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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.20018 |
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| _version_ | 1866917138298568704 |
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| author | Deng, Zechun Liu, Ziwei Bi, Ziqian Song, Junhao Liang, Chia Xin Yeong, Joe Song, Xinyuan Hao, Junfeng |
| author_facet | Deng, Zechun Liu, Ziwei Bi, Ziqian Song, Junhao Liang, Chia Xin Yeong, Joe Song, Xinyuan Hao, Junfeng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_20018 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Achieving Trustworthy Real-Time Decision Support Systems with Low-Latency Interpretable AI Models Deng, Zechun Liu, Ziwei Bi, Ziqian Song, Junhao Liang, Chia Xin Yeong, Joe Song, Xinyuan Hao, Junfeng Artificial Intelligence Hardware Architecture 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. |
| title | Achieving Trustworthy Real-Time Decision Support Systems with Low-Latency Interpretable AI Models |
| topic | Artificial Intelligence Hardware Architecture |
| url | https://arxiv.org/abs/2506.20018 |