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        <datestamp>2026-09-29T09:56:18Z</datestamp>
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        <dc:title>Delete Names Five Operations: Erasure Semantics for Derived Agent Memory, and Why a State-Level Definition Cannot Be the Auditable One</dc:title>
        <dc:creator>Pranay Mahendrakar</dc:creator>
        <dc:subject>agent memory</dc:subject>
        <dc:subject>right to erasure</dc:subject>
        <dc:subject>machine unlearning</dc:subject>
        <dc:subject>data provenance</dc:subject>
        <dc:subject>view update problem</dc:subject>
        <dc:subject>retrieval-augmented generation</dc:subject>
        <dc:subject>privacy</dc:subject>
        <dc:subject>deletion semantics</dc:subject>
        <dc:description>(c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0.
Persistent memory has become a standard component of language-model agents, and with it a standard assumption: that where a fact lives in a retrievable store rather than in weights, erasing it is a solved problem, because a store supports deletion and weights do not. Agent memory does not hold the records that assumption presumes. It holds derivations - extracted facts, consolidated summaries, reflections, learned procedures, and edits made to earlier entries because a later one arrived - and deleting a source does not remove what was derived from it. This paper separates the operations that the single word delete is used for in this literature: removing the stored item, withdrawing everything derived from it, barring it from supporting an outgoing claim, making the fact non-inferable from what remains, and restoring the store to the state that would have obtained had the item never been written. Three systems published in 2026 supply a semantics for one of these each, and they are not the same semantics. The paper argues that the second is the database view-deletion problem, which has a known structure - no unique source update, and two inequivalent minimality objectives - that the agent-memory literature is rediscovering without the vocabulary; that the fourth is not a property of a record at all, and that deletion is itself an inference signal; and that the fifth is not defined for LLM-mediated consolidation, because the same trajectories are reported to yield different memories under different update schedules. The parametric unlearning literature reached the parallel conclusion in 2021, that only an algorithmic-level definition is auditable. What is not known is stated flatly: no work located for this paper reports what fraction of a production memory store&apos;s entries carry a recoverable derivation link to their sources. Six measurements are specified.
The literature search, drafting and citation verification for this paper were carried out with AI assistance under the author&apos;s direction. Every citation was machine-verified against the arXiv API and Crossref before inclusion, and every quantitative claim was read back against the cited source&apos;s own abstract or, where a claim is drawn from a paper&apos;s body, against the located passage. The author is responsible for the final text and for all claims made in it.</dc:description>
        <dc:publisher>Life of Research</dc:publisher>
        <dc:date>2026-09-06</dc:date>
        <dc:type>info:eu-repo/semantics/article</dc:type>
        <dc:identifier>https://lifeofresearch.com/articles/65</dc:identifier>
        <dc:identifier>https://doi.org/10.5281/zenodo.23022338</dc:identifier>
        <dc:language>eng</dc:language>
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