HyperAgents

Abstract

Self-improving AI systems aim to reduce reliance on human engineering by learning to improve their own learning and problem-solving processes. Existing approaches to recursive self-improvement typically rely on fixed, handcrafted meta-level mechanisms, which fundamentally limit how fast such systems can improve. The Darwin Gödel Machine (DGM)(Zhang et al., 2025b) demonstrates that open-ended self-improvement is achievable in coding. Starting from a single coding agent, the DGM repeatedly generates and evaluates self-modified variants, forming a growing archive of stepping stones for future improvement. Because both evaluation and self-modification are coding tasks, gains in coding ability can translate into gains in self-improvement ability. However, this alignment does not generally hold beyond coding domains. We introduce hyperagents, self-referential agents that integrate a task agent (which solves the target task) and a meta agent (which modifies itself and the task agent) into a single editable program. Crucially, the meta-level modification procedure is itself editable, enabling metacognitive self-modification, improving not only task-solving behavior, but also the mechanism that generates future improvements. We instantiate this framework by extending DGM to create DGM-Hyperagents (DGM-H). By allowing the improvement procedure to evolve, the DGM-H eliminates the assumption of domain-specific alignment between task performance and self-modification skill, and can potentially support self-accelerating progress on any computable task. Across diverse domains (coding, paper review, robotics reward design, and Olympiad-level math-solution grading), the DGM-H improves performance over time and outperforms baselines without self-improvement or open-ended exploration, as well as prior self-improving systems like DGM. We further show that the DGM-H improves the process by which it generates new agents (e.g., persistent memory, performance tracking), and that these meta-level improvements transfer across domains and accumulate across runs. All experiments were conducted with safety precautions (e.g., sandboxing, human oversight). We discuss what safety entails in this setting and the broader implications of self-improving systems. DGM-Hyperagents offer a glimpse of open-ended AI systems that do not merely search for better solutions, but continually improve their search for how to improve.