Delete Names Five Operations: Erasure Semantics for Derived Agent Memory, and Why a State-Level Definition Cannot Be the Auditable One

Abstract

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's entries carry a recoverable derivation link to their sources. Six measurements are specified.

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About the author

Pranay Mahendrakar is an ai specialist and large language model engineer based in Bengaluru, India. He builds production artificial intelligence systems and publishes open-access research on how those systems fail. See all 66 papers by Pranay Mahendrakar, or his ORCID record.