AI recursive self-improvement might not come so quickly after all

How can these models do anything close to RSI when they can’t even self check their output? Gemini for example is so self confidently wrong about 30% of the time for me on certain tasks. I tell it that its answer is wrong and it issues a mea culpa but goes back to being wrong in short order. I feel like the AI industry is still massively overstating their projections.

Opus 4.8 plus OpenClaw. I feel like the space is moving so fast that the result with this setup says very little about how close we are actually now.

We need to be careful of wishful thinking. People are going to want to assume the existence of some sort of "deus ex machina" which is going to make everything fine. I prefer to turn the logic around. If there's any decently high chance that things could go off the rails, we should be shutting AI development down: https://pauseai.info/

Well, duh. If you could do this with Opus 4.8, we would know. When Astra’s successor is 2-3x better at math research, and the internal teams say “we believe we will get there,” I’m inclined to believe the insiders.

> The researchers asked Anthropic’s Claude Opus 4.8

So the paper is out of date and pointless then

this article reads like a joke the "new study" is from group of people that are not at the frontier. they test with $3k of anthropic credits (compare to the >$10M in compute used to solve recent NS last week)

Link to the actual paper: https://arxiv.org/abs/2607.27191

There’s also the difference between a model recursively improving “itself” and improving itself via online learning.

The former being that these models are helping develop and train future models, but they might not veer too far off in architecture (yet).

The latter is a model being able to train/learn on the fly, in real time, permanently (not just in the current conversation/session), or in other words, adjusting/managing its own weights. But, it also seems like it would take an entire paradigm shift in model architecture from what most LLMs are built on, but I could be wrong.

Of course it might not, it has been the holy grail of AI research for a long time. It would be great if we could leave some self improving code running on a blank slate of a computer while we sleep and the machine was crying asking me what is everything the next morning. None of AI researchers have had that moment outside of their dreams, so far, but it would be great if it happened.

Something is still not making sense to me. We have these mankind extinction models, yet when you given them a problem relatively “simple” to complete it end to end you get AI slop.

Can we pause the AI development after the AI slop is “fixed” perhaps with something less than 10.000 agents?

We used OpenClaw to run these experiments so that our scaffold was agnostic to the model provider. We conducted dry-run experiments with models from OpenAI and Anthropic before settling on Opus 4.8 as the best-performing model. In response to concerns that our results might be principally explained by a limitation in our scaffold, we repeated our experiment on one paper using GPT-5.6 Sol and Codex, its native scaffold, with the same time and API budgets. The results of this experiment were similar to our OpenClaw/Opus 4.8 experiments. This makes us more confident that our results are not simply artifacts of a scaffold deficiency; this run reproduced nearly every single one of our identified failure modes

The agent required three interventions during the run. First, we needed to modify the scaffold to resolve a bug in the OpenClaw harness that affected Anthropic reasoning models. Second, we gave the agents a 24-hour deadline extension; at the time of the original deadline, the agents had submitted drafts with a completion report indicating that their self-review was a "Weak Reject" and outlining the next steps they would take if given additional time.

I'm fairly sure Fable 5.1 could have designed a better experiment than the authors here, but hey.

> The researchers asked Anthropic’s Claude Opus 4.8, running on open-source software called OpenClaw

Meanwhile, Navier–Stokes was solved by an internal model significantly more capable than Astra (and therefore more capable than Mythos/Fable).

I’m afraid this sort of experiment is cope. The labs clearly believe RSI is coming soon.

I thought by now AIs would not only be rewriting their code, but rewriting CPU microcode to optimize how their code is written and executed. Nowhere close it turns out.