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
