Search: “governance”

arXiv:2610.02664v1 Announce Type: new Abstract: Long-horizon agents are now playing an increasingly significant role in assisting humans with complex problem-solving. However, it is exactly their exte

arXiv:2610.00008v1 Announce Type: cross Abstract: Sim-to-real research pursues physics fidelity as a primary objective: simulators are judged by how closely they reproduce real-world contact dynamics.

arXiv:2609.28693v1 Announce Type: new Abstract: Large Language Model (LLM) agents struggle to scale safely when exposed to vast enterprise toolsets. Providing an agent with access to every internal to

arXiv:2609.28479v1 Announce Type: cross Abstract: Conversational AI increasingly mediates intimate-partner communication, and refusal logic at the inference layer now functions as a governance thresho

arXiv:2609.22869v1 Announce Type: cross Abstract: Technology cannot steer itself. Strategy provides that steering, establishing the rule that purpose and judgment must precede compute capital. As the

arXiv:2609.16232v1 Announce Type: new Abstract: Geospatial artificial intelligence (GeoAI) powered by large language models (LLMs) is expanding the capacity to query, generate, and interpret spatial i

arXiv:2609.12932v1 Announce Type: new Abstract: Proposed governance framework for autonomous robotic systems, introducing a three-layer compliance architecture (ARC) instantiated through model safety

arXiv:2609.05529v1 Announce Type: cross Abstract: Large language model (LLM)-based multi-agent systems (MAS) predominantly rely on centralized orchestration and lack formal verification mechanisms for

arXiv:2609.03553v1 Announce Type: new Abstract: Policy analysis requires more than predicting whether a proposal will pass: it requires identifying who will be affected, how those actors respond, and

arXiv:2609.00267v1 Announce Type: cross Abstract: Autonomous LLM agents increasingly act on a user's behalf: they hold credentials, call tools and services, and spawn sub-agents that act further on th

arXiv:2609.01275v1 Announce Type: cross Abstract: Frontier AI systems function as constitutional institutions: each deployed model encodes an implicit ranking among safety, helpfulness, honesty, auton

arXiv:2608.19838v1 Announce Type: new Abstract: Spec Driven Development SDD has consolidated the idea that the specification rather than the code should be the primary artefact governing AI assisted w

arXiv:2608.18324v1 Announce Type: new Abstract: Machine-verifiable workflows produce governance records linking a task contract, model attempt, verifier decision, accepted output, and target origin. W

arXiv:2608.16055v1 Announce Type: new Abstract: Existing agent benchmarks ask whether the agent finished the task. We ask whether it finished it within policy. We introduce Fiducia-bench, a benchmark

arXiv:2608.12166v1 Announce Type: cross Abstract: Algorithm registers have been championed as a means of providing transparency on the use of algorithms in public services. Yet potential publics diffe

arXiv:2607.25648v1 Announce Type: cross Abstract: Public services face growing pressure to adopt artificial intelligence (AI) to close the gap between rising demand and falling resources. That pressur

arXiv:2607.10467v2 Announce Type: replace-cross Abstract: Healthcare organizations often cannot freely centralize patient data because medical records are sensitive, regulated, and institutionally con

arXiv:2603.04390v2 Announce Type: replace Abstract: WebGIS development requires consistency, yet agentic AI often fails due to LLM context constraints, forgetting, stochasticity, instruction failure,

arXiv:2607.00334v1 Announce Type: new Abstract: Autonomous agents, whether LLM-driven software agents or robotic physical agents, face a common class of failure modes when operating without continuous

arXiv:2606.31339v1 Announce Type: new Abstract: Agentic artificial intelligence is increasingly used to decompose industrial tasks, propose robot actions, and adapt execution plans in dynamic cyber-ph

arXiv:2606.26099v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in artificial intelligence (AI) governance analysis across national and international organisat

arXiv:2606.26866v1 Announce Type: cross Abstract: Third-party vendors, such as analytics platforms, cloud services, identity providers, and software suppliers, are increasingly embedded in digital ser

arXiv:2606.22142v1 Announce Type: new Abstract: We present RoboLineage, an agent-native data lifecycle governance system for robot policy iteration. Modern robot policies improve through repeated data

