arXiv AI By Minkyu Song

When Retrieval Fails Before It Begins: Structurally Indirect Prerequisite Eviction as a Retention Failure in Agentic Memory

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The paper introduces a new failure mode in agentic memory systems called structurally indirect prerequisite eviction, where upstream memory blocks that are only weakly aligned with a query are prematurely discarded due to budget constraints. The authors define this failure operationally, present a deterministic benchmark and trace diagnostics, and evaluate a one‑hop graph‑aware rule called Dependency‑aware Semantic Garbage Collection (DSGC). DSGC dramatically improves full‑chain retention rates across different encoders, and the study identifies the budget and scaling regimes where the rule is effective or fails.

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arXiv AI
Oct 16

Beyond Downstream QA: Rethinking How We Evaluate Agent Memory

The paper critiques current agent memory benchmarks that rely on downstream QA accuracy, arguing they conflate memory correctness with retrieval and generation quality, leading to misleading results. It introduces FEB-30, a new benchmark of 30 fact‑evolution trajectories across six diagnostic categories and three difficulty levels, generated automatically to provide deterministic ground truth. Using FEB-30, the authors show a significant divergence between intrinsic and extrinsic evaluations, revealing that a representative memory system correctly classifies memory operations only 20% of the time—an error invisible to traditional QA metrics.

By Sebastian-Antonio Toma, Jean de Kernier, Mike Ply, Oliviu Matei
arXiv AI
Oct 16

MemCut: Every Secret Leaves a Trail in the Long-Term Memory of LLM Agents

MemCut introduces a benchmark to test user-requested forgetting in large language model agents, featuring 456 hand-designed secrets embedded in long-horizon conversations with fully annotated evidence trails. The benchmark defines two recovery metrics—native and residual—to assess how well secrets can be retrieved after deletion. Experiments across five memory designs and four LLMs show that deletion reduces native recovery more than residual recovery, highlighting candidate coverage as a key bottleneck.

By Cheng Zhang, Xuanning Cao, Zhicheng Zheng, Hui Xiong