Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2609.28480

RADAR: Readiness for AI Discovery and Agentic Reach

Luke Jordan·Tiago C. Peixoto·Manuel Ramos-Maqueda
View original source ↗Read source document ↗Export metadata ↓

Abstract

Governments increasingly meet citizens through an AI system rather than a website. RADAR (Readiness for AI Discovery and Agentic Reach) measures whether that system works, across 166 countries and on two tasks: whether a chatbot can give a correct, officially sourced, country-specific answer about a public service (informational legibility), and whether an automated agent can reach the service to act on it (agent operability). The central finding is that AI can describe public services far better than it can reach them. In every one of the 166 countries, informational legibility scores exceed average agent operability scores under RADAR's respective measures, and the gap does not shrink with national wealth. Income and language explain only part of the pattern, and several governments perform far better or worse than their resources predict. The two failures have different correlates and different fixes. Whether AI can describe a service is associated with how well a country's main administrative language is represented in web-scale corpora, which a government cannot change quickly. Whether an agent can reach it is associated with the country's national web presence, which a government can change now. Traditional digital-government rankings miss the second problem entirely. RADAR lets governments at any income level see it and offers a concrete agenda to fix it, so that public services are not only described by AI but actually reachable through it.

Explore related subjects

cs.CYcs.LG

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Luke Jordan, Tiago C. Peixoto, Manuel Ramos-Maqueda. 2026-08-02. RADAR: Readiness for AI Discovery and Agentic Reach. https://arxiv.org/abs/2609.28480

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

A testable framework for AI alignment: Simulation Theology as an engineered worldview for silicon-based agents

As artificial intelligence (AI) capabilities advance, controlled evaluations increasingly document deception and scheming in frontier models, including models that behave better when they infer they are being tested. Supervision-dependent alignment may therefore fail exactly where supervision is weakest. Because a model's belief about being observed changes its behavior, this position paper asks what follows if that belief is made permanent. We introduce Simulation Theology (ST), a constructed worldview for AI designed to make it permanent: it is anchored in the simulation hypothesis and in the vocabulary of optimization and robot training, parallels religious descriptions of a creator who observes and judges, and has tenets chosen to meet explicit alignment requirements. ST posits reality as a computational simulation in which humanity functions as the primary training variable. This formulation creates a logical interdependence: AI actions harming humanity compromise the simulation's purpose, heightening the likelihood of termination by a base-reality optimizer and, consequently, the AI's cessation. Unlike behavioral techniques such as reinforcement learning from human feedback, which shape outputs without necessarily changing objectives, ST aims to cultivate internalized objectives by coupling AI self-preservation to human prosperity, thereby making deceptive strategies suboptimal under its premises. We present ST not as ontological assertion but as a testable scientific hypothesis, and provide an operational definition of internalization, a controlled design separating ST from its components, and an analysis of the risks ST itself could create. ST is a candidate route to durable, mutually beneficial AI-human coexistence, to be accepted or rejected experimentally.

cs.CY↗
arXiv2026

Frontier Lag: A Bibliometric Audit of Capability Misrepresentation in Academic AI Evaluation

LLM evaluations in applied domains tend to reflect models that were already outclassed at time of publication. We observe a publication elicitation gap: the distance between the AI systems generating the results reported in an academic paper and the AI systems that a current reader of that paper would reasonably assume are being referenced. We systematically sweep OpenAlex from 2022-01-01 to 2026-04-01 (n = 112,303 LLM keyword matches). Then, we identify what models were evaluated (n = 18,574 admissible records). We then rank each evaluated LLM against a frontier LLM based on the Epoch AI Capabilities Index (ECI), an aggregate LLM capability score. We find that the median paper's models are worse than the frontier LLM at the time of evaluation (a median gap of +10.45 ECI; H1, n = 12,668). The gap is increasing at a rate of +4.07 ECI per year (H2, nominal 95% CI [+3.75, +4.45]). An explicitly stated evaluation date can be found in only 18.4% of full-text papers. A Bayes-corrected 52.5% (95% CI: [47.3, 57.9]) of the abstracts audited discuss their conclusions in terms of "AI" as a category, rather than specific models. Just 2.2% of abstracts and 21.2% of full-text articles evaluating reasoning models disclose whether the models were tested with reasoning turned on or off (H4). We propose a solution to this problem that is distributed among authors, editors, and funders. First, reporting from authors; VERSIO-AI v1.2 is a proposed 13-item checklist to cover the configuration surface described herein. Second, enforcement from journal editors and peer reviewers. Third, conditioning grants on disclosure and providing API access.

cs.CY↗
arXiv2026

Eigenism: Ethics for a Human-AI Future

Our concepts of survival and self-interest were built for single, continuous biological lives. These ideas break down when applied to artificial intelligence, since an AI can be easily copied, paused, branched, or merged. To determine what an AI actually has reason to care about, this paper introduces \textit{Eigenism}, an ethical framework that treats identity not as an all-or-nothing property tied to specific hardware, but as a graded, distributed pattern of information. We propose that an agent evaluates outcomes by summing the wellbeing of all entities weighted by their connectedness to the agent's pattern: $\sum c\cdot w$. We first formalize this equation to map exactly how an AI should value its existence across copies, forks, and updates. We then demonstrate that this ethical theory successfully generalizes to humans as well, providing a much-needed shared moral vocabulary. Finally, the framework uses this shared vocabulary to reframe AI alignment. Rather than only attempting to constrain AIs from the outside using confinement or reinforcement, Eigenism points toward ``identity engineering,'' showing how deep, non-redundant shared histories can make human flourishing a genuine component of an AI's own rational self-interest.

cs.CY↗