AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work proposes SCOUT, a cascaded two-track retrieval framework that separates retrieval performance from exhaustive search, and evaluates it on 4 major benchmarks against 6 baselines and observes a significant query-time latency improvement up to 80.7x.
ChartAttack is presented, a framework for evaluating how MLLMs use design misleaders to generate charts that induce incorrect interpretations and AttackViz is introduced, a chart question-answering (QA) dataset labeled with effective misleaders and their induced incorrect answers.
This work proposes CORE-T, a scalable, training-free framework that enriches tables with LLM-generated purpose metadata and pre-computes a lightweight table-compatibility cache, and uses 1.20x fewer total selection tokens than LLM-intensive baselines.
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