Mental Model Management: An Operator-Based Framework for LLM Memory

2026-08-15

AI Summary

This study addresses the challenge of lacking compact and dynamically evolving conceptual representations in large language models (LLMs) by proposing the 3M framework. This approach models knowledge as compact mental models and introduces innovative operator mechanisms, including chunking, extractive retrieval, consistency verification, and evolution, to enable continuous knowledge integration and dynamic reorganization. The research effectively overcomes memory management bottlenecks in LLMs by achieving dynamic knowledge evolution while maintaining representational compactness. Consequently, this method significantly enhances the model's knowledge reasoning capabilities and establishes a novel paradigm for constructing intelligent systems equipped with adaptive memory.

Abstract

Large language models process large amounts of information but usually lack an explicit mechanism for maintaining compact and evolving conceptual representations. We introduce Mental Model Management (3M), a framework in which knowledge is represented as mental models consisting of compact chunks. Rather than accumulating text passages, 3M continuously integrates new information into an existing conceptual representation. A set of operators extracts knowledge, retrieves relevant models, adds and updates chunks, reorganizes representations, detects inconsistencies, and derives new knowledge. We describe the main 3M operators and illustrate each operation using Evolution Strategies as a running example.

Problem

• Large Language Models
• Memory
• Mental Model
• Conceptual Representation

Innovation

• Mental Model Management
• Operator-Based Framework
• Compact Knowledge Representation
• Dynamic Memory Integration
• LLM Memory