Closed-Loop Autonomous Experimentation

A system that proposes, runs, analyzes, and re-plans experiments in a loop with limited human intervention.

The loop

Closed-loop experimentation joins four steps into a cycle: a planner proposes the next run, an executor performs it (a simulation or an instrumented rig), an analyzer extracts results, and an optimizer updates its beliefs and proposes again. Humans set objectives and guardrails; the loop handles the routine iteration.

Components

• Objective and constraints: what to maximize, what must not be violated
• Proposal engine: often Bayesian optimization or active learning
• Execution and data capture with full provenance
• Automated analysis that returns a scalar or vector the optimizer understands
• Safety and sanity gates that can halt the loop

Why it is powerful

Human-in-the-loop iteration is limited by attention and working hours. An autonomous loop explores overnight, keeps a complete record, and does not skip the boring parts. For simulation campaigns it can traverse a design space far faster than manual batch submission.

Why it needs guardrails

An optimizer will exploit any flaw in the objective. If a reward is mis-specified, the loop finds the loophole, not the intended goal. Autonomous loops therefore include validity checks, bounds on parameters, and a human review of any result that would change a frozen design number.

def loop(propose, run, analyze, update, stop):
    state = None
    while not stop(state):
        x = propose(state)
        if not valid(x):            # guardrail
            state = update(state, x, penalty=True); continue
        y = analyze(run(x))
        state = update(state, x, y)
    return state

Kronos scope

At Kronos these loops drive simulation studies, not physical reactor operation; the machines are design and simulation, and construction of the breeder begins in the second quarter of 2027. Autonomy accelerates the modeling that de-risks that build.