SuperLocalMemory 4.0: The Governed Memory Operating System for AI Agents

[2608.08253] SuperLocalMemory 4.0: The Governed Memory Operating System for AI Agents Skip to main content Search Submit Donate Log in Computer Science > Artificial Intelligence arXiv:2608.08253 (cs) [Submitted on 8 Aug 2026 ( v1 ), last revised 25 Aug 2026 (this version, v2)] Title: SuperLocalMemory 4.0: The Governed Memory Operating System for AI Agents Authors: Varun Pratap Bhardwaj , Garima Singh , Arun Pratap Bhardwaj View PDF HTML (experimental) Abstract: We present SuperLocalMemory 4.0, a governed, local-first memory operating system for AI agents, unifying multi-channel retrieval under reciprocal-rank fusion, bi-temporal recall, multi-scope isolation, role-based access, verified erasure, and a hash-chained audit trail. A reliability spine governs the primary write path: generation-fenced admission, verifiable memory transactions with per-projection apply, verify, compensate and erase owners, and hash-checkable completion manifests. Eleven fault-injection scenarios, each repeated 200 times, upheld 2,199 of 2,200 scoped component properties. This version leads with a negative result. Ten mechanisms here were implemented, reachable on a live call path, and ineffective at their final connection. Implemented, reachable and effective are three different questions, and the third requires an oracle independent of the mechanism under test. We contribute two mechanical invariants that supply one: a prior-distance assertion over Bayesian learners, and a join-liveness assertion over schema-guarded paths that reports where a guard's missing data resides. A three-arm ablation varying only the recall session-identifier namespace moves no posterior with the defect present and every instantiated arm with it absent, while a negative control that writes every ticket but supplies no engagement settles nothing. We withdraw the previous version's governed write-envelope overhead figure: the two paths it differenced are not comparable. Timing the envelope in place gives an 11.0 ms governed write of which the envelope is 70.6 percent, but the generation fence costs 1.9 microseconds and the obligation ledger 42 microseconds. The cost is durability, not governance. Comments: 54 pages, 15 figures, 8 tables. Substantially revised: retracts two claims from v1 and adds two controlled experiments. Code: this https URL Zenodo DOI: https://doi.org/10.5281/zenodo.21853302 . Code: this https URL Subjects: Artificial Intelligence (cs.AI) ; Information Retrieval (cs.IR) ACM classes: I.2.6; H.3.3 Cite as: arXiv:2608.08253 [cs.AI] (or arXiv:2608.08253v2 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.08253 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Varun Pratap Bhardwaj [ view email ] [v1] Sat, 8 Aug 2026 17:26:58 UTC (53 KB) [v2] Tue, 25 Aug 2026 05:47:19 UTC (83 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.AI < prev | next > new | recent | 2026-08 Change to browse by: cs cs.IR 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. 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