HyperAgents: Self-referential self-improving agents

The whole theme of llm dev to date has been "theres more common than not" in llm applications

Agree. It's code all the way down. The key is to give agents a substrate where they can code up new capabilities and then compose them meaningfully and safely.

Larger composition, though, starts to run into typical software design problems, like dependency graphs, shared state, how to upgrade, etc.

I've been working on this front for over two years now too: https://github.com/smartcomputer-ai/agent-os/

I disagree that evaluation is always a coding task. Evaluation is scrutiny for the person who wants the thing. It’s subjective. So, unless you’re evaluating something purely objective, such as an algorithm, I don’t see how a self contained, self “improving “ agent accomplishes the subjectivity constraint - as by design you are leaving out the subject.

because submarine piloting is a going-under-water activity, improvements in holding one's breath can lead to faster submersibles.

I don't think generation/discrimination is fundamental. A more general framing is evolutionary epistemology (Donald T. Campbell, 1974, essay found in "The Philosophy of Karl Popper"), which holds that knowledge emerges through variation and selective retention. As Karl Popper put it, "We choose the theory which best holds its own in competition with other theories; the one which, by natural selection, proves itself the fittest to survive."

On this view, learning in general operates via selection under uncertainty. This is less visible in individual cognition, where we tend to over-attribute agency, but it is explicit in science: hypotheses are proposed, subjected to tests, and selectively retained, precisely because the future cannot be deduced from the present.

In that sense, generation/discrimination is a particular implementation of this broader principle (a way of instantiating variation and selection) not the primitive itself.

It's a feedback loop.

I've always felt that the most important part of engineering was feedback loops.

Maybe nature is the greatest engineer ever?

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.

Hermes agent does this, if you're curious

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

What are the differences wrt Recursive Language Models