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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A Heterogeneous Temporal Memory Governance Framework for Long-Term LLM Persona Consistency
May 14, 2026Zhao Yang, Wang Huan, Li Yingshuo +2Memory-Augmented Language Models Persona Consistency
Changchun Kelaile Technology Co., Ltd, Qinglong Road, Luyuan District, Changchun, 130000, Jilin, China. · School of Electronic Information, Zhongshan Institute, University of Electronic Science and Technology of China, No. 1 Xueyuan Road,2026 Zhongshan, 528402, Guangdong, China.
Test-Time Learning with an Evolving Library
May 14, 2026Weijia Xu, Alessandro Sordoni, Chandan Singh +4Memory-Augmented Language Models Continual Test-Time Adaptation
Microsoft Research
Stateful Reasoning via Insight Replay
May 14, 2026Bin Lei, Caiwen Ding, Jiachen Yang +2Memory-Augmented Language Models CoT Reasoning
University of Minnesota · 2Simular AI
Phasor Memory Networks: Stable Backpropagation Through Time for Scalable Explicit Memory
May 13, 2026Sungwoo Goo, Hwi-yeol Yun, Sangkeun JungMemory-Augmented Neural Networks Memory-Augmented Language Models
College of Pharmacy Chungnam National University Daejeon, South Korea · Department of Computer Science & Engineering Chungnam National University Daejeon, South Korea
Continual Fine-Tuning of Large Language Models via Program Memory
May 13, 2026Hung Le, Svetha VenkateshContinual Learning for LLMs Memory-Augmented Language Models
Deakin Applied AI Initiative, Deakin University, Australia
$δ$ -mem: Efficient Online Memory for Large Language Models
May 12, 2026Jingdi Lei, Di Zhang, Junxian Li +7Memory-Augmented Language Models Memorization in Language Models
Nanyang Technological University · Mind Lab · Fudan University +3
Automated Reformulation of Robust Optimization via Memory-Augmented Large Language Models
May 12, 2026Jinbiao Chen, Shuang Jin, Guoyun Zhang +3Memory-Augmented Language Models Large Language Model-Guided Optimization
Department of Industrial Systems Engineering and Management, National University of Singapore · Department of Data and Systems Engineering, The University of Hong Kong · Institute of Operations Research and Analytics, National University of Singapore +1
Mela: Test-Time Memory Consolidation based on Transformation Hypothesis
May 11, 2026Lungchuan ChenMemory-Augmented Neural Networks Memory-Augmented Language Models
MusubiAI
GRC: Unifying Reasoning-Driven Generation, Retrieval and Compression
May 9, 2026Zhongtao Miao, Qiyu Wu, Yoshimasa TsuruokaRetrieval-Augmented Generation Memory-Augmented Language Models
The University of Tokyo · Tsuruoka Lab. · Independent Researcher
VORT: Adaptive Power-Law Memory for NLP Transformers
May 9, 2026Nabil MlaikiMemory-Augmented Language Models Transformer
Department of Mathematics and Sciences, Prince Sultan University, Riyadh, Saudi Arabia
The Echo Amplifies the Knowledge: Somatic Marker Analogues in Language Models via Emotion Vector Re-Injection
May 9, 2026Jared GloverMemory-Augmented Language Models Causal Interventions in Language Models
CapSen Robotics
Grokers: Bottom-Up Inductive Comprehension and Write-Time Intelligence over Typed Knowledge Graphs
May 7, 2026Gregory MagarshakKG Construction Memory-Augmented Language Models
Qbix, Inc. & Intercoin, Inc. · IE University NYC · New York, NY, USA
Memory Inception: Latent-Space KV Cache Manipulation for Steering LLMs
May 7, 2026Andy Zeyi Liu, Michael Zhang, Ilana Greenberg +3Language Model Steering Memory-Augmented Language Models
Yale University · Princeton University · Jump Trading
TIDE: Every Layer Knows the Token Beneath the Context
May 7, 2026Ajay Jaiswal, Lauren Hannah, Han-Byul Kim +3Memory-Augmented Language Models Language Modeling
Apple
Contextual Memory-Enhanced Source Coding for Low-SNR Communications
May 6, 2026Ziqiong Wang, Rongpeng LiMemory-Augmented Language Models Autoregressive Language Modeling
College of Information Science and Electronic Engineering, Zhejiang University
Sparse Memory Finetuning as a Low-Forgetting Alternative to LoRA and Full Finetuning
May 4, 2026Prakhar Gupta, Garv Shah, Satyam Goyal +1Memory-Augmented Language Models LLM Fine-Tuning
University of Michigan
Evolving Token Communication with Parametric Memory Network
May 3, 2026Weixuan Chen, Qianqian YangTest-Time Adaptation Memory-Augmented Language Models
College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China
HoReN: Normalized Hopfield Retrieval for Large-Scale Sequential Model Editing
May 2, 2026Yuan Fang, Yi Xie, Xuming RanKnowledge Editing Memory-Augmented Language Models
1IXL Learning, Inc · 2Technical University of Munich · 3National University of Singapore
MedSynapse-V: Bridging Visual Perception and Clinical Intuition via Latent Memory Evolution
Apr 29, 2026Chunzheng Zhu, Jiaqi Zeng, Junyu Jiang +2Memory-Augmented Language Models Medical Image Analysis
Hunan University, Changsha, China
Learning from Execution: Self-Evolving Memory for Private-Library Code Generation
Apr 27, 2026Mofei Li, Taozhi Chen, Guowei Yang +1Memory-Augmented Language Models Knowledge Augmentation for Language Models
College of AI Tsinghua University Beijing, China · Fitten Tech Co., Ltd. Beijing, China · Emory University Atlanta, GA, USA
Graph Memory Transformer (GMT)
Apr 26, 2026Nicola Zanarini, Niccolò Ferrari, Evelina LammaMemory-Augmented Neural Networks Transformer FFNs
Bonfiglioli Engineering s.r.l. · Department of Engineering, University of Ferrara, Via Saragat 1, 44122 Ferrara, Italy · NAIS s.r.l., Via Maria Callas 4, 40131 Bologna, Italy
Evolve: A Persistent Knowledge Lifecycle for Small Language Models
Apr 25, 2026Dikran HovagimianRetrieval-Augmented Generation Memory-Augmented Language Models
Independent Researcher
Beyond N-gram: Data-Aware X-GRAM Extraction for Efficient Embedding Parameter Scaling
Apr 23, 2026Yilong Chen, Yanxi Xie, Zitian Gao +10Memory-Augmented Language Models Language Model Scaling
Institute of Information Engineering, Chinese Academy of Sciences · School of Cyber Security, University of Chinese Academy of Sciences · IQuest Research +1
Automatic Ontology Construction Using LLMs as an External Layer of Memory, Verification, and Planning for Hybrid Intelligent Systems
Apr 22, 2026Pavel Salovskii, Iuliia GorshkovaKG Construction Memory-Augmented Language Models
Partenit.io, San Francisco, CA, USA · Partenit.io San Francisco, CA, USA
SCM: Sleep-Consolidated Memory with Algorithmic Forgetting for Large Language Models
Apr 22, 2026Saish Sachin ShindeMemory-Augmented Language Models Memorization in Language Models
Clyrai IP Studio
Knowledge Capsules: Structured Nonparametric Memory Units for LLMs
Apr 22, 2026Bin Ju, Shenfeng Weng, Danying Zhou +2Memory-Augmented Language Models Knowledge Augmentation for Language Models
Zhejiang Angel Medical AI Technology Co., Ltd., Hangzhou, China · Miti AI Technology Co., Ltd., Hangzhou, China · China-Singapore Belt and Road Joint Laboratory on Translational Infection Biology for Diagnostics and Therapies, State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital
Absorber LLM: Harnessing Causal Synchronization for Test-Time Training
Apr 22, 2026Zhixin Zhang, Shabo Zhang, Chengcan Wu +2Memory-Augmented Language Models Long-Context Language Model Inference
1Peking University.
A Control Architecture for Training-Free Memory Use
Apr 20, 2026Yanzhen Lu, Muchen Jiang, Zhicheng Qian +1Memory-Augmented Language Models Agent Memory Management
Shanghai Qizhi Institute · Nanjing Whale Cloud · Southeast University +1
AnchorMem: Anchored Facts with Associative Contexts for Building Memory in Large Language Models
Apr 19, 2026Zhanyu Shen, Sijie Cheng, Zhicheng Guo +3Memory-Augmented Language Models Memorization in Language Models
College of Computer Science and Software Engineering, Shenzhen University · Tsinghua University
The Continuity Layer: Why Intelligence Needs an Architecture for What It Carries Forward
Apr 19, 2026Samuel Sameer TanguturiMemory-Augmented Language Models Persistent Memory for Language Models
Kenotic Labs

