Persistent Memory for Language Models

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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
LANTERN: Layered Archival and Temporal Episodic Retrieval Network for Long-Context LLM Conversations
Apr 18, 2026Rahul SubramaniMemory-Augmented Language Models Persistent Memory for Language Models
Cisco Systems, Inc.
HeLa-Mem: Hebbian Learning and Associative Memory for LLM Agents
Apr 18, 2026Jinchang Zhu, Jindong Li, Cheng Zhang +2Memorization in Language Models Hebbian Learning
The Hong Kong University of Science and Technology (Guangzhou) · Jilin University · The Chinese University of Hong Kong
APEX-EM: Non-Parametric Online Learning for Autonomous Agents via Structured Procedural-Episodic Experience Replay
Mar 31, 2026Pratyay Banerjee, Masud Moshtaghi, Ankit ChadhaContinual Learning for LLM Agents Persistent Memory for Language Models
Amazon, AGI / Sunnyvale, USA
MemSifter: Offloading LLM Memory Retrieval via Outcome-Driven Proxy Reasoning
Mar 3, 2026Jiejun Tan, Zhicheng Dou, Liancheng Zhang +3Memory-Augmented Language Models Persistent Memory for Language Models
Gaoling School of Artificial Intelligence · Renmin University of China · Beijing, China
Understanding LoRA as Knowledge Memory: An Empirical Analysis
Mar 1, 2026Seungju Back, Dongwoo Lee, Naun Kang +4Continual Learning for LLMs Memory-Augmented Language Models
1KAIST · 2Samsung SDS · 3New York University
Learning to Remember: End-to-End Training of Memory Agents for Long-Context Reasoning
Feb 13, 2026Kehao Zhang, Shangtong Gui, Sheng Yang +2Persistent Memory for Language Models Long-Context Inference
1Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences (ICT/CAS) · University of Chinese Academy of Sciences, Beijing, China · 4Li Auto Inc. +1
MemBuilder: Reinforcing LLMs for Long-Term Memory Construction via Attributed Dense Rewards
Jan 9, 2026Zhiyu Shen, Ziming Wu, Fuming Lai +2Reinforcement Learning Memory-Augmented Language Models
School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China · Tencent Inc.
Beyond Static Summarization: Proactive Memory Extraction for LLM Agents
Jan 8, 2026Chengyuan Yang, Zequn Sun, Wei Wei +1LLM Agent Memory Persistent Memory for Language Models
State Key Laboratory for Novel Software Technology, Nanjing University, China · National Institute of Healthcare Data Science, Nanjing University, China
Continual Memorization of Factoids in Language Models
Nov 11, 2024Howard Chen, Jiayi Geng, Adithya Bhaskar +2Continual Learning for LLMs Memorization in Language Models
Princeton Language and Intelligence (PLI), Princeton University

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The Continuity Layer: Why Intelligence Needs an Architecture for What It Carries Forward

Samuel Sameer Tanguturi

Kenotic Labs

Memory-Augmented Language Models Persistent Memory for Language Models LLM Memory Long-Term Conversational Memory

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The most important architectural problem in AI is not the size of the model but the absence of a layer that carries forward what the model has come to understand. Sessions end. Context windows fill. Memory APIs return flat facts that the model has to reinterpret from scratch on every read. The result is intelligence that is powerful per session and amnesiac across time. This position paper argues that the layer which fixes this, the continuity layer, is the most consequential piece of infrastructure the field has not yet built, and that the engineering work to build it has begun in public. The formal evaluation framework for the property described here is the ATANT benchmark (arXiv:2604.06710), published separately with evaluation results on a 250-story corpus; a companion paper (arXiv:2604.10981) positions this framework against existing memory, long-context, and agentic-memory benchmarks. The paper defines continuity as a system property with seven required characteristics, distinct from memory and from retrieval; describes a storage primitive (Decomposed Trace Convergence Memory) whose write-time decomposition and read-time reconstruction produce that property; maps the engineering architecture to the theological pattern of kenosis and the symbolic pattern of Alpha and Omega, and argues this mapping is structural rather than metaphorical; proposes a four-layer development arc from external SDK to hardware node to long-horizon human infrastructure; examines why the physics limits now constraining the model layer make the continuity layer newly consequential; and argues that the governance architecture (privacy implemented as physics rather than policy, founder-controlled class shares on non-negotiable architectural commitments) is inseparable from the product itself.

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