Structured Memory Mechanisms for Stable Context Representation in Large Language Models

2025-05-28

AI Summary

To address semantic loss, drift, and context decay in large language models (LLMs) during long-context understanding, this paper proposes an explicit structured long-term memory architecture. Methodologically, it introduces a novel memory unit integrating gated writing, attention-driven reading, and a learnable dynamic forgetting function, coupled with a multi-objective training framework that jointly optimizes task performance and memory policy. Crucially, the approach incurs no additional inference latency. Experiments demonstrate substantial improvements in long-text generation coherence, multi-turn dialogue stability, and cross-paragraph reasoning accuracy. The memory mechanism is empirically validated for its effectiveness in semantic persistence and precise retrieval, exhibiting strong generalization across diverse long-context tasks. This work establishes a new paradigm for modeling extended contextual dependencies in LLMs.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Search and Optimization: Learning to Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Large language models for search
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

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.

Problem

• Enhancing long-term context understanding in large language models
• Improving semantic retention and retrieval across paragraphs and dialogues
• Mitigating context loss and semantic drift in long-text tasks

Innovation

• Long-term memory mechanism for context retention
• Gated writing and attention-based reading modules
• Joint training objective for memory optimization