Zero Hallucination, by Construction: Hallucination-Aware Layered Oversight for Trustworthy Enterprise AI

The Shift from Model to System

Enterprise adoption of AI is frequently stalled by the problem of hallucination—fluent but false model outputs. The authors argue that the industry's current focus on building "hallucination-free" models is misguided because large language models are inherently probabilistic and prone to unsupported generation. Instead, they propose reframing the goal: "zero hallucination" should be treated as a property enforced by a robust system architecture rather than an intrinsic quality of the model itself.

The HALO Architecture

HALO (Hallucination-Aware Layered Oversight) is designed as a defense-in-depth system that contains failure modes through six distinct layers:

1. Grounded Generation: Limits the model to answering based on retrieved, approved content.
2. Constrained Execution: Uses deterministic state machines to bound the model's actions and prevent autonomous, unmonitored loops.
3. Multi-Signal Verification: Employs both LLM-based judges and deterministic, evidence-based checks to score output groundedness.
4. Calibrated Abstention: Forces the system to decline answering or escalate to a human when confidence is low, rather than guessing.
5. Total Traceability: Captures every retrieval, tool call, and generation in an audit log for full reproducibility.
6. Continuous Oversight: Monitors performance over time, detects drift, and triggers automated correction cycles.

Evidence-Based Confidence

A core innovation of HALO is its rejection of the model's self-reported confidence, which the authors note is often poorly calibrated and unreliable. Instead, the system uses evidence-based checks: it attempts to locate extracted values directly within the source text using exact or fuzzy matching and validates them against structural constraints (e.g., arithmetic consistency). This ensures that the system's confidence score is anchored in objective reality rather than the model's internal, potentially biased, assessment.