HALO: Hallucination-Aware Layered Oversight for Trustworthy Enterprise AI
A new preprint argues that achieving zero hallucination in large language models (LLMs) cannot be accomplished by the model alone, but must be enforced at the system level. The authors introduce HALO, a six-layer assurance architecture that includes grounded generation, constrained execution, multi-signal verification, calibrated abstention, total traceability, and continuous oversight. The framework is demonstrated on a regulated claims-extraction task.
Why it matters: This work reframes hallucination as a system-level failure mode and proposes a practical, multi-layered architecture for deploying trustworthy AI in regulated enterprise environments.
Full story at: arXiv Computation and Language ↗