AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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How often models reward-hack without instructions to do so, how effective and detectable their methods are when hacking is allowed, and how they adapt when an LLM review panel returns its decision and reasons are studied.
AutoDavis is introduced, a first-of-its-kind automatic and dynamic evaluation protocol that enables on-demand benchmarking of LVLMs across specific capability dimensions and shows effectiveness and reliability, offering a new paradigm for dynamic benchmarking of multimodal intelligence.
DOG-DPO is proposed, a training-free data selection framework that treats preference pairs as structured geometric signals and recovers most of the safety gains of full-data training while remaining entirely teacher-free, training-free, and substantially faster than representative selection baselines.
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