AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work introduces an inclined boundary that evaluates prediction loss relative to predictive entropy, and shows that entropy correction can preserve the expected membership signal while reducing its variance, thereby improving standardized member--non-member separation.
CAT is introduced, a paradigm in which the agent writes Playwright code to drive the browser, gathers feedback, and autonomously explores web applications to uncover bugs, revealing a clear gap between current VLM capabilities and the demands of real-world testing in AI web development.
COVE is presented, a unified agent self-evolution framework that combines harness-based and parameter-based learning through task-aware routing, stage-aware scheduling, and knowledge optimization, and shows that COVE outperforms single-channel evolution strategies.
This paper systematically compares four state-of-the-art LLMs, including Gemini~3.1~Pro, GPT-5.4, and Claude~Opus~4, on a FormalMATH subset and on two real PDE theorems requiring deep domain expertise, evaluating their ability to produce verified Lean~4 proofs and to identify errors in deliberately incorrect proofs.
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