General Agentic Memory tackles context rot and outperforms RAG in memory benchmarks
PositiveArtificial Intelligence

- A Chinese research team has introduced a new memory architecture for AI agents called General Agentic Memory (GAM), which aims to reduce information loss during prolonged interactions by integrating compression techniques with deep research methodologies.
- This development is significant as it addresses the challenges of context rot, which can hinder the performance of AI systems, thereby enhancing their reliability and effectiveness in maintaining coherent dialogues over extended periods.
- The introduction of GAM comes at a time when the AI industry is grappling with issues of reliability and memory retention, as seen in recent benchmarks highlighting the limitations of existing models, underscoring the need for innovative solutions to improve AI performance.
— via World Pulse Now AI Editorial System







