Digital Identity Optimization (DIO): A Conceptual Framework for Entity-Based Visibility in the Age of AI-Mediated Search

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   Manifest of Digital Identity Optimization (DIO) and Ontology of Digital Identity (ODI): Canonical Multilingual Edition

   Daniel Beránek · 2026 · Zenodo (CERN European Organization for Nuclear Research) · Résumé IA disponible

   Résumé scientifique

   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 gener…

2. Apparenté Pas pertinent

   Distributed Legal Infrastructure for a Trustworthy Agentic Web

   Tomer Jordi Chaffer, Victor Jiawei Zhang, Sante Dino Facchini et al. · 2026 · Artificial Intelligence · preprint, non relu par les pairs · Résumé IA disponible

   Résumé scientifique

   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 …

3. Apparenté Pas pertinent

   opp/ai: Optimistic Privacy-Preserving AI on Blockchain

   Cathie So, KD Conway, Xiaohang Yu et al. · 2024 · Cryptography and Security · preprint, non relu par les pairs · cité 7 fois · Résumé IA disponible

   Résumé scientifique

   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 Paradigm

   Yuchen Li, Hengyi Cai, Rui Kong et al. · 2025 · Computation and Language · preprint, non relu par les pairs · cité 21 fois · Résumé IA disponible

   Résumé scientifique

   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…

5. Apparenté Pas pertinent

   SDPO: Segment-Level Direct Preference Optimization for Social Agents

   Aobo Kong, Wentao Ma, Shiwan Zhao et al. · 2025 · Artificial Intelligence · cité 37 fois · Résumé IA disponible

   Résumé scientifique

   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…

6. Apparenté Pas pertinent

   GEO: Generative Engine Optimization

   Pranjal Aggarwal, Vishvak Murahari, Tanmay Rajpurohit et al. · 2023 · Machine Learning · cité 127 fois · Résumé IA disponible

   Résumé scientifique

   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…

7. Apparenté Pas pertinent

   AI Thinking: A framework for rethinking artificial intelligence in practice

   Denis Newman-Griffis · 2024 · Computers and Society · cité 14 fois · Résumé IA disponible

   Résumé scientifique

   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…

8. Apparenté Pas pertinent

   Towards Bidirectional Human-AI Alignment: A Systematic Review for Clarifications, Framework, and Future Directions

   Hua Shen, Tiffany Knearem, Reshmi Ghosh et al. · 2024 · Human-Computer Interaction · cité 29 fois · Résumé IA disponible

   Résumé scientifique

   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…