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. ...
TL;DR for operators LearnerAgent puts LLM-based “students” through a simulated year of high-school English learning: weekly lessons, exercises, monthly exams, memory retrieval, self-reflection, confidence updates, and peer debate.1 The point is not to cosplay a classroom because AI research apparently needed more homework. The point is to observe learning as a process, not merely as a final benchmark score. ...
TL;DR for operators Manimator is best understood as a content-production pipeline, not as a magical professor trapped inside a video renderer. The system takes a prompt, PDF, or arXiv ID, asks an LLM to turn it into a structured scene plan, asks a code-focused LLM to generate Manim Python, and then renders the result into an explanatory animation.1 ...