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A research team from NYU, Columbia, Princeton, University of Maryland, Harvard and Lawrence Livermore National Laboratory published a paper on a method that compresses LLM context input by 16x without degrading model accuracy. The approach addresses the computational bottleneck of growing context windows from agent runs, retrieved documents, and conversation history.
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