NGM: A Plug-and-Play Training-Free Memory Module for LLMs
The paper introduces N-gram Memory (NGM), a training-free memory module designed for large language models (LLMs). NGM utilizes a Causal N-Gram Encoder and a Cosine-Gated Memory Injector to enhance knowledge retrieval without the need for additional training. Evaluation results show that NGM improves performance across various benchmarks, particularly in code generation and knowledge-intensive tasks.
- ▪NGM is a plug-and-play memory module that does not require training.
- ▪It combines a Causal N-Gram Encoder with a Cosine-Gated Memory Injector for efficient knowledge retrieval.
- ▪The module shows performance improvements of 0.5 to 1.2 points on average across eight benchmarks.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.16893 |
| Publication time | Tue, 19 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-19T04:04:57.272Z |
| Last seen | 2026-05-19T04:04:57.272Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
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| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | WqD_WOCE6aRZ |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Artificial Intelligence arXiv:2605.16893 (cs) [Submitted on 16 May 2026] Title:NGM: A Plug-and-Play Training-Free Memory Module for LLMs Authors:Yuwen Qu, Wenhui Dong, Chenyang Si, Caifeng Shan View a PDF of the paper titled NGM: A Plug-and-Play Training-Free Memory Module for LLMs, by Yuwen Qu and 3 other authors View PDF HTML (experimental) Abstract:Recent studies introduce conditional memory modules that decouple knowledge storage from neural computation, enabling more direct knowledge access. Compared to MoE, which relies on dynamic computation paths, explicit lookup provides a more efficient knowledge retrieval mechanism. However, these approaches still depend on learned memory embeddings, requiring additional training and limiting flexibility.
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