AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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The results show that sharing observations across objectives and ignoring irrelevant objective-wise distinctions can substantially reduce the sampling cost, as confirmed by experiments on synthetic and real-world tasks.
Large Language Model (LLM) agents have demonstrated remarkable capabilities in tackling complex tasks through agentic workflows. However, manually designing these workflows is labor-intensive and lacks flexibility to adapt to diverse queries. While automated workflow optimization is a promising direction, existing meth...
Verifying LLM-generated answers under a shared computational budget requires jointly deciding which candidates to inspect and which verification attributes to evaluate. We formulate this problem as multi-attribute good-arm identification under a global budget: each candidate is an arm evaluated along several costly att...
Sparse Probing and Outcome-calibrated Targets OPD is introduced, which addresses two coupled decisions, where to probe and what to distill, through an acquisition--exploration--exploitation procedure and demonstrates the effectiveness of SPOT in improving reasoning performance while balancing solution quality and cover...
DemoPSD is introduced, a novel framework that resolves problems through the idea of *selective adoption of teacher guidance*.
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