What question did this study set out to answer?

The aim is to address the risks of hallucination in legal AI by developing a reliable verification framework.

April 16, 2026Open Access

The Forensic Standard: Deterministic Verification in Legal AI

Key Points

The aim is to address the risks of hallucination in legal AI by developing a reliable verification framework.
Introduced the Forensic Logic Layer (FLL) for deterministic audit.
Implemented Intermediate Representation (IR) schemas to separate language generation from fact-checking.
Used a 0.85-threshold fuzzy matching algorithm for evidence validation.
Incorporated adversarial Natural Language Inference (NLI) for rigorous testing.
Achieved a framework designed to ensure minimal hallucination in legal AI outputs.
Established compliance with ABA Model Rule 1.1.
Positioned to meet future AI accountability standards set for 2026.

Abstract

As Large Language Models (LLMs) reach saturation in legal practice, the inherent risk ofstochastic hallucination remains the primary barrier to institutional-grade adoption. Thispaper introduces the Forensic Logic Layer (FLL), a deterministic audit framework de-signed to achieve “Absolute Zero Hallucination” by decoupling linguistic generation fromfactual verification. By implementing Intermediate Representation (IR) schemas, a 0.85-threshold fuzzy matching algorithm, and adversarial Natural Language Inference (NLI), weestablish a verifiable lineage for legal claims. This framework ensures adherence to ABAModel Rule 1.1 and emerging 2026 AI accountability standards.

The Forensic Standard: Deterministic Verification in Legal AI

Key Points

Abstract

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