VQV Signal
The paper identifies an assurance-transition gap where current benchmarks and audits fail to specify how evidence should affect an AI agent's authority during critical tasks. It proposes Runtime Assurance Contracts (RACs), a formal policy schema that governs autonomy boundaries, evidence states, an...
The paper identifies an assurance-transition gap where current benchmarks and audits fail to specify how evidence should affect an AI agent's authority during critical tasks. It proposes Runtime Assurance Contracts (RACs), a formal policy schema that governs autonomy boundaries, evidence states, an...
AI-assisted summary based on the listed source.
Benchmarks, audits, and agent protocols describe performance, permissions, and repair, but not how observed evidence should change an agent's authority during a consequential task. We call this the assurance-transition gap. We propose a Runtime Assurance Contract (RAC), a policy-level formal schema binding...
As AI agents take on more consequential roles, ensuring their authority adapts appropriately to observed evidence is crucial for safety and reliability. RACs offer a structured approach to dynamically manage trust and oversight in high-risk AI deployments.
VQV organizes public signals from inspectable sources. It does not independently verify the underlying report.
Signal Strength reflects source quality, relevance, freshness and evidence. Public Interest helps organize discovery; it is not proof of truth.
VQV surfaced this signal because it is recent, relevant to AI Agents, connected to arXiv.
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