AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This paper proposes TAPE (Trajectory Alignment and exPerience pool Evolution), a novel self-evolving fine-tuning framework designed to enhance the generalization and robustness of web search agents without requiring large-scale human annotation.
LS-CM (Long-Short Collaborative Memory), a dual-memory architecture that couples a Retrieval-Augmented Generation (RAG)-based short-term memory for code-base context with a Reinforcement Learning (RL)driven long-term memory that distills strategic patterns from historical test failures and patch iterations, is proposed.
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