Demonstrates a framework for factual verification in legal AI, implying enhanced accuracy and accountability.
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.
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Anteneh Tessema (2026) studied this question.
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