The paper introduces Dual-Path Parametric Memory (DPPM), a method for long‑term personalization of language models that leverages interaction history without requiring it in the inference context. DPPM uses two complementary paths: an Evidence path that pools historical representations to retain earlier evidence, and a Delta path that sequentially updates an associative state to capture changes. By fusing these outputs into history‑conditioned LoRA adapters, DPPM achieves superior performance on PersonaMem‑v2 (54.22%) and PrefEval (86.79%) compared to existing baselines.
By Yuhao Chen, Shuochen Liu, Jiayao Shi, Jian Hong, Chen Cheng, Xinyun Ding, Tao Wang, Ya Li, Quan Liu, Tong Xu
arXiv:2607. 22556v1 Announce Type: new Abstract: Continual learning (CL) is essential for small language models (SLMs) to adapt to evolving real-world needs in resource-constrained deployments.
By Dong Li, Yanchi Liu, Xujiang Zhao, Wei Cheng, Zhengzhang Chen, Xintao Wu, Zhong Chen, Chen Zhao, Haifeng Chen
arXiv:2609.00184v1 Announce Type: cross
Abstract: Large language models (LLMs) rely on static pretraining corpora, causing their knowledge to become outdated over time. Existing approaches for evalua...
By Jonathan Zheng, Zirui Shao, Alan Ritter, Wei Xu
arXiv:2606. 27634v1 Announce Type: new Abstract: Small Language Models (SLMs) are increasingly being considered for deployment on edge devices such as laptops, enabling private, low-latency, and locally personalized applications.
By Thomas S. Paula, Lucas S. Kupssinsk\"u, Rodrigo C. Barros
arXiv:2601.13734v2 Announce Type: replace-cross
Abstract: In this paper, we propose active recap learning (ARL), a framework for enhancing large language model (LLM) in understanding long contexts. A...
By Chenyu Hui
Large language models (LLMs) rely on static pretraining corpora, causing their knowledge to become outdated over time. Existing approaches for evaluating knowledge edits either suffer from rapid conta...
arXiv:2608. 03463v1 Announce Type: new Abstract: Long-term memory is essential for LLM-based agents to sustain interactions and reliably leverage distant history.
By Yuxin Liao, Le Wu, Min Hou, Hao Liu, Han Wu, Zishu Wang
arXiv:2610.08630v1 Announce Type: new
Abstract: Recently Large Language Models (LLMs) and LLM-based agents increasingly need to incorporate knowledge acquired after pretraining, e.g., domain facts, u...
By Haoyu Huang, Zhongwei Xie, Jiaxin Bai, Yisen Gao, Hong Ting Tsang, Wuganjing Song, Huihao Jing, Yufei Li, Yangqiu Song
arXiv:2601.03199v2 Announce Type: replace-cross
Abstract: Diffusion language models (DLMs) have shown strong potential for general natural language tasks with in-context examples. Existing In-Context...
By Yang Li, Han Meng, Chenan Wang, Zhenyu Bi, Xuan Wang, Haipeng Chen
arXiv:2603. 12658v2 Announce Type: replace-cross Abstract: Continual learning (CL) has emerged as a pivotal paradigm to enable large language models (LLMs) to dynamically adapt to evolving knowledge and sequential tasks while mitigating catastrophic forgetting, a critical limitation of the static pre-training paradigm inherent to modern LLMs.
By Hongyang Chen, Zhongwu Sun, Hongfei Ye, Kunchi Li, Xuemin Lin
arXiv:2608.30647v1 Announce Type: cross
Abstract: Language models can answer from precomputed memory, a model's saved reading of a body of material, reused across requests instead of read again at ea...
By Asa Shepard
arXiv:2609.37930v1 Announce Type: cross
Abstract: Persistent textual memory allows language models to carry information across long interactions, but learning what to remember is fundamentally a cred...
By Jiaming Tang, Mingyan Liu, Armin Sarabi