arXiv AI

Referential Regimes: Transformation-Invariant Identity for Neutral Substrates

arXiv:2601. 16152v2 Announce Type: replace-cross Abstract: Data systems increasingly operate under persistent legal, political, and analytic disagreement, where no single interpretive authority can be assumed.

arXiv AI
2d ago

Multi-Jurisdictional Legal Identity Assurance for Capability Gating: A Design-Science Proposal for Tiered, Reusable Identity Assurance of Natural, Juridical, and Machine Entities

The paper proposes a tiered, reusable identity assurance model that separates assurance state from capability gates, allowing participants to disclose only what is necessary for each act. It introduces a typed entity taxonomy, a two‑axis coordinate system for assertion scope and source, and a time‑indexed jurisdiction attribute, with reliance recorded in bitemporal snapshots. The design is evaluated against existing flat‑verification and per‑credential models, addressing cross‑border reuse and data‑erasure versus evidentiary retention concerns.

By Walter Kurz
arXiv AI
2d ago

Actions with Receipts: Jointly Binding Claims, Evidence, and Execution for Replayable Tool-Agent Auditing

The paper introduces a claim‑anchored execution contract that binds a tool‑using agent’s emitted claim to its exact source span, the ordered execution prefix that produced it, and the source version and access state observed. Each receipt contains deterministic anchors, source identifiers, offsets, hashes, quotes, and a domain‑separated execution commitment, allowing a verifier to reconstruct these bindings before semantic or task labels are joined. The contract defines seven independently testable properties and demonstrates high detection rates against cross‑object attacks, with strong performance on conflict‑aware support guard evaluations.

By Miaobo Hu, Shuhao Hu, Xiaobo Guo, Xin Wang, Bokun Wang, Yina Sa, Daren Zha, Jun Xiao
arXiv AI
Aug 28

Persona-Execution Separation: An Architecture Pattern for Evolving LLM Agents under Execution Audit

The paper introduces Persona‑Execution Separation (PES), an architecture pattern that splits an LLM agent’s persona—its instructions, tone, and self‑presentation—from its execution—stateful, auditable work—by placing them in distinct trust domains linked through a governed contract bridge. PES allows the persona to evolve freely while keeping execution traceable, using mechanisms such as an approval matrix, data‑loss‑prevention exceptions, and continuous identity. A pilot implementation on a regulated digital‑employee platform demonstrated that PES successfully decouples persona drift from execution audit, preventing re‑validation or fingerprinting of hard‑asserted fields.

By Yisen Xi
arXiv AI
Aug 20

One Gate Is Not Enough: Composing Stateful Pre-Action Controls for Agentic AI

The paper investigates how multiple pre‑action controls—authority, resource, and evidence gates—interact in agentic AI systems. It formalizes remediation‑induced control coupling, showing that remediation can invalidate earlier judgments and that the order of remediation matters. The authors propose a remediate‑and‑regate protocol to restore soundness, analyze non‑commuting remediation operators, and demonstrate the approach on a deterministic open‑data artifact with three published engines.

By Gaston Besanson
arXiv AI
Sep 10

Causal Attribution for Agentic Decisions: Estimators, Coupling, and a Traceability Specification

The paper presents a framework for causal attribution in agentic AI systems, outlining estimators and conditions where they fail. It distinguishes between marginal total effects and common‑random‑number total effects, introduces a natural direct effect under pinned downstreams, and derives a coupling method to keep direct effects estimable. The authors also propose a traceability specification to meet upcoming regulatory requirements for high‑risk AI systems.

By Ajay Pravin Mahale (Hochschule Trier)