ML for Systems

Machine Learning (ML) for Systems applies machine learning techniques to computer systems. With the rise of large language models (LLMs) and generative AI agents, ML has the potential to revolutionize the entire hardware and software stack, replacing long-standing heuristics and even the process in which these systems are designed and implemented. This has led to a paradigm shift in systems design, such as automating multi-objective tasks including designing new data structures [1], integrated circuits [2, 3], and design verification [20, 21], implementing control algorithms for applications including compilers [12, 13, 19], databases [8], operating systems [37], memory management [9, 10], cloud platform orchestration [33, 34], and ML training frameworks [11]. The rise of LLMs and generative AI agents has presented new opportunities and challenges within the diverse domain of computer systems. For the 10th edition of the ML for Systems workshop, we are shaping the program around three key pillars: (1) Pushing the frontier: demanding mature, scalable, and cost-efficient research on LLMs/agents for systems problems that moves the community beyond one-off demonstrations; (2) Agents for cybersecurity and agentic security: Introducing a critical new focus on security and reliability as LLM-generated code and autonomous agents increasingly enter production systems, to have a grounded understanding besides lay reports on "Mythos solved security"; and (3) Charting the future: Hosting a 10-year retrospective and forward-looking panel to define the next generation of systems intelligence.