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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>AI agents forget. Every time a coding assistant loses track of a debugging thread, or a data analysis agent re-ingests the same context it already processed, the team pays in latency, token c [...]
<p>Redis built its name as the caching layer that kept web applications from collapsing under load. The problem it is targeting now has the same structure but is harder to solve: production AI a [...]
<p>Something shifted in enterprise RAG in Q1 2026. VB Pulse data spanning January through March tells a consistent story: the market stopped adding retrieval layers and started fixing the ones i [...]