TRACE: A Multi-Layer Benchmark for Human AI Controller Coordination Under Drift and Failure
Modern cyber-physical and AI-assisted systems couple human operators, AI decision modules, and automated controllers in a single control loop, so trustworthiness depends on the whole loop, not any one model. Yet no standard benchmark captures time-aligned, multi-layer traces of how drift and failures propagate across these layers, so we cannot diagnose where coordination breaks down, why, or how to recover. This paper targets one facet of that gap: drift, a deviation that can originate in any st
Record details
Published: 7 August 2026
Source: arXiv
Category: Research
Topics: Healthcare · Military & security
Retrieved: 10 August 2026
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ethics.ai (7 August 2026), “TRACE: A Multi-Layer Benchmark for Human AI Controller Coordination Under Drift and Failure,” evidence record 17830, https://ethics.ai/record/17830 (originally published by arXiv).
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