The Price of Scale in Proactive Memory Recall
Read the original on arXiv AI →The paper investigates how a long‑lived assistant decides whether to interrupt a user with a memory recall, framing the decision as a multiple‑testing problem whose difficulty scales with the size of the memory store, N. Experiments on LongMemEval and LoCoMo show that a similarity threshold tuned to a 5% false‑alarm rate at N=8 achieves 87–100% hit rates at N=2048, and theoretical analysis predicts a per‑item false‑positive rate of about –ln(1-α)/N, leading to a power‑law decay of hit rate with N. The study also demonstrates that lexical and semantic channels require thresholds growing like ln N and √ln N, that per‑cue normalisation roughly doubles hit rates, and introduces a cue‑swap conformal gate that achieves near‑label‑tuned performance without labels. whyItMatters":"The work provides a quantitative understanding of how memory‑recall systems must adjust thresholds as their knowledge base grows, informing the design of more efficient and user‑friendly AI assistants."
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