GAN

The latest tutorial by Li, Huang, Li, Zhou, Zhang, and Liu surveys how GANs, diffusion models, and LLMs now mass‑produce synthetic text, tables, graphs, time series, and images for data‑mining workloads. That’s the supply side. The demand side—execs asking “will this improve my model and keep us compliant?”—is where most projects stall. This piece extracts a decision framework from the tutorial and extends it with business‑grade evaluation and governance so you can decide when synthetic data is a shortcut—and when it’s a trap. ...

For years, LLM security research has mirrored the cybersecurity arms race: attackers find novel jailbreak prompts, defenders patch with filters or fine-tuning. But in this morning’s arXiv drop, a paper titled “CAVGAN: Unifying Jailbreak and Defense of LLMs via Generative Adversarial Attacks” proposes something fundamentally different: a single framework that learns to attack and defend simultaneously, using a GAN trained on internal embeddings. This paradigm shift offers not only better performance on both sides of the battlefield, but a new perspective on what it means to “align” a model. ...