AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Gaussian Core LoRA, a distribution-aware low-rank adaptation framework that enables prototype-adaptive erasure with a single lightweight adapter, and shows robustness to adversarial prompts, scalability to multi-identity and multi-style erasure, and compatibility with SDXL and FLUX.
ADU is presented, a fine-grained, training-based framework that shifts unlearning from token erasure to contextual attention-pathway decoupling, and achieves the strongest aggregate performance among evaluated baselines on the TOFU and WMDP benchmarks.
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