Holographic Invariant Storage: Design-Time Safety Contracts via Vector Symbolic Architectures

[2603.13558] Holographic Invariant Storage: Design-Time Safety Contracts via Vector Symbolic Architectures Skip to main content Search Submit Donate Log in Statistics > Machine Learning arXiv:2603.13558 (stat) This paper has been withdrawn by Arsenios Scrivens [Submitted on 13 Mar 2026 ( v1 ), last revised 24 Aug 2026 (this version, v2)] Title: Holographic Invariant Storage: Design-Time Safety Contracts via Vector Symbolic Architectures Authors: Arsenios Scrivens No PDF available, click to view other formats Abstract: We introduce Holographic Invariant Storage (HIS), a protocol that assembles known properties of bipolar Vector Symbolic Architectures into a design-time safety contract for LLM context-drift mitigation. The contract provides three closed-form guarantees evaluable before deployment: single-signal recovery fidelity converging to $1/\sqrt{2} \approx 0.707$ (regardless of noise depth or content), continuous-noise robustness $2\Phi(1/\sigma) - 1$, and multi-signal capacity degradation $\approx\sqrt{1/(K+1)}$. These bounds, validated by Monte Carlo simulation ($n = 1{,}000$), enable a systems engineer to budget recovery fidelity and codebook capacity at design time -- a property no timer or embedding-distance metric provides. A pilot behavioral experiment (four LLMs, 2B--7B, 720 trials) confirms that safety re-injection improves adherence at the 2B scale; full results are in an appendix. Comments: The experiment has a design defect: the VSA drift trigger was not in the causal path of its own runs, so the results do not test the claimed mechanism. Clean-room replication: content-aware re-injection beats neither random re-injection (p=0.46) nor a fixed timer (p=0.29), while positive controls confirm task signal (p=0.002, p=0.001). The conclusions are unsupported and should not be cited Subjects: Machine Learning (stat.ML) ; Computation and Language (cs.CL); Information Theory (cs.IT); Machine Learning (cs.LG) ACM classes: I.2.6; I.2.4 Cite as: arXiv:2603.13558 [stat.ML] (or arXiv:2603.13558v2 [stat.ML] for this version) https://doi.org/10.48550/arXiv.2603.13558 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Arsenios Scrivens [ view email ] [v1] Fri, 13 Mar 2026 19:51:50 UTC (765 KB) [v2] Mon, 24 Aug 2026 20:39:33 UTC (1 KB) (withdrawn) Full-text links: Access Paper: Withdrawn Current browse context: stat.ML < prev | next > new | recent | 2026-03 Change to browse by: cs cs.CL cs.IT cs.LG math math.IT stat References & Citations NASA ADS 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) Major funding support from

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