Man-Made Heuristics Are Dead. Long Live Code Generators!
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917001327280128 |
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| author | Dwivedula, Rohit Saxena, Divyanshu Akella, Aditya Chaudhuri, Swarat Kim, Daehyeok |
| author_facet | Dwivedula, Rohit Saxena, Divyanshu Akella, Aditya Chaudhuri, Swarat Kim, Daehyeok |
| contents | Policy design for various systems controllers has conventionally been a manual process, with domain experts carefully tailoring heuristics for the specific instance in which the policy will be deployed. In this paper, we re-imagine policy design via a novel automated search technique fueled by recent advances in generative models, specifically Large Language Model (LLM)-driven code generation. We outline the design and implementation of PolicySmith, a framework that applies LLMs to synthesize instance-optimal heuristics. We apply PolicySmith to two long-standing systems policies - web caching and congestion control, highlighting the opportunities unraveled by this LLM-driven heuristic search. For caching, PolicySmith discovers heuristics that outperform established baselines on standard open-source traces. For congestion control, we show that PolicySmith can generate safe policies that integrate directly into the Linux kernel. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_08803 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Man-Made Heuristics Are Dead. Long Live Code Generators! Dwivedula, Rohit Saxena, Divyanshu Akella, Aditya Chaudhuri, Swarat Kim, Daehyeok Operating Systems Distributed, Parallel, and Cluster Computing Machine Learning Neural and Evolutionary Computing Policy design for various systems controllers has conventionally been a manual process, with domain experts carefully tailoring heuristics for the specific instance in which the policy will be deployed. In this paper, we re-imagine policy design via a novel automated search technique fueled by recent advances in generative models, specifically Large Language Model (LLM)-driven code generation. We outline the design and implementation of PolicySmith, a framework that applies LLMs to synthesize instance-optimal heuristics. We apply PolicySmith to two long-standing systems policies - web caching and congestion control, highlighting the opportunities unraveled by this LLM-driven heuristic search. For caching, PolicySmith discovers heuristics that outperform established baselines on standard open-source traces. For congestion control, we show that PolicySmith can generate safe policies that integrate directly into the Linux kernel. |
| title | Man-Made Heuristics Are Dead. Long Live Code Generators! |
| topic | Operating Systems Distributed, Parallel, and Cluster Computing Machine Learning Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2510.08803 |