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You will begin your thesis by conducting a comprehensive literature review on memory in agents, analyzing existing benchmark implementations, datasets, and methods to build a deep understanding of the field while also exploring the domain of continual learning.
Building on this foundation, you will adapt existing benchmarks or implement your own for Bosch-related use cases.
In this context, you will write code to apply LLMs in an agentic setting, with a particular focus on agent memory.
Based on these insights, you will derive and implement methods aimed at improving the memory of continually learning agentic systems.
Finally, you will rigorously evaluate the performance of the developed approaches on standard academic benchmarks as well as Bosch use cases, while you will analyze scalability, robustness, and deployment potential.
You will carry out all of these tasks within a tight project timeline, with your results strongly encouraged to be submitted to major upcoming machine learning conferences, while performing effectively under deadline driven time pressure is mandatory.