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flockonus 3 hours ago

The readme seems very unclear about what it does. Anyone has a practical example of it?

pegasus 3 hours ago | parent | next [-]

There's a paper at https://arxiv.org/abs/2603.19461

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.

functional_dev 2 hours ago | parent [-]

This 'self vs non-self' logic is very similar to how plants prevent self-pollination. They have a biological 'discrimination' system to recognize and reject their own genetic code.

Here is a breakdown - https://vectree.io/c/plant-self-incompatibility-logic

OutThisLife 2 hours ago | parent | prev [-]

Hermes agent does this, if you're curious

https://github.com/NousResearch/hermes-agent