Memory-Augmented Language Models

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29 papers in the last four weeks, up 142% on the four weeks before. 0.3% of all new papers.

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Memory Decoder at Scale: A Pretrained, Parametric Long-Term Memory
Jul 30, 2026Rubin Wei, Jiaqi Cao, Jiarui Wang +4Language Model Pretraining Memory-Augmented Language Models
LUMIA Lab, School of Artificial Intelligence, Shanghai Jiao Tong University · Shanghai Artificial Intelligence Laboratory · Electronic Engineering, Tsinghua University
Subtract, Transport, or Replay? Auditable Deletion from Language-Model Memory
Jul 30, 2026Vishwajith RameshMemory-Augmented Language Models LLM Auditing
Vy Labs, Inc.
InferScale: GPU-Native KV Injection for Personalized LLM Serving
Jul 29, 2026Peter Li, Prashant PandeyLLM Serving Memory-Augmented Language Models
Northeastern University Boston, Massachusetts, USA
Metis: Memory Foundation Model
Jul 29, 2026Zeyu Zhang, Ziliang Guo, Yihang Sun +14Agent Memory Memory-Augmented Language Models
MemTensor (Shanghai) Technology Co., Ltd. · Shanghai Jiao Tong University · National University of Singapore +2
Voice Memory for Agentic Speech Recognition
Jul 29, 2026Chao-Han Huck Yang, Zih-Ching Chen, Piotr Zelasko +3Memory-Augmented Language Models Automatic Speech Recognition
NVIDIA
MemSFT: Mitigating Alignment Tax with an External Parametric Memory
Jul 28, 2026Jiarui Wang, Xiang Shi, Jiaqi Cao +8LLM Alignment Memory-Augmented Language Models
LUMIA Lab, School of Artificial Intelligence, Shanghai Jiao Tong University · Shanghai AI Laboratory · School of Computer Science, Shanghai Jiao Tong University +1
Memory for Large Language Models
Jul 28, 2026Sining Zhoubian, Dan Zhang, Evgeny Kharlamov +1Memory-Augmented Language Models LLM Memory
Tsinghua University · National University of Singapore · Bosch AI
Raven: High-Recall Sequence Modeling with Sparse Memory Routing
Jul 28, 2026Arshia Afzal, Aviv Bick, Eric P. Xing +2Memory-Augmented Language Models Long-Context Language Modeling
EPFL · Carnegie Mellon University · MBZUAI +1
CALMRec: Causally Aligned Language Memory for Long-Horizon Recommendation
Jul 26, 2026Gengyu ZhanMemory-Augmented Language Models Recommender Systems
Shenzhen University
MaSRead: Content-Addressed Reading of Replicated Latent Stores
Jul 21, 2026Carlos Baquero, Luís Brito, João ResendeMemory-Augmented Language Models
FEUP & INESC TEC, Universidade do Porto, Porto, Portugal · ESTG, Instituto Politécnico de Viana do Castelo, Viana do Castelo, Portugal · DCC, FCUP, Universidade do Porto, Porto, Portugal
SmartRAG: Native Graph-Based RAG for Mobile Device
Jul 16, 2026Zhihan Jiang, Meng Li, Shenghao Liu +5Named Entity Recognition On-Device Language Model Inference
1Nanjing University · 2HUST · 3Southeast University
Smarter and Cheaper at Once: Byte-Exact KV-Cache Grafting Turns a Frozen Small Model into a Verified-Knowledge Flywheel
Jul 15, 2026Sietse SchelpeMemory-Augmented Language Models Knowledge Augmentation for Language Models
Corbenic AI
PReM: Learning What to Preserve and When to Refresh for Context Compression
Jul 15, 2026Bohan Yu, Lei Shen, Chenxi Zhou +5Memory-Augmented Language Models Long-Context Language Model Inference
Alibaba Group
Extending LLM Context via Associative Recurrent Memory
Jul 13, 2026Gleb Kuzmin, Ivan Rodkin, Aydar Bulatov +8Memory-Augmented Language Models Long-Context Language Modeling
1FusionBrain Lab 2MBZUAI 3Cognitive AI Systems Lab 4RUDN · 2MBZUAI · 6MIRAI +5
Context by Distinct Information: An Auditable Dirichlet-Process Working Memory for Long, Redundant Context Streams
Jul 11, 2026Siddharth Pal, Viktoria RojkovaMemory-Augmented Language Models Long-Context Modeling
Creativity, honesty and designed forgetting emerge in small hyperbolic language models
Jul 10, 2026Kwan Soo Shin, In Seok Kang, Yunkyung MinLLM Sycophancy Memory-Augmented Language Models
PolymathMinds Lab, Seoul, Republic of Korea. · Department of Chemical Engineering, POSTECH, Pohang, and aSSIST University, Seoul, Republic of Korea. · Korean Educational Development Institute, Jincheon, Republic of Korea.
Co-LMLM: Continuous-Query Limited Memory Language Models
Jul 8, 2026Yair Feldman, Linxi Zhao, Nathan Godey +5Language Model Pretraining Memory-Augmented Language Models
Department of Computer Science Cornell University
TF-Engram: A Train-Free Engram with SSD-Backed Memory for Large Language Models
Jul 8, 2026Yutang Ma, Kecheng Huang, Xikun Jiang +1Memory-Augmented Language Models Knowledge Augmentation for Language Models
The Chinese University of Hong Kong · Beijing Institute of Technology, Zhuhai
Sparse Delta Memory: Scaling the State of Linear RNNs through Sparsity
Jul 8, 2026Loïc Cabannes, Pierre-Emmanuel Mazaré, Gergely Szilvasy +6Long-Context Retrieval Memory-Augmented Language Models
Meta FAIR · Inria Paris & ENS-PSL University · University of Tübingen
MILES: Modular Instruction Memory with Learnable Selection for Self-Improving LLM Reasoning
Jul 8, 2026Ruilin Tong, Dong GongContinual Learning for LLMs Memory-Augmented Language Models
University of New South Wales (UNSW Sydney)
Do You Remember? Toward Memory-Centric Multimodal AI
Jul 7, 2026Xuguang Yu, Weigang Zheng, Minyue YuMemory-Augmented VLMs Multimodal Memory
From Application-Layer Simulation to Native Meta-Architecture: Structural Tension as an Endogenous Driver for Heterogeneous AI Evolution
Jul 7, 2026Heting MaoLLM Alignment Memory-Augmented Language Models
Shanghai Lixin University of Accounting and Finance
MemDefrag: Latent Memory Defragmentation for Large Language Models
Jul 7, 2026Ruiyi Yan, Zhuoyuan Mao, Yiwen GuoContinual Learning for LLMs Memory-Augmented Language Models
Kyoto University · LIGHTSPEED · Independent Researcher
Akashic: A Low-Overhead LLM Inference Service with MemAttention
Jul 7, 2026Yang Liu, Zhaokai Luo, Huayi Jin +7LLM Serving Memory-Augmented Language Models
Xiaohongshu Inc., China · ShangHai JiaoTong University · Peking University
Parametric Memory Decoding for Zero-Shot Routing in LoRA-Based External Parametric Memory
Jul 5, 2026Fengxian Ji, Zhuohan Xie, Jingpu Yang +3Zero-Shot Learning Memory-Augmented Language Models
1MBZUAI, United Arab Emirates · Institute of Automation, Chinese Academy of Sciences, China
PraMem: Practice-derived Experiential Memory for Long-horizon Behavior Prediction
Jul 3, 2026Zhuoqun Li, Boxi Cao, Jiawei Chen +11Memory-Augmented Language Models Next-Action Prediction
Chinese Information Processing Laboratory, Institute of Software, Chinese Academy of Sciences · University of Chinese Academy of Sciences · Fudan University +1
DRIFTLENS: Measuring Memory-Induced Reasoning Drift in Personalized Language Models
Jul 2, 2026Xi Fang, Weijie Xu, Yingqiang Ge +3LLM Personalization Memory-Augmented Language Models
1Amazon
A Hippocampus for Linear Attention: An Exact Memory for What the Recurrent State Forgets
Jul 2, 2026Wanyun CuiLong-Context Retrieval Memory-Augmented Language Models
Shanghai University of Finance and Economics
InduceKV: Fixed-Footprint Continual Adaptation of Multimodal LLMs via Inducing KV Memories
Jul 2, 2026Qianyu Chen, Ziteng Feng, Canran Xiao +1Continual Learning for LLMs Memory-Efficient Fine-Tuning
1Nanyang Technological University · University of Science and Technology of China · 3Shenzhen Campus of Sun Yat-sen University
Auditing Forgetting in Limited Memory Language Models
Jul 1, 2026Arya Raeesi, Hanna RoedMemory-Augmented Language Models LLM Auditing
University of California, Berkeley, Berkeley, California, United States of America.

