The October 9, 2025 paper from UT Austin paper introduces PolicySmith, a novel framework that automates the design of system policies, arguing that the traditional manual creation of heuristics by experts is becoming inefficient due to rapidly changing environments. PolicySmith leverages Large Language Models (LLMs) and evolutionary search to generate instance-optimal heuristic code that is tailored to specific workloads and hardware contexts. The authors demonstrate the framework's effectiveness in two critical systems domains: discovering superior cache eviction policies for web caching and generating functional, safe policies for Linux kernel congestion control through eBPF. This research proposes a fundamental shift, moving policy intelligence from fixed rules to an automated process of code generation, which results in more performant and context-aware system policies compared to established human-designed and pure machine-learning baselines. Source: https://arxiv.org/pdf/2510.08803