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.
We study environment scaling as a way to increase agent-benchmark difficulty while holding core task objectives fixed. Our framework, Recursive Environment Scaling (RES), transforms existing benchmark environments while preserving their objectives and completion requirements. A proposer changes information access, reso...
These findings show that effective pruning should preserve broad visual coverage before fusion and progressively refine the retained tokens as cross-modal evidence evolves during fusion, and propose STAR-Pro (STage-Wise Adaptive Token Reduction with Progressive Refinement), a training-free two-stage framework.
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