\n\n## Introduction\n\nIn the field of artificial intelligence, language models have become increasingly powerful, but suffer from a insidious problem: context loss. This phenomenon, known as 'context rot', causes these models to struggle with long and complex conversations. In this article, we will explore a new solution called GAM (general agentic memory), which promises to solve this problem.
\n\n## Technical Details\n\nGAM is a dual-agent system that uses two primary components: the 'memorizer' and the 'researcher'. The memorizer captures every conversation in full, while the researcher executes a strategic search to find the required information.
\nThe system employs an approach called JIT (Just-In-Time) compilation, which involves creating a personalized context only when necessary.
\nThis solution revolutionizes how AI models manage memory and retrieve information.
\n\n## Practical Implications\n\nThe GAM system offers a practical solution for AI developers who want to create more powerful and reliable artificial intelligence models.
\nGAM can be used to improve the ability of AI to remember long conversations, and provide more accurate and personalized responses.
\nIn addition, GAM can help reduce the cost of AI models, as it does not require creating large and complex contexts.
\n\n## Conclusion\n\nIn conclusion, GAM represents an innovative solution for the 'context rot' problem in artificial intelligence. The dual-agent system and JIT compilation approach offer a practical and reliable solution for AI developers who want to create more powerful and complex models.
\nWe hope that this article has provided a clear idea of how GAM works and its practical implications.