# Structured Memory Mechanisms for Stable Context Representation in Large Language Models

arXiv: https://arxiv.org/abs/2505.22921 · HTML: https://mlsanity.com/paper/2505.22921/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 20 references that are in the map.
- First posted on arXiv 28 May 2025.

## Builds on (most cited first, 13 of 13)

- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Chain of Agents: Large Language Models Collaborating on Long-Context Tasks](https://mlsanity.com/paper/2406.02818/) (2024)
- [LooGLE: Can Long-Context Language Models Understand Long Contexts?](https://mlsanity.com/paper/2311.04939/) (2023)
- [A Comprehensive Survey on Long Context Language Modeling](https://mlsanity.com/paper/2503.17407/) (2025)
- [Adapting GPT, GPT-2 and BERT Language Models for Speech Recognition](https://mlsanity.com/paper/2108.07789/) (2021)
- [Long Context is Not Long at All: A Prospector of Long-Dependency Data for Large Language Models](https://mlsanity.com/paper/2405.17915/) (2024)
- [LADM: Long-context Training Data Selection with Attention-based Dependency Measurement for LLMs](https://mlsanity.com/paper/2503.02502/) (2025)
- [LongAttn: Selecting Long-context Training Data via Token-level Attention](https://mlsanity.com/paper/2502.16860/) (2025)
- [Context-Guided Dynamic Retrieval for Improving Generation Quality in RAG Models](https://mlsanity.com/paper/2504.19436/) (2025)
- [GPT vs RETRO: Exploring the Intersection of Retrieval and Parameter-Efficient Fine-Tuning](https://mlsanity.com/paper/2407.04528/) (2024)
- [Semantic and Contextual Modeling for Malicious Comment Detection with BERT-BiLSTM](https://mlsanity.com/paper/2503.11084/) (2025)
- [Graph-Based Spectral Decomposition for Parameter Coordination in Language Model Fine-Tuning](https://mlsanity.com/paper/2504.19583/) (2025)
- [Towards Robust Few-Shot Text Classification Using Transformer Architectures and Dual Loss Strategies](https://mlsanity.com/paper/2505.06145/) (2025)

## Built on by (most cited first, 9 of 9)

- [A Survey of Context Engineering for Large Language Models](https://mlsanity.com/paper/2507.13334/) (2025)
- [Trustworthy Summarization via Uncertainty Quantification and Risk Awareness in Large Language Models](https://mlsanity.com/paper/2510.01231/) (2025)
- [Structure-Learnable Adapter Fine-Tuning for Parameter-Efficient Large Language Models](https://mlsanity.com/paper/2509.03057/) (2025)
- [Dynamic Prompt Fusion for Multi-Task and Cross-Domain Adaptation in LLMs](https://mlsanity.com/paper/2509.18113/) (2025)
- [Collaborative Distillation Strategies for Parameter-Efficient Language Model Deployment](https://mlsanity.com/paper/2507.15198/) (2025)
- [Transferable Modeling Strategies for Low-Resource LLM Tasks: A Prompt and Alignment-Based Approach](https://mlsanity.com/paper/2507.00601/) (2025)
- [Controllable Abstraction in Summary Generation for Large Language Models via Prompt Engineering](https://mlsanity.com/paper/2510.15436/) (2025)
- [Topology-Aware Graph Reinforcement Learning for Dynamic Routing in Cloud Networks](https://mlsanity.com/paper/2509.04973/) (2025)
- [When Corrective Hints Hurt: Prompt Design in Reasoner-Guided Repair of LLM Overcaution on Entailed Negations under OWL~2~DL](https://mlsanity.com/paper/2604.23398/) (2026)

## Abstract (arXiv)

This paper addresses the limitations of large language models in understanding long-term context. It proposes a model architecture equipped with a long-term memory mechanism to improve the retention and retrieval of semantic information across paragraphs and dialogue turns. The model integrates explicit memory units, gated writing mechanisms, and attention-based reading modules. A forgetting function is introduced to enable dynamic updates of memory content, enhancing the model's ability to manage historical information. To further improve the effectiveness of memory operations, the study designs a joint training objective. This combines the main task loss with constraints on memory writing and forgetting. It guides the model to learn better memory strategies during task execution. Systematic evaluation across multiple subtasks shows that the model achieves clear advantages in text generation consistency, stability in multi-turn question answering, and accuracy in cross-context reasoning. In particular, the model demonstrates strong semantic retention and contextual coherence in long-text tasks and complex question answering scenarios. It effectively mitigates the context loss and semantic drift problems commonly faced by traditional language models when handling long-term dependencies. The experiments also include analysis of different memory structures, capacity sizes, and control strategies. These results further confirm the critical role of memory mechanisms in language understanding. They demonstrate the feasibility and effectiveness of the proposed approach in both architectural design and performance outcomes.