arXiv：cs.CL· Mingyuan Li, Guangsheng Yu, Xu Wang, Shaoxiong Ji·· 16 小时前
冻结记忆还不够：重新审视外部记忆作为提取对象
Frozen Memory Is Not Enough: Rethinking External Memory as Extraction
AI 导读
研究通过跨模型冻结记忆提取发现，在 Engram 式哈希记忆中，冻结的记忆表只有在与目标模型对齐的阅读器配合下才能发挥作用。在受控评测协议下，双层四分支阅读器将同模型与跨模型复用的差距几乎弥合，平均得分达 38.8。当提供方阅读器与目标接口直接兼容时，冻结产物无需目标侧训练即可产生可观效用，可选的阅读器适配还能进一步提升效果。
正文
Abstract:Methods for improving knowledge use in large language models typically fall into two regimes. Non-parametric retrieval offers flexible access to external knowledge, but adds retrieval latency, context overhead, and only shallow integration with the backbone. Parametric adaptation is efficient at inference time, but entangles knowledge with model weights and can be hard to update, audit, or transfer. Engram-style hashed memory occupies a middle regime: it stores learned information in an external, addressable table, yet consumes that table through a small learned reader. This raises a basic question: when such a memory is moved across backbones, what matters more, the frozen memory itself or the target-side reader? We study this question through cross-model frozen-memory extraction, in which a memory trained on a source model is frozen and attached to a different target model, with only a lightweight reader trained. Ablations show that learned memory content and correct addressing both matter, but the transferred table becomes useful only through a reader aligned to the target model. In downstream question answering tasks, a dual-layer, four-branch reader nearly closes the gap between same-model and cross-model reuse, achieving an average score of 38.8 under our controlled evaluation protocol. Moreover, when the provider reader is directly compatible with the target interface, the frozen artifact can provide substantial utility without target-side training, while optional reader adaptation yields further improvement. These results suggest that Engram can serve as a reusable external knowledge artifact, provided that the target has access to a compatible reader interface; target-side adaptation can further improve alignment when direct reader reuse is insufficient.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2608.17050 [cs.CL] |
| (or arXiv:2608.17050v4 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2608.17050 arXiv-issued DOI via DataCite |
Submission history
From: Mingyuan Li [view email]
[v1]
Mon, 17 Aug 2026 18:54:42 UTC (568 KB)
[v2]
Wed, 19 Aug 2026 07:30:09 UTC (568 KB)
[v3]
Wed, 30 Sep 2026 07:57:51 UTC (565 KB)
[v4]
Tue, 13 Oct 2026 18:08:29 UTC (1,035 KB)
来源：arXiv：cs.CL · arxiv.org