Presentation, Not Mechanism: A Render Confound in Deprecation-Aware Memory Evaluation
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Computer Science > Machine Learning
Title:Presentation, Not Mechanism: A Render Confound in Deprecation-Aware Memory Evaluation
Abstract:AI systems increasingly retrieve from records that revise themselves: issue threads, encyclopedic histories, policy logs, and long conversations. The challenge is not only finding relevant evidence, but deciding which claims remain in force, which were superseded, and when to abstain. Structured memories promise to solve this with typed edges, temporal updates, and conflict status, yet evaluations often change mechanism and prompt presentation together. We study this as Evidence-State Revision, comparing flat retrieval, coarse edge invalidation, and fine-grained RevisionLedger on 2,907 high-agreement questions from GitHub, multi-repo issue histories, Wikipedia, and DyKnow-style temporal streams. A render-matched control (same layout, deprecation disabled) reveals the central confound: when a value is changed and later restored, RevisionLedger appears to beat a flat baseline by +0.182, but almost all the gain comes from easier presentation; the fine-grained mechanism residual is indistinguishable from zero (+0.021 to +0.025 across two judge families). After presentation is controlled, coarse invalidation is the only mechanism that pays for current-state queries, beating the fine ledger by 0.084; the same query-sufficiency principle says provenance mainly needs retained invalidated evidence, not richer typing. Memory evaluations should hold render fixed, and deprecation-aware systems should deploy the coarsest retained state that covers their queries.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2607.16019 [cs.LG] |
| (or arXiv:2607.16019v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2607.16019
arXiv-issued DOI via DataCite (pending registration)
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