AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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SLDBench v2 is introduced with two extensions, and active scaling law discovery is introduced: an agent chooses which experiments to run, pays each one's recorded cost, and revises its law as outcomes arrive, and is scored on the same hidden extrapolation targets as in the passive setting.
This work presents SceneActBench, a benchmark for visually conditioned action across five 3D tasks under a unified agent-environment loop, and evaluates each final output against hidden ground truth with task-specific geometric metrics.
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