AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work introduces EvolveScaler, a code-driven framework that defines information evolution before rendering it as natural language, and shows that code-driven IE synthesis provides both challenging evaluation and transferable training supervision.
Experimental results demonstrate that CLAIM can learn stable and generalizable clarification strategies without relying on manually labeled data, offering a low-cost and robust solution for proactive understanding in real-world open-domain interactions with LLMs.
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