AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work proposes the Agent-Editing World Model (AEWM), which models how reasoning and actions shape future task progress rather than simulating tool responses, and combines AEWM with EditAct, which integrates these capabilities with real execution.
Evo-Bench is the first benchmark designed to evaluate models'intrinsic harness-evolving capabilities across Search, Office, and General agent domains, and exposes critical temporal anomalies like early saturation, while demonstrating that the synthesized harnesses act as highly transferable reasoning structures, consis...
This work presents a unified black-box RL framework for stable and scalable optimization of general agents through complex harnesses, supporting unified training across heterogeneous execution systems.
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