An AI system that not only improves task solving, but also enhances the process of self-improvement itself.
➡️ What is it?
Darwin Gödel Machine (DGM) demonstrated that open self-improvement is possible through the iterative generation and evaluation of improved agents, but it relies on a key assumption: that improvements in task solving (for example, in programming) lead to improvements in the process of self-improvement itself. This correspondence persists in programming, where both evaluation and modification are expressed in the same subject area, but it is generally violated in other cases. As a result, previous systems remain limited to fixed, manually defined meta-level procedures that do not evolve on their own.
They introduce Hyperagents - self-referential agents capable of modifying both their behavior in task solving and the process that generates future improvements. This enables what they call metacognitive self-modification: learning not only to perform tasks better, but also to become better at the process of improvement itself.
This approach is implemented in the form of DGM-Hyperagents (DGM-H) - an extension of DGM, in which both the behavior of task solving and the self-improvement procedure are editable and subject to evolution. In various domains (programming, scientific paper review, design of reward functions in robotics, and evaluation of mathematical Olympiad tasks), hyperagents ensure continuous quality improvement over time and outperform basic approaches without self-improvement or open evolution, as well as previous self-improving systems (including DGM). DGM-H also improves the process of generating new agents itself (for example, through persistent memory and performance tracking), and these meta-level improvements are transferable between domains and accumulate from run to run.
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