arXiv:2605.28807v1 Announce Type: new Abstract: Agentic AI systems capable of autonomous planning and extended environmental interaction pose a fundamental control problem: how can humans maintain meaningful oversight of systems that may exceed their own capabilities? Existing approaches to scalable oversight rely on complex assumptions, remain largely heuristic, or lack practical methods for sequential settings with statistical guarantees. We introduce Calibrated Collective Oversight (CCO), which aggregates diverse auxiliary scoring functions into a penalty measuring deviation from a conserva
The proliferation of advanced AI systems with agentic capabilities necessitates robust oversight mechanisms to ensure human control and alignment, making research in this area critically timely.
As AI systems become more autonomous and capable of extended environmental interaction, humans require new methods for maintaining meaningful oversight, especially given potential capability disparities.
This research introduces a novel method, Calibrated Collective Oversight (CCO), for aggregating diverse scoring functions to measure deviations from conservative behavior in AI agents, offering a path to more reliable control.
- · AI safety researchers
- · Developers of autonomous AI systems
- · Industries deploying agentic AI
- · Organizations with inadequate AI governance strategies
- · AI systems lacking robust oversight mechanisms
Improved safety and reliability of general-purpose AI agents.
Accelerated deployment and adoption of agentic AI in sensitive domains due to enhanced trust.
New regulatory frameworks and industry standards built around verifiable, auditable AI oversight mechanisms.
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Read at arXiv cs.AI