AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Multi-agent LLM systems rely on generative models for expert execution, workflow planning, and artifact review. Yet many coordination operations require only bounded decisions, such as selecting the next expert or judging artifact quality. Profiling three representative MAS frameworks, we find that routing and review a...
MAS-Shield is proposed, a secure and efficient defense framework designed with a coarse-to-fine filtering pipeline that achieves a 92.5\% recovery rate against diverse adversarial scenarios and reduces defense latency by over 70\% compared to existing methods.
This work introduces Router-Mem, an evidence-conditioned progressive execution framework for long-horizon agent memory that is trained with evidence-level supervision and rationale-conditioned representation distillation and achieves strong answer quality while maintaining low online latency.
HFlow is proposed, an evolutionary framework for generating agentic workflows through human-agent collaboration that produces cost-effective and adaptive solutions, better aligning automated agen-tic systems with dynamic human needs.
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