# Adaptive Trust Metrics for Multi-LLM Systems: Enhancing Reliability in Regulated Industries

arXiv: https://arxiv.org/abs/2601.08858 · HTML: https://mlsanity.com/paper/2601.08858/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 19 references that are in the map.
- First posted on arXiv 7 Jan 2026.

## Builds on (most cited first, 1 of 1)

- [Trust in AI: Progress, Challenges, and Future Directions](https://mlsanity.com/paper/2403.14680/) (2024)

## Abstract (arXiv)

Large Language Models (LLMs) are increasingly deployed in sensitive domains such as healthcare, finance, and law, yet their integration raises pressing concerns around trust, accountability, and reliability. This paper explores adaptive trust metrics for multi LLM ecosystems, proposing a framework for quantifying and improving model reliability under regulated constraints. By analyzing system behaviors, evaluating uncertainty across multiple LLMs, and implementing dynamic monitoring pipelines, the study demonstrates practical pathways for operational trustworthiness. Case studies from financial compliance and healthcare diagnostics illustrate the applicability of adaptive trust metrics in real world settings. The findings position adaptive trust measurement as a foundational enabler for safe and scalable AI adoption in regulated industries.