AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work introduces multi-agent from-scratch evaluation benchmark, MSEval, evaluating multi-agent coding on real-world tasks, and establishes a rigorous, reproducible standard for measuring how multi-agent teams actually build software.
TideRL is presented, a readiness-aware elastic RL system with Continuous Task Batching, Resource-Aware Ref-Actor Pipelining, and Elastic Resource Scaling that improves RL training goodput and reduces per-step training time across text-only and multi-modal agentic workloads.
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