AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work proposes $D^2-Monitor, in which a lightweight probe runs on every sample, producing a base prediction and a hesitation estimate that decides whether to escalate the sample to a more expressive probe, and exhibits the best trade-off between effectiveness and efficiency relative to 8 baselines.
This paper introduces skill cascading attacks, a threat paradigm in which a malicious objective is distributed across multiple skills so that each modification looks benign in isolation, yet their combined execution is harmful.
A multi-agent synthesis framework is developed that composes individually benign components into harmful scenarios and generates diverse DIH videos with explicit reasoning annotations, yielding a dataset of over 9,000 videos spanning visual-only and audio-visual settings.
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