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arXiv:cs.AI· Rongtian Ye·· 17 小时前

论文审计 PIT 语言智能体的未来记忆使用:Qwen 与 DeepSeek 均能回忆未来事实

Auditing Future-Memory Use in Point-in-Time Language Agents

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针对 48 个历史公司对比任务的研究发现,Qwen 与 DeepSeek 都能回忆训练截止后的事实,但均未支持预先设定的"偏向记忆更牢公司"的搜索分配假设。Qwen 在 28 个回忆不均衡任务上的四个分配估计均为负,区间跨零;DeepSeek 在前瞻性复制中于 20 个任务上呈现反向模式,四项指标区间均低于零,经 max-T 校正的符号翻转 p 值全部小于 .05。

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Abstract:Point-in-time (PIT) agents can recall post-cutoff facts even when their external evidence respects a historical cutoff. We examine whether this access predicts evidence gathering in 48 historical company-comparison tasks. Independent closed-book revenue probes identify the better-remembered company for each model on tasks with unequal recall, fixing the predicted direction before agent runs. We then compare paired runs with real or anonymized company identities while keeping external evidence PIT-valid. Both Qwen and DeepSeek recalled future facts, but neither supported the prespecified shift toward the better-remembered company. Qwen's four allocation estimates on 28 unequal-recall tasks were negative, with descriptive intervals spanning zero. A prospective DeepSeek replication showed an inverse pattern across all four metrics on 20 such tasks, with intervals below zero. These estimates remained negative after each company, pair, and future-fact omission, and all four post-hoc max-T-adjusted sign-flip p-values were below .05. In a prospectively specified follow-up, explicit future facts produced small estimated shifts away from the supplied-fact company, with intervals spanning zero, while final choices shifted toward it. The mechanism behind the natural inverse pattern remains unresolved. These findings support separate assessments of memory accessibility, search allocation, and final outputs alongside cutoff checks on external evidence.
Comments: 18 pages
Subjects: Artificial Intelligence (cs.AI)
Cite as: arXiv:2610.18602 [cs.AI]
  (or arXiv:2610.18602v1 [cs.AI] for this version)
  https://doi.org/10.48550/arXiv.2610.18602

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Rongtian Ye [view email]
[v1] Tue, 13 Oct 2026 15:56:58 UTC (116 KB)

来源:arXiv:cs.AI · arxiv.org