Towards In-Parameter Memory Augmentation for Large Language Models
Recently Large Language Models (LLMs) and LLM-based agents increasingly need to incorporate knowledge acquired after pretraining, e.g., domain facts, user preferences, documents, and interaction experience. In-context learning (ICL) and ICL-based agent harness remain flexible, but they consume context capacity and incur repeated discretized encoding cost that grows with context length. \textbf{In-parameter memory} offers a complementary substrate: reusable memory information is represented in model parameters, adapters, or other parameter-like objects that are composed into the forward pass at inference time. This survey focuses on methods that augment LLMs with such parametric memory at ...
| Authors | Haoyu Huang, Zhongwei Xie, Jiaxin Bai et al. |
|---|---|
| Primary category | cs.CL |
| Published | 6 October 2026 |
| Last updated at source | 6 October 2026 |
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