May 14, 2026, cs.AIJ/K move · Enter open · S save

A Heterogeneous Temporal Memory Governance Framework for Long-Term LLM Persona Consistency

Zhao Yang, Wang Huan, Li Yingshuo, Tu Haomiao+1

Changchun Kelaile Technology Co., Ltd, Qinglong Road, Luyuan District, Changchun, 130000, Jilin, China. · School of Electronic Information, Zhongshan Institute, University of Electronic Science and Technology of China, No. 1 Xueyuan Road,2026 Zhongshan, 528402, Guangdong, China.

Memory-Augmented Language Models Persona Consistency Persistent Memory for Language Models Long-Term Conversational Memory LLM Context Management

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Large language models often suffer from fact loss, timeline confusion, persona drift, and reduced stability during long-range interaction, especially under high-noise knowledge bases, context clearing, and cross-model transfer. To address these issues, we introduce ARPM, an external temporal memory governance framework for long-term dialogue. ARPM separates static knowledge memory from dynamic dialogue experience memory and combines vector retrieval, BM25, RRF fusion, dual-temporal reranking, chronological evidence reading, and a controlled analysis protocol for evidence verification and answer binding. Unlike approaches that encode persona consistency into model weights or rely only on long context, ARPM treats continuity as a traceable, auditable, and transferable governance problem. Using engineering logs, we conduct three experiments. First, in a 50-round question-answering setting, we compare signal-to-noise ratios of 1:5 and 1:200+, and distinguish CSV auto-judgment from manual review. Under 1:5, CSV recall accuracy is 54.0%, while manual review raises it to 100.0%. Under 1:200+, the values are 44.0% and 80.0%. These results show that automatic rules can underestimate recall after supporting evidence enters the prompt. Second, ablation results show that dialogue history retrieval is necessary for recent continuity: disabling it reduces strict accuracy from 100% to 66.7%, and disabling BM25 reduces it to 80.0%, indicating that pure semantic retrieval is insufficient for correction and tracing. Third, under a 5.1-million-character noise substrate, periodic context clearing, and multi-model handoff, ARPM maintains semantic continuity, boundary continuity, and persona consistency, while exposing limits caused by weak protocol compliance. These findings show that long-term persona consistency can be decomposed into governable components and evaluated in a white-box manner.

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