# Achieving Trustworthy Real-Time Decision Support Systems with Low-Latency Interpretable AI Models

arXiv: https://arxiv.org/abs/2506.20018 · HTML: https://mlsanity.com/paper/2506.20018/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 80 of its 90 references that are in the map.
- First posted on arXiv 24 Jun 2025.

## Builds on (most cited first, 30 of 80)

- [Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing](https://mlsanity.com/paper/1910.05316/) (2019)
- [A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness](https://mlsanity.com/paper/2411.03350/) (2024)
- [Empowering Things with Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things](https://mlsanity.com/paper/2011.08612/) (2020)
- [Towards a Science of Human-AI Decision Making: A Survey of Empirical Studies](https://mlsanity.com/paper/2112.11471/) (2021)
- [The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities](https://mlsanity.com/paper/2408.13296/) (2024)
- [Determinants of LLM-assisted Decision-Making](https://mlsanity.com/paper/2402.17385/) (2024)
- [Med42 -- Evaluating Fine-Tuning Strategies for Medical LLMs: Full-Parameter vs. Parameter-Efficient Approaches](https://mlsanity.com/paper/2404.14779/) (2024)
- [Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI](https://mlsanity.com/paper/2111.06061/) (2021)
- [Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges](https://mlsanity.com/paper/1907.09059/) (2019)
- [Improving Human-AI Collaboration With Descriptions of AI Behavior](https://mlsanity.com/paper/2301.06937/) (2023)
- [Fine-tuning and Utilization Methods of Domain-specific LLMs](https://mlsanity.com/paper/2401.02981/) (2024)
- [Advancing Human-AI Complementarity: The Impact of User Expertise and Algorithmic Tuning on Joint Decision Making](https://mlsanity.com/paper/2208.07960/) (2022)
- [Edge Impulse: An MLOps Platform for Tiny Machine Learning](https://mlsanity.com/paper/2212.03332/) (2022)
- [100 instances is all you need: predicting the success of a new LLM on unseen data by testing on a few instances](https://mlsanity.com/paper/2409.03563/) (2024)
- [DeLLMa: Decision Making Under Uncertainty with Large Language Models](https://mlsanity.com/paper/2402.02392/) (2024)
- [Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligence](https://mlsanity.com/paper/2105.01798/) (2021)
- [Human-AI collaboration is not very collaborative yet: A taxonomy of interaction patterns in AI-assisted decision making from a systematic review](https://mlsanity.com/paper/2310.19778/) (2023)
- [Efficient Sequential Decision Making with Large Language Models](https://mlsanity.com/paper/2406.12125/) (2024)
- [Distributed Deep Convolutional Neural Networks for the Internet-of-Things](https://mlsanity.com/paper/1908.01656/) (2019)
- [Towards Optimizing Human-Centric Objectives in AI-Assisted Decision-Making With Offline Reinforcement Learning](https://mlsanity.com/paper/2403.05911/) (2024)
- [FastML Science Benchmarks: Accelerating Real-Time Scientific Edge Machine Learning](https://mlsanity.com/paper/2207.07958/) (2022)
- [RT-LM: Uncertainty-Aware Resource Management for Real-Time Inference of Language Models](https://mlsanity.com/paper/2309.06619/) (2023)
- [When IoT Meet LLMs: Applications and Challenges](https://mlsanity.com/paper/2411.17722/) (2024)
- [Bridging the Bosphorus: Advancing Turkish Large Language Models through Strategies for Low-Resource Language Adaptation and Benchmarking](https://mlsanity.com/paper/2405.04685/) (2024)
- [Utilizing Human Behavior Modeling to Manipulate Explanations in AI-Assisted Decision Making: The Good, the Bad, and the Scary](https://mlsanity.com/paper/2411.10461/) (2024)
- [Decoding AI's Nudge: A Unified Framework to Predict Human Behavior in AI-assisted Decision Making](https://mlsanity.com/paper/2401.05840/) (2024)
- [A Multi-Stage Automated Online Network Data Stream Analytics Framework for IIoT Systems](https://mlsanity.com/paper/2210.01985/) (2022)
- [Do Generative Large Language Models need billions of parameters?](https://mlsanity.com/paper/2309.06589/) (2023)
- [TrainerAgent: Customizable and Efficient Model Training through LLM-Powered Multi-Agent System](https://mlsanity.com/paper/2311.06622/) (2023)
- [Learning Complementary Policies for Human-AI Teams](https://mlsanity.com/paper/2302.02944/) (2023)

## Built on by (most cited first, 1 of 1)

- [When Misinformation Speaks and Converses: Rethinking Fact-Checking in Audio Platforms](https://mlsanity.com/paper/2604.16767/) (2026)

## Abstract (arXiv)

This paper investigates real-time decision support systems that leverage low-latency AI models, bringing together recent progress in holistic AI-driven decision tools, integration with Edge-IoT technologies, and approaches for effective human-AI teamwork. It looks into how large language models can assist decision-making, especially when resources are limited. The research also examines the effects of technical developments such as DeLLMa, methods for compressing models, and improvements for analytics on edge devices, while also addressing issues like limited resources and the need for adaptable frameworks. Through a detailed review, the paper offers practical perspectives on development strategies and areas of application, adding to the field by pointing out opportunities for more efficient and flexible AI-supported systems. The conclusions set the stage for future breakthroughs in this fast-changing area, highlighting how AI can reshape real-time decision support.