Skill UI

This guide helps authors position their machine learning systems manuscripts for the rigorous Conference on Machine Learning and Systems (MLSys). It details the venue's specific focus on system-level contributions, including training/inference infrastructure, compilers, data systems, and hardware-aware ML. Learn how to frame your work to appeal to systems reviewers by emphasizing measurable impacts on capability, cost, reliability, or deployment scale.

Autonomous agents are AI systems capable of independently decomposing goals, planning actions, executing tools, and self-correcting without constant human guidance. This skill focuses on ensuring reliability in agent loops like ReAct and Plan-Execute, emphasizing goal decomposition and reflection patterns. It highlights that compounding error rates can kill autonomy, advocating for constrained, domain-specific agents over general autonomy. Build for reliability first.