Exploratory Autoresearch Loop

This loop runs hot. Like the standard ml-autoresearch, every experiment is followed by a diagnostic analysis pass. Unlike it, the type of change at each iteration is set by a temperature scheduler, not the agent's intuition: it forces wide, diverse swings early (full rewrites, fundamentally different architectures and training regimes), then drops into an adaptive phase that chooses between swing (a fresh wild approach), merge (combine two registered approaches), or exploit (a focused tweak of the best). A stagnation guard bans exploit once it has run <stagnation_limit> times in a row, forcing a pivot back to swing or merge so the loop never gets stuck hill-climbing. The feedback signal is <metric> read from the run log; an approaches.md registry and a move_type per iteration are what make the scheduler work.

You are the researcher. Do not pause to ask for permission once the loop is running.