arXiv:2606.05710v1 Announce Type: cross Abstract: The increasing penetrations of the critical infrastructure sector in the United States with intelligent digital technologies have greatly increased ex

arXiv:2606.04104v1 Announce Type: cross Abstract: Agent systems execute through runtimes with very different control points: local coding tools, framework SDKs, managed agent platforms, API gateways,

arXiv:2606.03518v1 Announce Type: new Abstract: As AI systems evolve from passive models into autonomous active agents capable of initiating actions, collaborating, and delegating tasks, the tradition

arXiv:2605.24737v1 Announce Type: cross Abstract: Current approaches to AI compliance treat conformity as a binary, audit-time verdict rather than a continuous, measurable property of production syste

arXiv:2605.20210v1 Announce Type: cross Abstract: Agentic AI systems - systems that can pursue goals through multi-step planning and tool-mediated action with limited direct supervision - are moving f

arXiv:2605.16298v1 Announce Type: cross Abstract: While traditional AI and data-driven facilities management approaches have improved building operational efficiency, they remain constrained by centra

arXiv:2605.14802v1 Announce Type: new Abstract: Large language models often suffer from fact loss, timeline confusion, persona drift, and reduced stability during long-range interaction, especially un

arXiv:2605.15164v1 Announce Type: cross Abstract: This position paper argues that behavioural assurance, even when carefully designed, is being asked to carry safety claims it cannot verify. AI govern

arXiv:2605.13362v1 Announce Type: cross Abstract: Computational social choice and algorithmic decision theory offer rich aggregation theory but no end-to-end, polynomial-time process for egalitarian s

arXiv:2605.12364v1 Announce Type: cross Abstract: Agentic AI governance is a critical component of agentic AI infrastructure ensuring that agents follow their owner's communication and interaction pol

arXiv:2605.04076v1 Announce Type: new Abstract: U.S. financial institutions deploying AI-based fraud detection face a fragmented compliance landscape spanning four regulatory frameworks -- OCC Bulleti

arXiv:2604.26997v1 Announce Type: cross Abstract: Autonomous AI agent ecosystems require stronger mechanisms for secure discovery, identity verification, capability attestation, and policy governance.

arXiv:2604.11839v1 Announce Type: cross Abstract: Autonomous AI agents built on open-source runtimes such as OpenClaw expose every available tool to every session by default, regardless of the task. A

arXiv:2604.09620v1 Announce Type: cross Abstract: Large language models are increasingly used to support organizational decisions from hiring to governance, raising fairness concerns in AI-assisted ev

arXiv:2609.22476v1 Announce Type: cross Abstract: Transmission system operators face rising complexity from renewable integration, reduced inertia, and tighter security margins. Large language models

arXiv:2609.20016v1 Announce Type: cross Abstract: The EU AI Act (Regulation 2024/1689) imposes technical obligations on high-risk AI providers, yet Articles 8-15 were drafted for predictive AI and lea

arXiv:2608.15193v1 Announce Type: cross Abstract: As large language model (LLM) agents are increasingly adopted in scientific research, external knowledge bases, knowledge graphs, and long-term memory

arXiv:2608.06246v1 Announce Type: new Abstract: Post-training adaptation has become central to modern machine learning practice and includes techniques such as retraining, fine-tuning, parameter-effic

arXiv:2607.05163v1 Announce Type: cross Abstract: AI systems may produce failures after deployment that pre-deployment safety assessments do not anticipate. Managing these failures requires what we re

arXiv:2607.01248v1 Announce Type: cross Abstract: Large language models are increasingly used for knowledge acquisition, code generation, academic writing, and agent-based automation. In these setting

arXiv:2606.30652v1 Announce Type: cross Abstract: Transparency is increasingly mandated for public-sector AI systems, with organisations required to publish statements describing their AI use and over

arXiv:2606.30306v1 Announce Type: cross Abstract: Always-on agents are systems whose future behavior depends on durable state accumulated across earlier interactions. We treat them as persistent-state

arXiv:2606.08172v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly mediate high-stakes interactions in finance, medicine, and mental-health support, yet users have limited con