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Memory Decoder at Scale: A Pretrained, Parametric Long-Term Memory

Rubin Wei, Jiaqi Cao, Jiarui Wang, Junming Zhang+3

LUMIA Lab, School of Artificial Intelligence, Shanghai Jiao Tong University · Shanghai Artificial Intelligence Laboratory · Electronic Engineering, Tsinghua University

Language Model Pretraining Memory-Augmented Language Models Persistent Memory for Language Models Language Model Scaling Language Modeling

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Decoder-only language models entangle long-term memory and reasoning in a single parameter set, making it difficult to scale memory capacity independently. Memory Decoder introduces a parametric long-term memory module but only studies it at a relatively small scale. In this work, we present Memory Decoder at Scale, scaling memory models up to 6.9B parameters and pretraining them on 300B tokens. At this data scale, the combined cost of indexing and search makes a standard Faiss pipeline infeasible. We address this bottleneck with a distributed pipeline for Faiss indexing and retrieval, together with sparse, batch-wise loading of kNN distributions. Across model scales, we find that allocating more parameters to memory yields a better parameter-performance tradeoff than scaling the base model alone. On 17 benchmarks, pairing a 6.9B general memory with Pythia-410M raises its average score from 29.86 to 37.34, surpassing Pythia-12B (37.24) with 39% fewer total parameters. For Qwen3 Base models ranging from 0.6B to 14B, 1.7B domain memories improve the average score across the three domains by more than 9 points at every scale. Overall, our results demonstrate that independently scaling pretrained memory offers a more parameter efficient path to improving language model performance.

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