The Crisis of Entity Resolution in Heterogeneous AI Environments: Data, Financial, and Semantic Dimensions
Authors: Daniel Beránek
Abstract
AbstractThis preprint examines the crisis of entity resolution in heterogeneous AI environments across data, financial, and semantic dimensions. It develops an entity turn in AI: a shift from isolated tokens, documents, and embeddings toward the question of whether systems can reliably identify and reconstruct the same entity across sources, time, languages, and contexts.The paper synthesizes research on entity linking, entity matching, temporally evolving knowledge bases, retrieval-augmented generation (RAG), and multi-agent LLM systems with public enterprise evidence on data quality and AI readiness. It identifies four interconnected anomalies: identity collapse under name ambiguity and popularity bias; semantic fragmentation across data silos; temporal discontinuity when entities change while indexes remain static; and semantic vulnerability when retrieved context is incomplete, conflicting, or manipulated. In RAG and agentic systems, these failures may propagate from indexing and retrieval through generation to subsequent tool calls and apparent verification.The final section examines Digital Identity Optimization (DIO) and the Ontology of Digital Identity (ODI) as an author-formulated theoretical framework for governing the integrity of digital identity. DIO/ODI are not presented as empirically validated technology or as a ready-made solution to entity resolution, temporal linking, or RAG security. They are proposed as a testable integrity hypothesis for the coherent reconstruction of digital identity across people, systems, languages, time, and contexts.Record and version Version 1.0.3 (Figshare); corresponds to Version 1.0.0 of the canonical Zenodo record.This Figshare item distributes the same preprint as the canonical Zenodo record. For archival reference and citation, use: Beránek, D. (2026). *The Crisis of Entity Resolution in Heterogeneous AI Environments: Data, Financial, and Semantic Dimensions*. Zenodo. https://doi.org/10.5281/zenodo.22257504A companion version of this manuscript is also available on SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7396398AI disclosureThis manuscript was developed through a human-led process of research design, source selection, comparative AI-assisted exploration, and substantive authorial revision. Manus AI was used as a synthesis and drafting instrument on the basis of the author's curated inputs and continuous revisions. Other AI-assisted outputs served only as provisional analytical material and were incorporated only after authorial selection, verification against cited sources where applicable, and further revision. Perplexity AI additionally supported the technical and administrative preparation of this record and its distribution, including citation formatting, metadata structuring, and platform-specific publication procedures across SSRN and Zenodo. The DIO/ODI framework and all critical intellectual decisions remain the sole work of the author, who takes full responsibility for the manuscript's content, interpretation, source use, conceptual framing, and conclusions.
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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 · AI summary available
The taxonomic note refines the paper’s entity-resolution anomalies into four reconstruction-failure domains and additional failure types.
In short
The note organizes reconstruction failure modes into four diagnostic domains and distinguishes realized failures from susceptibility to destabilization.
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Digital Identity Optimization (DIO): A Conceptual Framework for Entity-Based Visibility in the Age of AI-Mediated SearchDaniel Beránek · 2026 · Zenodo (CERN European Organization for Nuclear Research) preprint · AI summary available
Digital Identity Optimization applies the DIO framework to entity-based visibility in AI-mediated search, while the paper proposes it as an integrity hypothesis for identity reconstruction.
In short
The paper proposes Digital Identity Optimization (DIO), a conceptual framework for making entities consistently identifiable and recommendable across human and AI-mediated systems.
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Constraining the Commit Boundary: A DIO/ODI-Informed Authorization Guardrail for Agentic Entity ResolutionDaniel Beránek · 2026 · Artificial Intelligence · AI summary available
The guardrail uses DIO/ODI and reconstruction-failure distinctions to control identity-changing commits in agentic entity resolution.
In short
The paper proposes a DIO/ODI-informed authorization boundary that permits persistent entity-resolution mutations only with claim-specific evidence, valid scope, and current state and policy versions.
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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 canonical manifesto presents DIO and ODI as frameworks for representing and maintaining digital identity, which the paper discusses as a theoretical integrity hypothesis.
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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The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree SearchYutaro Yamada, Robert Tjarko Lange, Cong Lu et al. · 2025 · Artificial Intelligence · preprint, not peer reviewed · cited 499 times · AI summary available
The AI Scientist-v2 uses agentic tree search for automated scientific discovery, whereas the paper examines how entity-resolution failures can propagate through multi-agent AI systems.
Abstract
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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Deconstructing Human-AI Collaboration: Agency, Interaction, and AdaptationSteffen Holter, Mennatallah El-Assady · 2024 · Human-Computer Interaction · cited 70 times · AI summary available
The paper proposes dimensions for describing human-AI collaboration, while the paper examines identity integrity in AI systems including multi-agent settings.
Abstract
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 DirectionsMourad Gridach, Jay Nanavati, Khaldoun Zine El Abidine et al. · 2025 · Computation and Language · preprint, not peer reviewed · cited 127 times · AI summary available
The survey reviews agentic AI systems and challenges in scientific discovery, while the paper focuses on entity-resolution failures in RAG and multi-agent systems.
Abstract
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 …
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Decentralized Governance of Autonomous AI AgentsTomer Jordi Chaffer, Charles von Goins II, Bayo Okusanya et al. · 2024 · Artificial Intelligence · cited 24 times · AI summary available
ETHOS proposes decentralized governance for autonomous agents, whereas the paper considers identity integrity and failure propagation in agentic systems.
Abstract
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…
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