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This work introduces MAPLE (Memory-Augmented Planning with Language and Evolution), an agent for maintaining optimization problems through successive natural-language requests and introduces NLDO, a benchmark of 15 trajectories and 180 updates spanning selection, scheduling, rostering, routing, and cloud-resource placement.
Extracting attack chains from CTI reports can now be automated, achieving 19 out of 20 successful attack goal identifications through innovative modeling of attack steps.
