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A Chinese research team has developed a new memory architecture for AI agents. "GAM" is designed to minimize information loss during long interactions by combining compression with deep research.
The article General Agentic Memory tackles context rot and outperforms RAG in memory benchmarks appeared first on THE DECODER.
<p>For all their superhuman power, today’s AI models suffer from a surprisingly human flaw: They forget. Give an AI assistant a sprawling conversation, a multi-step reasoning task or a project [...]
<p>A new technique developed by researchers at Shanghai Jiao Tong University and other institutions enables large language model agents to learn new skills without the need for expensive fine-tu [...]
<p>By now, enterprises understand that retrieval augmented generation (RAG) allows applications and agents to find the best, most grounded information for queries. However, typical RAG setups co [...]