mlsanityData to 14 Oct 2026

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

arXiv 2506.20018 ↗First posted 24 Jun 2025cs.AI cs.ARcited by 1

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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.

Key facts. Cited by 1 paper 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.

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80 of 90 references.

References · 80

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38 more
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