AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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LOBIN is a solution that utilizes machine learning within the network for market prediction based on high-frequency market data feeds and achieves over a 10% reduction in latency compared to the NASDAQ order-matching server benchmark and delivers microsecond-level latency.
The gateway breaks the direct-connect data plane and consolidates legacy API integration, protocol bridging, access control, and session-aware routing, while scaling out elastically at low per-call overhead.
NetArtifactBench is introduced, which tests whether AI agents can repair inconsistent records derived from public network-system artifacts while preserving claims that remain supported, and argues that artifact integrity should become a first-class design and evaluation requirement for AI agents operating on network sy...
A cloud-scale gateway system for MCP service is presented, which breaks the direct-connect model on the data plane and offloads legacy service integration, consolidating incompatible MCP variants, access control, tool recommendation, and session-aware routing to the gateway.
A reference architecture is developed that separates proposal generation from governed execution, identifies recurring integration patterns for LLM-enabled operations, and derives a research agenda for higher network autonomy under explicit assurance, safety, and governance constraints.
An architecture that decouples complex policy enforcement from high-speed packet forwarding to support VPC semantics on back-end NICs and enable front-end/back-end integration is proposed, suggesting that commodity hardware can support both high-throughput AI training and flexible VPC features.
ParaFlowO is proposed, an architecture that Parallelizes processing elephant Flows across multiple CPU cores while preserving in-Order delivery and integrates a lightweight reordering mechanism to preserve packet order and controls parallelism to mitigate contention on shared state.
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