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arXiv AI By Isaac Kofi Nti

Diagnosing Capability Preservation and Task Sensitivity in Memory Augmented Document Classifiers

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The paper introduces Protected QL Memory, a dual‑path document classifier that separates a causal local pathway from an associative matrix writer and a finalized memory reader. It evaluates three empirical properties—capability preservation, diagnostic access, and task performance sensitivity—across nine dataset‑seed conditions, finding that writer capability is fully preserved while diagnostic accuracy gaps are large but macro‑F1 scores change minimally. The study concludes that preserving associative capability and diagnostic access does not guarantee downstream reliance on memory, urging separate evaluation of these objectives.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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