AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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As claims about recursive self-improvement (RSI) and artificial general intelligence (AGI) proliferate, we ask a simple question: do frontier AI models have a sense of data, i.e., can they reliably select the right data for training? We introduce DataSense-Bench to study this capability through the fundamental problem...
This work identifies the \emph{interaction space} as a key design choice, and shows that acting on adapter corrections that refine backbone outputs, the \emph{correction space}, rather than on the predictions themselves, avoids directly propagating backbone errors across variates.
It is argued that tutoring safety is fundamentally different from conventional LLM safety: the primary risk is not toxic content but the quiet erosion of learning through answer over-disclosure, misconception reinforcement, and the abdication of scaffolding.
This survey presents a comprehensive overview of fairness-aware network embeddings for complex networks and proposes a taxonomy that categorizes existing methods along three main complementary dimensions: underlying embedding approach, fairness intervention strategy, and fairness objective criterion.
This work proposes a bias evaluation framework named GPTBIAS that leverages the high performance of LLMs (e.g., GPT-4 \cite{openai2023gpt4}) to assess bias in models and introduces prompts specifically designed for evaluating model bias.
This work proposes a framework that aligns self-supervised respiratory encoders with medical terminology in a shared latent space turning them into a zero-shot-capable foundation model, and uses a medical LLM to synthesize structured reports from metadata, creating dense semantic anchors for contrastive learning.
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