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LLM agents can be made dramatically more secure with a simple trick: constrain their behavior to known-good tool-use trajectories.
Federated learning can be sped up by 74% without sacrificing security, thanks to a novel hardware-assisted approach that cleverly decouples cryptographic setup from the active training phase.
