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Synapse
April 16, 20260 citationsOpen Access

The Forensic Standard: Deterministic Verification in Legal AI

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ATAnteneh Tessema

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.

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Cite This Study

Anteneh Tessema (2026) studied this question.

synapsesocial.com/papers/69e07cfa2f7e8953b7cbe05chttps://doi.org/10.5281/zenodo.19582431
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1CAN LARGE LANGUAGE MODELS ACT AS “CO-AUDITORS”?2026
  2. 2Method for Evaluating the Effectiveness of Large Language Models for Law Enforcement Purposes: Towards AI Standardization in Public Administration2026
  3. 3Deterministic Structural Layers for Stabilizing Probabilistic AI System – A Hybrid Semantic-Operational Control Architecture for Drift‑Resistant, Auditable and Reproducible AI Workflows2026
  4. 4Deterministic Boolean Verification Catches What LLMs Miss: A Hallucination Benchmark2026
  5. 5Hallucination Detection, Categorization, and Mitigation in Large Language Models: A Cross-Domain Evaluation Framework2026