[2603.19461] Hyperagents

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Computer Science > Artificial Intelligence

arXiv:2603.19461 (cs)

[Submitted on 19 Mar 2026]

Title:Hyperagents

Authors:Jenny Zhang, Bingchen Zhao, Wannan Yang, Jakob Foerster, Jeff Clune, Minqi Jiang, Sam Devlin, Tatiana Shavrina<br>View a PDF of the paper titled Hyperagents, by Jenny Zhang and 7 other authors

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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 self-improvement rely on fixed, handcrafted meta-level mechanisms, fundamentally limiting how fast such systems can improve. The Darwin Gödel Machine (DGM) demonstrates open-ended self-improvement in coding by repeatedly generating and evaluating self-modified variants. 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 \textbf{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 the task-solving behavior, but also the mechanism that generates future improvements. We instantiate this framework by extending DGM to create DGM-Hyperagents (DGM-H), eliminating the assumption of domain-specific alignment between task performance and self-modification skill to potentially support self-accelerating progress on any computable task. Across diverse domains, the DGM-H improves performance over time and outperforms baselines without self-improvement or open-ended exploration, as well as prior self-improving systems. Furthermore, the DGM-H improves the process by which it generates new agents (e.g., persistent memory, performance tracking), and these meta-level improvements transfer across domains and accumulate across runs. 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.

Comments:<br>Code at this https URL

Subjects:

Artificial Intelligence (cs.AI)

Cite as:<br>arXiv:2603.19461 [cs.AI]

(or<br>arXiv:2603.19461v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2603.19461

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arXiv-issued DOI via DataCite

Submission history<br>From: Jenny Zhuoting Zhang [view email]<br>[v1]<br>Thu, 19 Mar 2026 20:50:08 UTC (6,553 KB)

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