Automated Discovery

Memory is usually where agentic systems go to become expensive. That is not the glamorous failure mode. It is not the cinematic robot rebellion, nor the slightly more realistic spreadsheet full of hallucinated invoices. It is quieter: an LLM agent keeps improving a program, stores previous attempts, retrieves a few “good” ones, and then spends half its context window rereading code scaffolding that no longer explains anything useful. ...

A tutoring system has one deceptively simple job: give the learner the next problem. Not the hardest problem. Not the flashiest problem. Not the one that makes the model feel terribly pleased with itself after a 4,000-token monologue. The next problem: the one that keeps a student engaged, teaches the right structure, and feels worth the effort. ...