No matter how far we go, we end up with generation / discrimination architecture.
Its is the core of any and all learning/exellency; exposure to chaotic perturbations allow selection of solutions that are then generalized to further, ever more straining problems; producing increasingly applicable solutions.
This is the core of evolution, and is actually derivable from just a single rule.
The readme seems very unclear about what it does. Anyone has a practical example of it?
That's great but how about UltraAgents: Meta-referential meta-improving self-referential hyperagents?
Can someone add this to OpenClaw :)
Pi is self modifying, self aware. https://lucumr.pocoo.org/2026/1/31/pi/
But this idea of having a task agent & meta agent maybe has wings. Neat submission.
I think even code bases will have self improving agents. Software is moving from just the product code, to the agent code that maintains the product. Engineering teams/companies that move in this direction will vastly out produce others.
I've had to really shift how I think about building code bases, alot of logic can go into claude skills and sub agents. Requires essentially relearning software engineering
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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 other, related or not, capabilities. For example "reflexion" goes into coding - hey, this didn't work, let me try ... Then "tools". Then "reflxion" + "tools". And so on.
You can get workflows that have individual parts that aren't so precise become better by composing them, and letting one component influence the other. Like e2e coding gets better by checking with "gof" tools (linters, compilers, etc). Then it gets even better by adding a coding review stage. Then it gets even better by adding a static analysis phase.
Now we're seeing this all converge on "self improving" by combining "improving" components. And so on. This is really cool.