Iteration Scheduler A research loop without a scheduler is a queue with delusions. The scheduler is where the loop decides what to stop exploring, and that decision is the whole game.
Prerequisites: Phase 19 lessons 50-53
# Learning Objectives
Model a research workflow as a hypothesis queue feeding parallel experiment slots whose results fan back in.
Run multiple experiments concurrently with asyncio so the scheduler can keep all slots busy.
Score each hypothesis branch with UCB so the scheduler can prune low-yield branches without abandoning exploration.
Fan out finished results to a paper-write stage and a re-queue stage so a high-yield branch spawns follow-up hypotheses.
Surface a per-iteration trace with branch scores, slot occupancy, and pruning decisions.
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The text and code of this lesson are based on AI Engineering from Scratch by Rohit Ghumare, MIT licensed.