orchestration of digital identity. It formalizes the complex, dynamic processes through which human identities, brand architectures, and informational intents are represented, trusted, and continually reconstructed by generative artificial intelligence, algorithmic semantic layers, and human perception. First articulated as a Czech-language manifest at danielberanek.cz in June 2026, the system is deployed here across sixteen rigorous linguistic projections. Each version carefully accommodates the specific cultural and linguistic nuances of its environment while strictly preserving a shared conceptual invariant. This multilingual architectural design ensures seamless cross-lingual consistency and global accessibility. This canonical record establishes a stable reference for interpretation, scholarly reuse, citation, and further interdisciplinary development in information science, semantic web studies, digital epistemology, and related fields.The artifact includes the source manifesto in Czech and 15 additional language projections (EN, DE, FR, ES, IT, PT, PL, FI, HU, UK, ZH, JA, KO, AR, HI).
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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 · 2026 · Artificial Intelligence · preprint, not peer reviewed · AI summary available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 examines identity and accountability protocols for autonomous AI agents, while DIO addresses how identities are represented and maintained across AI systems.
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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OLMo: Accelerating the Science of Language ModelsDirk Groeneveld, Iz Beltagy, Pete Walsh et al. · 2024 · Computation and Language · cited 785 times · AI summary available
OLMo provides an open language model and research framework, while DIO is a multilingual conceptual archive concerning identity in generative AI.
Abstract
Language models (LMs) have become ubiquitous in both NLP research and in commercial product offerings. As their commercial importance has surged, the most powerful models have become closed off, gated behind proprietary interfaces, with important details of their training data, architectures, and development undisclosed. Given the importance of these details in scientifically studying these models, including their biases and potential risks, we believe it is essential for the research community to have access to powerful, truly open LMs. To this end, this technical report details the first release of OLMo, a state-of-the-art, truly Open Language Model and its framework to build and study the…
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Towards Digital Nature: Bridging the Gap between Turing Machine Objects and Linguistic Objects in LLMMs for Universal Interaction of Object-Oriented DescriptionsYoichi Ochiai, Naruya Kondo, Tatsuki Fushimi · 2023 · Human-Computer Interaction · preprint, not peer reviewed · cited 3 times · AI summary available
Towards Digital Nature connects language-model representations to computational objects and human perception, themes adjacent to DIO’s account of digital identity.
Abstract
In this paper, we propose a novel approach to establish a connection between linguistic objects and classes in Large Language Model Machines (LLMMs) such as GPT3.5 and GPT4, and their counterparts in high level programming languages like Python. Our goal is to promote the development of Digital Nature: a worldview where digital and physical realities are seamlessly intertwined and can be easily manipulated by computational means. To achieve this, we exploit the inherent abstraction capabilities of LLMMs to build a bridge between human perception of the real world and the computational processes that mimic it. This approach enables ambiguous class definitions and interactions between objects …
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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 develops preference optimization for social-agent dialogue, whereas DIO considers how identities and intents are represented by AI systems.
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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Digital Twin sensors in cultural heritage applicationsFranco Niccolucci, Achille Felicetti · 2024 · Sensors 2024, 24, 3978 · cited 47 times · Not summarized yet
Digital Twin sensors extends an ontology for cultural-heritage systems, while DIO presents an ontology of digital identity.
Abstract
The paper concerns the extension of the Heritage Digital Twin Ontology introduced in previous work to describe the reactivity of digital twins used for cultural heritage documentation by including the semantic description of sensors and activators and all the process of interacting with the real world. After analysing previous work on the use of digital twins in cultural heritage, a summary description of the Heritage Digital Twin Ontology is provided, and the existing applications of digital twins to cultural heritage are overviewed, with references to reviews summarizing the large production of scientific contributions on the topic. Then a novel ontology, named Reactive Digital Twin Ontolo…
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Media of Langue: The Interface for Exploring Word Translation Network/SpaceGoki Muramoto, Atsuki Sato, Takayoshi Koyama · 2023 · Computation and Language · preprint, not peer reviewed · Not summarized yet
Media of Langue presents a multilingual interface for exploring word-translation networks, while DIO provides multilingual editions designed to preserve conceptual consistency.
Abstract
In the human activity of word translation, two languages face each other, mutually searching their own language system for the semantic place of words in the other language. We discover the huge network formed by the chain of these mutual translations as Word Translation Network, a network where words are nodes, and translation volume is represented as edges, and propose Media of Langue, a novel interface for exploring this network. Media of Langue points to the semantic configurations of many words in multiple languages at once, containing the information of existing dictionaries such as bilingual and synonym dictionaries. We have also implemented and published this interface as a web appli…
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Unifying Vision, Text, and Layout for Universal Document ProcessingZineng Tang, Ziyi Yang, Guoxin Wang et al. · 2022 · Computer Vision and Pattern Recognition · cited 211 times · AI summary available
UDOP unifies text, image, and layout for document processing, while DIO concerns the representation of identity across semantic and AI systems.
Abstract
We propose Universal Document Processing (UDOP), a foundation Document AI model which unifies text, image, and layout modalities together with varied task formats, including document understanding and generation. UDOP leverages the spatial correlation between textual content and document image to model image, text, and layout modalities with one uniform representation. With a novel Vision-Text-Layout Transformer, UDOP unifies pretraining and multi-domain downstream tasks into a prompt-based sequence generation scheme. UDOP is pretrained on both large-scale unlabeled document corpora using innovative self-supervised objectives and diverse labeled data. UDOP also learns to generate document im…
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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 proposes optimizing content visibility in generative search, while DIO frames digital presence as the broader orchestration of identity, brands, and intent.
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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Updated on 1 November 2026.