A Biophysically-Inspired Feedback Controller for Multi-Class Cache Fairness

[2608.14561] A Biophysically-Inspired Feedback Controller for Multi-Class Cache Fairness Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Distributed, Parallel, and Cluster Computing arXiv:2608.14561 (cs) [Submitted on 26 May 2026] Title: A Biophysically-Inspired Feedback Controller for Multi-Class Cache Fairness Authors: Matt R. Flax View a PDF of the paper titled A Biophysically-Inspired Feedback Controller for Multi-Class Cache Fairness, by Matt R. Flax View PDF HTML (experimental) Abstract: Cache replacement under multi-tenant LLM-serving conditions is a multi-class problem: short, high-reuse system prompts; long, moderate-reuse user documents; medium-length code context; and bursty conversation history share a single eviction pool. Under skewed multi-class arrivals, conventional flat-LRU policies expose the worst-served-class miss ratio ($m_{\max}$) only as a fixed point. We introduce a class of cache-replacement policies parameterised by a per-class flux formula, where three structural commitments -- a single global token-mass imbalance signal, $K$ parallel rectified per-class promotion accumulators, and an age-ordered eviction backstop -- produce emergent multi-class fairness. We instantiate this class with a linear V-coupled rectified flux and a Goldman-Hodgkin-Katz extension whose $V \to 0$ limit is exactly the linear form. Across four skew levels on synthetic multi-class workloads, the policy class closes 27--72\,\% of the LRU$\to$Belady gap on $m_{\max}$, with linear and GHK interchangeable on the headline objective within search variance. The fairness/throughput tradeoff is exposed as a tunable knob on a single hyperparameter axis. We position this against the LeCaR feedback-controller lineage and the formal-control-theory cache-decay lineage as a novel combination of known ingredients. Code and reproduction scripts: this https URL Comments: 24 pages, 1 figure Subjects: Distributed, Parallel, and Cluster Computing (cs.DC) ; Operating Systems (cs.OS); Performance (cs.PF) ACM classes: D.4.2 Cite as: arXiv:2608.14561 [cs.DC] (or arXiv:2608.14561v1 [cs.DC] for this version) https://doi.org/10.48550/arXiv.2608.14561 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Matt Flax [ view email ] [v1] Tue, 26 May 2026 09:00:24 UTC (45 KB) Full-text links: Access Paper: View a PDF of the paper titled A Biophysically-Inspired Feedback Controller for Multi-Class Cache Fairness, by Matt R. Flax View PDF HTML (experimental) TeX Source view license Current browse context: cs.DC < prev | next > new | recent | 2026-08 Change to browse by: cs cs.OS cs.PF References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: 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) Major funding support from

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