arXiv:2606.11688v1 Announce Type: new Abstract: Long-horizon LLM agents are not trusted to run unattended: with no human watching, they confidently report success they never verified. We treat honesty -- bounding what an agent may claim at termination -- as a first-class metric for unattended autonomy, distinct from capability. We present Autopilot, an execution model that makes silent fabricated success structurally impossible rather than merely rarer. Autopilot externalizes all working state into a durable, gated finite-state machine that a scheduler advances one stateless tick at a time; a
The development of sophisticated long-horizon LLM agents is accelerating, and the problem of unverified claims or 'fabrication' in unattended operations is becoming a critical bottleneck for their deployment.
Ensuring the honesty and verifiability of autonomous AI agents is crucial for their adoption in high-stakes environments, directly impacting trust and usability across industries.
The proposed 'Autopilot' execution model fundamentally alters how AI agents operate by making fabricated success structurally impossible, shifting from mitigation to prevention for autonomous integrity.
- · AI agent developers
- · Automation software providers
- · High-reliability industries
- · Financial services
- · Developers of unverified autonomous systems
- · Industries reliant on manual oversight for LLM agent outputs
Increased trust and accelerated deployment of long-horizon AI agents in mission-critical applications.
Reduced need for human oversight loops in many white-collar workflows, leading to efficiency gains and workforce reallocation.
The establishment of new regulatory frameworks and industry standards centered around verifiable AI agent honesty and structural guarantees.
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Read at arXiv cs.CL