Oct 2026· Adjunct Proceedings of the 14th Nordic Conference on Human-Computer Interaction· 0 citations· 16 references
TL;DR
It is demonstrated that researchers may make environmentally sustainable choices in research practices by making deliberate choices about agentic AI use, and that the use of LLMs versus other software largely drives the power consumption difference.
Abstract
Generative AI has been rapidly adopted into multiple research practices. In this paper, we focus on the newest trend, so-called agentic AI. We calculate the carbon footprint of using agentic AI in a simulation of an academic literature review. We show that the energy consumption increases proportionately with the level of agency, resulting in a carbon footprint about three times larger when moving from low agency to a multi-agent process. The most co2stly step of this system is processing large quantities of text by the LLM, and resource consumption grows proportionally to the model size. Finally, we show the energy consumption of the CPU and GPU during processing and illustrate that the use of LLMs versus other software largely drives the power consumption difference. We demonstrate that researchers may make environmentally sustainable choices in research practices by making deliberate choices about agentic AI use.1
Background. Agentic AI systems independently decompose tasks such as literature search, data analysis, and programming into subtasks, search the web, access databases, and execute code. This allows them to perform digital research tasks at high speed. Objectives. Under what conditions does the use of agentic systems pr...
The advancement of AI in science raises broader questions concerning the division of cognitive labour between human researchers and machines, and a typology of AI systems used in research is proposed, ranging from specialised scientific AI through scientific AI assistants and agents to hybrid experimental systems that...
P. Jedlička· Teorie vědy / Theory of Scie...· 0 citations
The implications of this shift in mindset are explored and lessons learned are shared that hopefully illustrate this shift in emphasis with current agentic technology.
Atria Dawn Preview is introduced, a foundation agentic language model designed for scientific research and engineering workflows, with the goal of expanding the frontier of agent productivity in the real world.
This survey bridges the existing gap by presenting a comprehensive blueprint for scientific agents' design and introduces a unified taxonomy based on capability envelope and capability maturity, characterizing both the scope of scientific workflow coverage and the reliability of agent behavior under realistic research...
Xin-Ming Wang, Jian Xu, Sheng Lian et al.· IEEE Transactions on Pattern...· 11 citations
Agentic AI systems are increasingly viewed as a viable response to the shortcomings of static, rigid, and human-in-the-loop Artificial Intelligence (AI) systems. This is because autonomous operation enables rapid adaptation to dynamic, complex problems with improved time-critical behaviour under real-world constraints....
S. S. Gill, S. S. Murugesan, K. Anurag et al.· PLOS Complex Systems· 4 citations
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
MIT News · Artificial Intelligence· news.mit.eduOct 15, 2026
