Observational memory rethinks agent context: dramatic cost cuts and stronger long-term recall
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Nvidia researchers introduced Dynamic Memory Sparsification (DMS), a retrofit that compresses the KV cache so large language models can reason farther with far less GPU memory. In benchmarks DMS reduced cache footprint by as much as eightfold, raised throughput up to five times for some models, and improved task accuracy under fixed memory budgets.


