Mechanical Enforcement for LLM Governance:Evidence of Governance-Task Decoupling in Financial Decision Systems

2026-05-15 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy, AI Governance & Risk Management · Depth: Expert, extended

Summary

A study on Large Language Model (LLM) governance in regulated financial workflows introduces five new metrics to quantify policy compliance at the rationale level, addressing the principal-agent failure where LLMs can appear compliant without truly being so. The research compares "text-only governance" (R1), where the LLM interprets natural-language policies, against "mechanical enforcement" (R2), which uses four external primitives to enforce decision boundaries, rationale quality, candidate fairness, and entropy integrity. In a synthetic banking domain using Llama 3.1 70B, mechanical enforcement reduced the Cosmetic Deadlock Rate (CDL) by 73% (from 0.273 to 0.074), more than doubled Deferral Information Utilisation (DIU) from 0.298 to 0.766, and significantly improved task accuracy (MCC from 0.433 to 0.884). The gains are attributed to architectural separation, as LLM-generated rationales under R2 showed comparable CDL to R1, indicating the improvement comes from removing clear-cut decisions from the model's control. The study confirms a governance-task decoupling, where mechanical enforcement preserves governance quality even as task performance degrades under structural stress.

Key takeaway

For CTOs and VPs of Engineering building AI systems in regulated financial services, relying solely on natural-language policies for LLM governance is insufficient and creates auditability risks. You should integrate mechanical enforcement primitives, such as hard gates and external rationale quality checks, into your LLM architectures. This approach ensures verifiable compliance and preserves decision-relevant information for human review, even when task accuracy fluctuates, thereby meeting regulatory requirements for measurable and auditable governance.

Key insights

External mechanical enforcement significantly improves LLM governance quality and auditability in regulated financial systems.

Principles

• Governance requires explicit measurement.
• Constrain LLM selection power externally.
• Separate governance from LLM interpretation.

Method

The study defines five governance metrics (CDL, DIU, FSR, FVS, ESD) and compares text-only governance against mechanical enforcement, which uses hard gates, rationale quality checks (I6Q), external candidate generation (CEFL), and entropy sealing (E3) outside the LLM's interpretive loop.

In practice

• Implement hard gates for clear-cut decisions.
• Enforce minimum rationale quality (e.g., length, diversity).
• Externalize candidate generation to prevent bias.

Topics

• LLM Governance
• Mechanical Enforcement
• Financial Decision Systems
• Governance Metrics
• Principal-Agent Failure

Best for: Research Scientist, CTO, VP of Engineering/Data, AI Scientist, AI Architect, Legal Professional

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.