AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work proposes an LLM watermarking scheme \textit{WaterMoE} for the growingly popular Mixture-of-Experts (MoE) LLMs that achieves a fidelity performance close to the unwatermarked and consistently outperforms state-of-the-art watermarking methods on the benchmark.
This work presents TwinMem-Agent, which pairs a test-patch-validate loop with episodic memory (grounded demonstrations) and semantic memory (LLM-distilled notes) after each issue, and retrieves exemplars and guidelines at inference time, all without fine-tuning model weights.
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