arXiv 论文提出规模条件化评测协议，检验智能体记忆证据可用性边界

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Abstract:Memory-agent evaluations report fixed-snapshot accuracy or retrieval quality, but these scores do not show whether evidence remains usable as irrelevant sessions (sessions not annotated as task-relevant evidence for the query) accumulate. We present a scale-conditioned evaluation protocol for agent memory under evidence-preserving growth: for each query, task evidence is held fixed while irrelevant sessions are added. The protocol logs agent-memory trajectories and reports four diagnostics: budget-compliant reliability, tail memory-call burden, failure-regime decomposition, and the usable-scale boundary where reliability falls below the target. Applied to LongMemEval and LoCoMo across flat, planar, and hierarchical memory interfaces, the protocol shows reliability loss is not a single phenomenon. On LongMemEval, HippoRAG stays within the two-call budget but loses 16-20 percentage points in budget-compliant reliability as irrelevant sessions are added; LiCoMemory's observed failures depend strongly on the agent, with Qwen3-8B exceeding the budget while Qwen3-32B and Qwen3-235B remain reliable in the tested range. The result supports a framework for making scalable-memory claims conditional on agent, interface, scale range, and interaction budget. Code and evaluation artifacts: this https URL

Comments:	Accepted at NeurIPS 2026, Evaluations and Datasets Track (poster). Updated to the camera-ready version. 25 pages, including references and appendices
Subjects:	Artificial Intelligence (cs.AI)
ACM classes:	I.2.7; H.3.3
Cite as:	arXiv:2605.07313 [cs.AI]
	(or arXiv:2605.07313v2 [cs.AI] for this version)
	https://doi.org/10.48550/arXiv.2605.07313 arXiv-issued DOI via DataCite

Submission history

From: Yiyi Lu [view email]
[v1] Fri, 8 May 2026 06:22:31 UTC (1,169 KB)
[v2] Thu, 29 Oct 2026 07:34:59 UTC (1,355 KB)