Given is an arbitrary formalizable problem whose solution may require interaction with a possibly reactive environment. For example, the goal may be to maximize the future expected (or worst-case) reward of a robot. While executing its initial problem solving strategy, the Gödel machine simultaneously runs a proof searcher which systematically and repeatedly tests proof techniques. Proof techniques are programs that may read any part of the Gödel machine's state, and write on a reserved part which may be reset for each new proof technique test. In our example Gödel machine this writable storage includes the variables proof and switchprog, where switchprog holds a potentially unrestricted program whose execution could completely rewrite any part of the Gödel machine's current software. Normally the current switchprog is not executed. However, proof techniques may invoke a special subroutine check() which tests whether proof currently holds a proof showing that the utility of stopping the systematic proof searcher and transferring control to the current switchprog at a precisely defined point in the near future exceeds the utility of continuing the search until some alternative switchprog is found. Such proofs are derivable from the proof searcher's axiom scheme which formally describes the utility function to be maximized (typically the expected future reward in the expected remaining lifetime of the Gödel machine), the computational costs of hardware instructions (from which all programs are composed), and the effects of hardware instructions on the Gödel machine's state. The axiom scheme also formalizes known probabilistic properties of the possibly reactive environment, and also the initial Gödel machine state and software, which includes the axiom scheme itself (no circular argument here). Thus proof techniques can reason about expected costs and results of all programs including the proof searcher.
Once check() has identified a provably good switchprog, the latter is executed (some care has to be taken here because the proof verification itself and the transfer of control to switchprog also consume part of the typically limited lifetime). The discovered switchprog represents a globally optimal self-change in the following sense: provably none of all the alternative switchprogs and proofs (that could be found in the future by continuing the proof search) is worth waiting for.
There are many ways of initializing the proof searcher. Although identical proof techniques may yield different proofs depending on the time of their invocation (due to the continually changing Gödel machine state), there are bias-optimal and asymptotically optimal proof searcher initializations based on variants of universal search  and the Optimal Ordered Problem Solver [38,40] (Section 2.3).
Our Gödel machine will never get worse than its initial problem solving strategy, and has a chance of getting much better, provided the nature of the given problem allows for a provably useful rewrite of the initial strategy, or of the proof searcher. The Gödel machine may be viewed as a self-referential universal problem solver that can formally talk about itself, in particular about its performance. It may `step outside of itself'  by rewriting its axioms and utility function or augmenting its hardware, provided this is provably useful. Its conceptual simplicity notwithstanding, the Gödel machine explicitly addresses the `Grand Problem of Artificial Intelligence'  by optimally dealing with limited computational resources in general environments, and with the possibly huge (but constant) slowdowns buried by previous approaches [16,15] in the widely used but often misleading O()-notation of theoretical computer science.
No complex proofs were necessary in this paper as the Gödel machine is designed precisely such that we may leave all complex proofs to the Gödel machine itself. Its main limitation is that it cannot profit from self-improvements whose usefulness it cannot prove in time.