LLM‑driven Feature Discovery uncovers hidden model behaviors without internals
The authors prompt a black‑box LLM to label notable snippets of chat transcripts, embed the resulting descriptors, and cluster them into semantic groups. This unsupervised pipeline surfaces unexpected Gemini behaviors without inspecting model internals, offering a cheap qualitative tool for deployment‑ or RL‑training‑distribution analysis.