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."
arXiv:2610.15710v1 Announce Type: new Abstract: Long-term memory for assistants needs a gate that decides when a new utterance retires an old stored claim. Benchmark labels for this step mark a pair...
arXiv:2609.06872v1 Announce Type: new Abstract: When a user asks an assistant to forget a record, the test is whether the memory now matches the state it would hold if the record had never been store...
The paper investigates how persistent memory in AI agents can lead to over‑trust in stale facts, creating a "Memory Trust Gap" that worsens as model capability increases. Using a benchmark with Benefit and Safety suites across Qwen3 models of varying sizes, the authors show that larger models are more prone to harmful over‑trust, especially when metadata is absent or misleading. They also demonstrate that mitigation strategies such as exposing metadata or pre‑resolving conflicts improve accuracy, but the effectiveness depends on model size and dataset.
arXiv:2608.30376v1 Announce Type: cross Abstract: Recurrent, attention-free sequence models share a structural weakness: a fading state cannot perform exact recall of something seen once, far in the...
arXiv:2607. 06503v1 Announce Type: new Abstract: Large language model (LLM) agents solving multi-step tasks frequently commit to trajectories that are doomed to fail, yet continue to consume substantial inference compute before the failure becomes observable.
arXiv:2608. 03372v1 Announce Type: cross Abstract: AI systems rewrite information constantly: conversations become stored memories, documents become answers.
arXiv:2609.22818v1 Announce Type: cross Abstract: Memory-poisoning defenses for LLM agents are typically evaluated by their ability to prevent attacks. However, the traffic they process is rarely adv...
arXiv:2607. 12204v1 Announce Type: new Abstract: Attention can be viewed as an online learner over context, yet existing test-time memories cannot certify that dropping a token leaves outputs unchanged or delete its influence outright.
arXiv:2609.37474v1 Announce Type: new Abstract: Persistent memory creates a control problem that retrieval relevance alone does not solve: a memory can remain highly useful after an update, deletion,...
arXiv:2607. 09889v1 Announce Type: cross Abstract: Fixed-state sequence models compress an unbounded past into a bounded state, which caps their associative recall at roughly the state dimension; attention escapes the cap by keeping a key-value entry for every token, at quadratic compute and a cache that grows with the sequence.
arXiv:2606. 27472v1 Announce Type: cross Abstract: Large language model (LLM) agents operate over long, multi-session interactions in which facts change: a user moves, a price updates, a plan is revised.
arXiv:2606. 31495v1 Announce Type: new Abstract: We study a single idea across two settings: that a prediction-error signal, computed by a small predictor over the latent space of a frozen encoder, can serve both as a gate on plasticity and as a substrate for metacognition.