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

Ranked by how close their title and abstract are to this paper’s. Select some to read them together.

Closest papers

1. Close Broader context Not relevant

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

   Daniel Beránek · 2026 · Figshare preprint · AI summary available

   The Crisis surveys entity resolution across heterogeneous data and AI systems, framing identity reconstruction challenges that this paper addresses at the mutation-authorization boundary.

   In short

   The preprint synthesizes entity-resolution risks in heterogeneous AI systems and proposes DIO/ODI as a testable, not yet validated, framework for digital identity integrity.

2. Close Groundwork for this paper Not relevant

   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 defines reconstruction-failure categories that this paper maps into domain-configured identity-preserving constraints.

   In short

   The note organizes reconstruction failure modes into four diagnostic domains and distinguishes realized failures from susceptibility to destabilization.

3. Close Broader context Not relevant

   Authorization Propagation in Multi-Agent AI Systems: Identity Governance as Infrastructure

   Krti Tallam · 2026 · arXiv.org · preprint, not peer reviewed · cited 11 times · Not summarized yet

   Authorization Propagation studies how authorization invariants persist across multi-agent workflows, while this paper governs evidence-based identity mutations at commitment.

   Abstract

   The security discussion around agentic AI focuses heavily on prompt injection. This paper argues that multi-agent systems also create a distinct authorization problem: maintaining authorization invariants as non-human principals retrieve data, delegate tasks, and synthesize results across changing boundaries. We call this problem authorization propagation. It is not reducible to prompt injection and is not fully addressed by classical access-control models such as RBAC, ABAC, or ReBAC. The paper formalizes authorization propagation as a workflow-level property, identifies three sub-problems (transitive delegation, aggregation inference, and temporal validity), and derives seven structural re…

4. Close Broader context Not relevant

   Decision Evidence Maturity Model for Agentic AI: A Property-Level Method Specification

   Oleg Solozobov · 2026 · arXiv.org · preprint, not peer reviewed · cited 2 times · Not summarized yet

   The Decision Evidence Maturity Model evaluates property-level evidence sufficiency for agentic decisions, whereas this paper requires claim-specific evidence to authorize identity changes.

   Abstract

   Agentic AI systems produce decision evidence at scale through execution telemetry, but property-level reconstruction often fails when an external party asks a specific governance question about a specific decision: the assembled evidence is insufficient to answer it. We name this pattern the container fallacy: the automatic equation of evidence-container presence with audit sufficiency. This paper specifies the Decision Evidence Maturity Model (DEMM), a property-level reconstructability method for agentic decisions. DEMM classifies evidence sufficiency into four executable categories plus a protocol-level"conflicting"category and aggregates per-property verdicts into a five-level capability …

5. Close Broader context Not relevant

   Overlaying Governance: A Compositional Authorization Framework for Delegation and Scope in Agentic AI

   Amjad Ibrahim, Yong Li · 2026 · arXiv.org · preprint, not peer reviewed · cited 4 times · Not summarized yet

   Overlaying Governance addresses delegation and scope in agentic authorization, while this paper binds mutation authority to evidence, operation scope, and current policy state.

   Abstract

   As AI systems evolve from passive models into autonomous active agents capable of initiating actions, collaborating, and delegating tasks, the traditional boundaries of software systems blur. Traditional authorization and delegation frameworks, built around fixed principals, explicit requests, and static scopes, are insufficient to govern agentic systems. Agentic AI demands richer authorization semantics: agents must inherit and delegate permissions, act under time-limited authority, and coordinate through shared protocols. Existing Identity and Access Management (IAM) systems fail to fully capture this notion of agency, lacking mechanisms for recursive delegation, contextual boundaries, and…

6. Close Groundwork for this paper Not relevant

   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 · AI summary available

   Digital Identity Optimization develops the identity framework this paper adapts through DIO/ODI-informed reconstruction constraints for entity-resolution mutations.

   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.

7. Close Broader context Not relevant

   Compliance-as-Code for AI-Driven Identity Systems: Clause-to-Control Traceability and Machine-Readable Evidence

   L. Nweke, P. Yeng · 2026 · IEEE Access · cited 1 time · Not summarized yet

   Compliance-as-Code makes regulatory controls and evidence traceable in AI identity workflows, while this paper specifies evidence conditions for committing identity changes.

   Abstract

   Artificial intelligence (AI)-driven identity (ID) systems (document onboarding, wallet-based credential presentation, fraud/risk scoring) increasingly face regulatory obligations requiring demonstrable accountability, traceability, and technical documentation. Yet current compliance practice remains predominantly document-centric and manual, making it difficult to keep evidence synchronized with rapidly evolving systems and models. We present a compliance-as-code approach for AI-driven ID workflows that: 1) maintains clause-to-control traceability under policy/configuration evolution; 2) treats evidence completeness as an enforceable property via fixed monitoring-window semantics (silence is…

8. Close Another approach to the problem Cited by this paper Not relevant

   Runtime Authorization of Self-Generated Subgoals in Long-Horizon Tool-Using AI Agents

   Gen-Liang Zhu, Chu Wang · 2026 · cited 1 time · Not summarized yet

   Runtime Authorization validates version-bound goal-graph mutations against an authorization root; this paper authorizes entity-resolution mutations using claim-specific evidence and policy constraints.

