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Other literature type . 2026
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Other literature type . 2026
License: CC BY
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Presentation . 2026
License: CC BY
Data sources: Datacite
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Presentation . 2026
License: CC BY
Data sources: Datacite
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Vision Language Models & Agentic Systems

Authors: Haase, Robert;

Vision Language Models & Agentic Systems

Abstract

In these slide decks, we introduce Vision Language Models (VLM), a form of Generative Artificial Intelligence that can combine textual information and image data. One the one hand these can classify, interpret and describe images. To retrieve the correct information from VLMs, prompt engineering plays a key-role. On the other hand, image-generation models can produce images from text alone, or by combining images and text. The second slide deck introduces agentic systems, as a form of computer system that acts on behalf of humans. Und the hood, large language models run that can call functions from a pre-defined list of available tools, or generate code. After function and/or code execution, these systems interpret the result and plan next steps. As an example of ongoing standardization efforts in this domain, we learn about the Model Context Protocol. Both, VLMs and agents, bring new challenges to our society which are also part of the materials to start a discussion.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average