Learning-Augmented Heuristics: Simple, yet Smart, Robust and Interpretable Cache Eviction

[2608.27975] Learning-Augmented Heuristics: Simple, yet Smart, Robust and Interpretable Cache Eviction Skip to main content Search Submit Donate Log in Computer Science > Distributed, Parallel, and Cluster Computing arXiv:2608.27975 (cs) [Submitted on 28 Aug 2026] Title: Learning-Augmented Heuristics: Simple, yet Smart, Robust and Interpretable Cache Eviction Authors: Haocheng Xia , William Nixon , Bintang Dwi Marthen , Pranav Bhandari , Juncheng Yang View PDF HTML (experimental) Abstract: Caching is widely used across the system stack to improve performance and efficiency, with eviction algorithms at its core. Existing cache eviction policies fall into two broad categories: static heuristics (e.g., 2Q, S3-FIFO) and smart algorithms (e.g., ARC, LRB). Smart caches can adapt to workloads and have the potential to achieve higher efficiency and robustness than static heuristics. However, we find that existing smart caches suffer from objective mismatches and instability. We introduce Learning-Augmented Heuristics (LAH), a framework that learns the cache-level parameters of static heuristics. By decoupling the data and control planes, LAH supports simple, high-speed data reads and writes on the data plane, while performing occasional asynchronous learning on the control plane using cache-level features. We demonstrate the effectiveness of LAH through S4-FIFO, a Smart S3-FIFO cache eviction algorithm. We pre-train a single model on 4,140 production traces and embed it in S4-FIFO to learn optimal cache parameters. On 1,035 evaluation traces, S4-FIFO improves the mean efficiency by 26% compared to S3-FIFO and by 8% compared to 3L-Cache, the best state-of-the-art algorithm. S4-FIFO is also robust---increasing miss ratio over FIFO by 0.8% on the worst trace, whereas 3L-Cache increases FIFO's miss ratio by 8.8%. Finally, S4-FIFO's decisions are also interpretable: a language model can provide a rationale for why a particular configuration was chosen. Comments: 22 pages, accepted to OSDI '26 Subjects: Distributed, Parallel, and Cluster Computing (cs.DC) Cite as: arXiv:2608.27975 [cs.DC] (or arXiv:2608.27975v1 [cs.DC] for this version) https://doi.org/10.48550/arXiv.2608.27975 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Haocheng Xia [ view email ] [v1] Fri, 28 Aug 2026 06:45:24 UTC (2,523 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.DC < prev | next > new | recent | 2026-08 Change to browse by: cs References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

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