Guided research lessons

A model can learn from a new corpus while losing old behavior, receive privacy-protecting noise while losing task accuracy, or refuse a harmful prompt while also refusing a harmless neighbor. Success at the intended target leaves a nearby question unanswered.

The studies suggest a bounded way to investigate: locate what changes, choose a comparator, and inspect the closest relevant boundary. That boundary is different for training updates, privacy calibration, and prompt repair. Neither a plausible mechanism nor a reassuring broad score settles the other studies’ questions.

A robot may close a laptop yet drop an object it has grasped; another must feel whether an object is soft, or complete a task when the tabletop changes. Improving these behaviors is not one problem with one remedy.

The studies here change different parts of a robot system: training from human touch, rewarding distinct successful actions during practice, or turning human movement into commands a humanoid can use. Each reports a physical-robot outcome, but the tasks and comparisons differ. The useful question is which change addresses the difficulty a particular deployment presents.

A robot can complete a task and still respond too late to a moving object. It can also reach a destination after a collision. Saying that it “succeeded” leaves out what was measured and what happened while it decided.

The selected studies offer a bounded answer: adapting computation was checked against task completion, rewarding varied successful behavior was checked under shifted simulation conditions, and a moving-world test separately counted safe arrivals. Each claim belongs to its own test.