Very cool paper on memory compression for agents. If you run many agent sandboxes in parallel for RL or evals, memory becomes highly redundant. This work suggests that compressing against that redundancy cuts sandbox memory by up to 8.7x. Memory is becoming the capacity limit for high-fanout agent workloads. One task can spawn many concurrent sandboxes, and they all start from the same template and run related trajectories. HKUST researchers measured 76 to 96% of pages with…