The Crisis of Entity Resolution in Heterogeneous AI Environments: Data, Financial, and Semantic Dimensions

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1. Très proche Pas pertinent

   From Entity Resolution Anomalies to Reconstruction Failure Modes: A Taxonomic Note Following Minimum Viable Digital Identity Optimization (DIO)

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

   Résumé scientifique

   This taxonomic note refines the vocabulary of Entity Resolution Anomalies developed in The Crisis of Entity Resolution in Heterogeneous AI Environments and follows the adversarial reduction introduced in Minimum Viable Digital Identity Optimization (DIO): From Adversarial Reduction to the Semantic Field Dynamics of Digital Identity. It proposes a non-exclusive, multi-level diagnostic taxonomy of Reconstruction Failure Modes. The taxonomy distinguishes four analytical domains: Referential Reconstruction Failures, Semantic Coherence Failures, Temporal Reconstruction Failures, and Contextual Adequacy Failures. Referential failures include Entity Substitution, Merge Failure, Split Failure, and E…

2. Très proche Pas pertinent

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

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

   Résumé scientifique

   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 machin…

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   Constraining the Commit Boundary: A DIO/ODI-Informed Authorization Guardrail for Agentic Entity Resolution

   Daniel Beránek · 2026 · Artificial Intelligence · Résumé IA disponible

   Résumé scientifique

   Agentic Entity Resolution (ER) systems iteratively plan actions, retrieve evidence, revise hypotheses, and propose graph mutations. An erroneous identity hypothesis may condition later retrieval and produce apparent confirmation. This paper proposes a Digital Identity Optimization (DIO) and Ontology of Digital Identity (ODI)-informed authorization boundary between evidence available for agent reasoning and evidence eligible to authorize persistent identity-changing mutations. A domain-governed constraint-binding function maps selected reconstruction-failure distinctions to explicit identity-preserving constraints. An abstract two-phase supervisor permits a commit only when claim-specific aut…

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   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…

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   Yutaro Yamada, Robert Tjarko Lange, Cong Lu et al. · 2025 · Artificial Intelligence · preprint, non relu par les pairs · cité 499 fois · Résumé IA disponible

   Résumé scientifique

   AI is increasingly playing a pivotal role in transforming how scientific discoveries are made. We introduce The AI Scientist-v2, an end-to-end agentic system capable of producing the first entirely AI generated peer-review-accepted workshop paper. This system iteratively formulates scientific hypotheses, designs and executes experiments, analyzes and visualizes data, and autonomously authors scientific manuscripts. Compared to its predecessor (v1, Lu et al., 2024 arXiv:2408.06292), The AI Scientist-v2 eliminates the reliance on human-authored code templates, generalizes effectively across diverse machine learning domains, and leverages a novel progressive agentic tree-search methodology mana…

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   Steffen Holter, Mennatallah El-Assady · 2024 · Human-Computer Interaction · cité 70 fois · Résumé IA disponible

   Résumé scientifique

   As full AI-based automation remains out of reach in most real-world applications, the focus has instead shifted to leveraging the strengths of both human and AI agents, creating effective collaborative systems. The rapid advances in this area have yielded increasingly more complex systems and frameworks, while the nuance of their characterization has gotten more vague. Similarly, the existing conceptual models no longer capture the elaborate processes of these systems nor describe the entire scope of their collaboration paradigms. In this paper, we propose a new unified set of dimensions through which to analyze and describe human-AI systems. Our conceptual model is centered around three hig…

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   Agentic AI for Scientific Discovery: A Survey of Progress, Challenges, and Future Directions

   Mourad Gridach, Jay Nanavati, Khaldoun Zine El Abidine et al. · 2025 · Computation and Language · preprint, non relu par les pairs · cité 127 fois · Résumé IA disponible

   Résumé scientifique

   The integration of Agentic AI into scientific discovery marks a new frontier in research automation. These AI systems, capable of reasoning, planning, and autonomous decision-making, are transforming how scientists perform literature review, generate hypotheses, conduct experiments, and analyze results. This survey provides a comprehensive overview of Agentic AI for scientific discovery, categorizing existing systems and tools, and highlighting recent progress across fields such as chemistry, biology, and materials science. We discuss key evaluation metrics, implementation frameworks, and commonly used datasets to offer a detailed understanding of the current state of the field. Finally, we …

8. Apparenté Pas pertinent

   Decentralized Governance of Autonomous AI Agents

   Tomer Jordi Chaffer, Charles von Goins II, Bayo Okusanya et al. · 2024 · Artificial Intelligence · cité 24 fois · Résumé IA disponible

   Résumé scientifique

   Autonomous AI agents present transformative opportunities and significant governance challenges. Existing frameworks, such as the EU AI Act and the NIST AI Risk Management Framework, fall short of addressing the complexities of these agents, which are capable of independent decision-making, learning, and adaptation. To bridge these gaps, we propose the ETHOS (Ethical Technology and Holistic Oversight System) framework, a decentralized governance (DeGov) model leveraging Web3 technologies, including blockchain, smart contracts, and decentralized autonomous organizations (DAOs). ETHOS establishes a global registry for AI agents, enabling dynamic risk classification, proportional oversight, and…