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HyperAgents: Self-referential self-improving agents

github.com

11–20 of 117 posts

Re: HyperAgents: Self-referential self-improving agents

#13

The paper is here - https://arxiv.org/pdf/2603.19461 This, IMO is the biggest insight into where we're at and where we're going: > Because both evaluation and self-modification are coding tasks, gains in coding ability can translate into gains in self-improvement ability. There's a thing that I've noticed early into LLMs: once they unlock one capability, you can use that capability to compose stuff and improve on oth…

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.

Re: HyperAgents: Self-referential self-improving agents

#15
post #5

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

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-improve…

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

Re: HyperAgents: Self-referential self-improving agents

#16

The paper is here - https://arxiv.org/pdf/2603.19461 This, IMO is the biggest insight into where we're at and where we're going: > Because both evaluation and self-modification are coding tasks, gains in coding ability can translate into gains in self-improvement ability. There's a thing that I've noticed early into LLMs: once they unlock one capability, you can use that capability to compose stuff and improve on oth…

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.

In science there are ways to surface subjectivity (cannot be counted) into observable quantized phenomena. Take opinion polls for instance: "approval" of a political figure can mean many things and is subjective, but experts in the field make "approval" into a number through scientific methods. These methods are just an approximation and have many IFs, they're not perfect (and for presidential campaign analysis in particular they've been failing for reasons I won't clarify here), but they're useful nonetheless.

Another thing that get quantized is video preferences to maximize engagement.

Re: HyperAgents: Self-referential self-improving agents

#17

The paper is here - https://arxiv.org/pdf/2603.19461 This, IMO is the biggest insight into where we're at and where we're going: > Because both evaluation and self-modification are coding tasks, gains in coding ability can translate into gains in self-improvement ability. There's a thing that I've noticed early into LLMs: once they unlock one capability, you can use that capability to compose stuff and improve on oth…

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.

[deleted]

Re: HyperAgents: Self-referential self-improving agents

#19

The paper is here - https://arxiv.org/pdf/2603.19461 This, IMO is the biggest insight into where we're at and where we're going: > Because both evaluation and self-modification are coding tasks, gains in coding ability can translate into gains in self-improvement ability. There's a thing that I've noticed early into LLMs: once they unlock one capability, you can use that capability to compose stuff and improve on oth…

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/

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