   Abstract

   Long-horizon tool-using AI agents create subgoals, replan, delegate work, and compose sibling results. Per-tool permission checks cannot establish that a changing goal graph remains within the principal-approved task. We address this authorization gap in a finite structured domain with one principal and one authorization root. Each proposed goal-graph mutation carries a version-bound witness that its continuation traces, resources, obligations, invariants, and closing condition refine the active root contract; every protected effect is rechecked at an atomic commit boundary. Free-form goal text supplies no authority. We prove trace-policy and modeled forbidden-state preservation under explic…

Prior work it builds on

1. Related Another approach to the problem Not relevant

   From Intent to Execution Grant: An Execution-Boundary Conformance Profile for High-Risk AI Actions

   Meng-Rong Wu, Lin Wang, Yong Zhang et al. · 2026 · cited 3 times · Not summarized yet

   EBL-Core grants execution authority to a fully specified AI action at its boundary, while this paper gates persistent identity changes on evidence and graph-state conditions.

   Abstract

   AI agents increasingly propose actions with external consequences, including financial transfers, infrastructure changes, software deployments, disclosures, and physical actuation. Authorization engines, policy languages, runtime monitors, provenance mechanisms, and agent guardrails provide important foundations, but do not necessarily define a common semantic contract for the final transition from a particular candidate action to execution authority. We specify EBL-Core, an execution-boundary conformance profile for deciding whether one canonical, fully materialized AI-generated candidate may receive action-scoped execution authority under explicit conditions. It binds a structured intent o…

2. Related Another approach to the problem Not relevant

   Temporary Authority, Permanent Effects: Commit-Time Authorization for LLM Agents

   Igor Santos-Grueiro · 2026 · arXiv.org · preprint, not peer reviewed · cited 26 times · Not summarized yet

   Temporary Authority requires current justification for an exact effect at commit, while this paper checks claim-specific evidence, conflicts, scope, and versions before identity changes.

   Abstract

   An LLM agent often acts on an approval it observed earlier: it reads the approval, prepares the request, and commits later. If the approval was withdrawn, or covers a different effect, the service can apply the change and report success. Seventeen fixed advisories from three deployed agent products document failures: an approval did not cover the executed effect, with no intervening change (12), or survived a change in the facts it depended on (5). We propose commit-time authorization (CTA), requiring at least one complete, current justification for the exact effect at commit: a live grant, current premises, ready dependencies, an exact-effect binding, and an eligible path. CommitGate select…

3. Related Broader context Not relevant

   CONTINUITY: Security-Context Contracts for Composable LLM Agent Controls

   Chris Zheng, Yang Geng · 2026 · cited 7 times · Not summarized yet

   CONTINUITY preserves security context across agent-control components, while this paper specifies a commit contract for evidence-authorized identity mutations.

   Abstract

   LLM agent systems increasingly combine provenance tracking, authorization, policy enforcement, protocol adapters, and execution controls. However, individually correct security mechanisms do not necessarily compose into an end-to-end secure system: security-critical context may be dropped, widened, rebound, or reinterpreted as actions cross component boundaries. We identify this failure mode as security-context discontinuity and introduce CONTINUITY, a framework for verifiable composition of agent security controls. CONTINUITY models each component with an assume-guarantee contract and carries authenticated security context across transitions using signed root grants, provenance commitments,…

4. Related Same question Not relevant

   AgenticER: the next frontier in Entity Resolution

   George Papadakis, Panos Korovesis, Manolis Koubarakis et al. · 2026 · arXiv.org · preprint, not peer reviewed · cited 1 time · Not summarized yet

   AgenticER studies iterative, uncertain entity resolution with LLM agents, the setting in which this paper restricts which evidence can authorize persistent identity changes.

   Abstract

   Entity Resolution (ER) is a fundamental problem in data management, playing a critical role in tasks like data cleaning and knowledge graph construction. The existing ER approaches range from traditional rule-based to deep learning techniques and LLM-based methods, but typically operate under a ``passive paradigm'', as duplicates are detected through static, one-shot similarity computations. Such approaches fail to capture the inherently uncertain and context-dependent nature of real-world ER tasks, especially in data lakes with streaming content in heterogeneous formats such as CSV files, JSON files, RDF dumps, and free text. In such settings, resolving ambiguity often requires iterative ev…

5. Related Broader context Not relevant

   From Business Events to Auditable Decisions: Ontology-Governed Graph Simulation for Enterprise AI

   Hongyin Zhu, Jinming Liang, Mengjun Hou et al. · 2026 · arXiv.org · preprint, not peer reviewed · cited 1 time · Not summarized yet

   LOM-action uses ontology-governed graph simulation for auditable enterprise decisions, while this paper uses domain constraints to govern entity-resolution graph mutations.

   Abstract

   Existing LLM-based agent systems share a common architectural failure: they answer from the unrestricted knowledge space without first simulating how active business scenarios reshape that space for the event at hand -- producing decisions that are fluent but ungrounded and carrying no audit trail. We present LOM-action, which equips enterprise AI with \emph{event-driven ontology simulation}: business events trigger scenario conditions encoded in the enterprise ontology~(EO), which drive deterministic graph mutations in an isolated sandbox, evolving a working copy of the subgraph into the scenario-valid simulation graph $G_{\text{sim}}$; all decisions are derived exclusively from this evolve…