Making LLMs Reliable When It Matters Most: A Five-Layer Architecture for High-Stakes Decisions
Published 10 Nov 2025 · arXiv · Alejandro R. Jadad
Overview
The paper introduces a five-layer architecture designed to enhance the reliability of large language models (LLMs) in high-stakes decision-making contexts. It addresses the challenge of cognitive biases that affect both humans and AI, proposing a structured approach to maintain effective human-AI partnerships.
Key Insights
- Five-Layer Architecture: A structured framework is proposed to maintain reliability in LLMs, addressing cognitive biases and ensuring defensible decisions.
- Calibration Process: A seven-stage calibration sequence is necessary to sustain partnership states, preventing performance degradation and costly errors.
- Cross-Model Validation: Systematic differences in performance across LLM architectures were observed, highlighting the need for tailored approaches.
BFSI Relevance
- Why Relevant: Reliable AI systems are crucial for high-stakes decisions in BFSI sectors, where strategic decisions impact valuations and investments.
- Primary Sector: Financial Services
- Subsectors: Asset Management, Corporate Banking
- Actionable Implications:
- Implement structured AI frameworks to enhance decision reliability.
- Monitor AI performance to prevent cognitive biases and errors.
- Tailor AI systems to specific decision-making contexts.
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