🗓️ Posted: October 17, 2025
Audrey Cheng, Shu Liu, Melissa Pan, Ion Stoica, and the ADRS team
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AI is no longer just tuning systems as a "black box." It's now rewriting their core algorithms by treating the system as a "white box" and discovering solutions that can outperform human experts. This new approach, which we term AI-Driven Research for Systems (ADRS), can automate some of the most tedious parts of research.
https://github.com/UCB-ADRS/ADRS
Using AI to improve computer systems has been an active area of research for the past decade. However, most prior work has treated systems as black boxes, leveraging AI to tune configuration knobs or using DNNs, and more recently LLMs, to replace individual components such as schedulers or query optimizers.
This is now changing. Instead of treating systems as black boxes, we are beginning to view them as white boxes, where AI tools can rewrite system code itself to improve performance (Figure 1). In other words, AI is starting to do what systems researchers have traditionally done.
Figure 1. A phase change from black box to white box.
This shift is being enabled by the recent emergence of LLM-based evolutionary tools to automate the discovery and evaluation of algorithms. FunSearch from DeepMind demonstrated it was possible to improve state-of-the-art algorithms in mathematics. Subsequently, AlphaEvolve from Google showed evolutionary techniques could dramatically improve system performance. Similarly, GEPA demonstrated success in discovering high-performing solutions using genetic prompt evolution. Recently, OpenEvolve emerged as a powerful open-source implementation, and of course, general-purpose coding assistants are becoming more capable every day.
Over this summer, we ran a seminar asking students in our lab (Sky Computing Lab) to apply these tools to their own research projects. The results, across nearly a dozen projects, have been encouraging. In multiple cases, using these tools didn't just match state-of-the-art, human-designed algorithms—it outperformed them!
https://drive.google.com/file/d/1KhbOSYIpxL0m4ZxGenVFrN-SR2OzDlnx/view?usp=sharing
Table 1 presents an overview of our results. In nearly all cases, LLMs were able to discover solutions that outperformed state-of-the-art baselines. Most of these solutions were discovered in under 12 hours, at a cost of less than $20. Importantly, the results we're sharing should be seen as a starting point; as the frameworks and models improve, we expect even more improvements.
Table 1. Results of applying ADRS to 11 different research problems.
The key to these successes lies in automating the core research loop, the iterative cycle of designing, implementing, and evaluating solutions, where researchers typically spend most of their time. We refer to this approach as AI-Driven Research for Systems (ADRS).
The ADRS framework automates several important parts of the research process. Typically, when working on a systems problem, a researcher follows the following stages (Figure 2):