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- Hacker News
- > 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.
by thorum - The method of the NS advance involved RLHE (reinforcement learning via human example), and that is only open-ended if users continue to advance the frontier within chats ahead of publications.by whatshisface
- 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?
by theplumber - > The researchers asked Anthropic’s Claude Opus 4.8
So the paper is out of date and pointless then
by Sedierta - 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.by numpad0
- 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.
by themgt - 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.
by swingboy - You may be interested in TITANS:
Test-Time Learning: The model updates its own memory weights while running an inference task.
by DenisM - 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)
- >compare to the >$10M in compute used to solve recent NS last week
Heres a thought, if theres going to be a dangerous super LLM, if it costs 10 million bucks a month to run, then theres very little danger of anyone letting it go without a purpose. Like at some point the economics make it super unlikely that AGI is a threat outside of being a tool for a nation state.
by protocolture - 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.by vessenes
- What if any of the older good models could also have written those math proofs if they were given the same order of magnitude of resources? We don’t know and there is literally no one else in the world to check it. To me it’s very suspicious that all these hacking, containment escape, hidden internal thinking, math proofs started coming out all at once in a very short time right as IPO talks have intensified and Chinese seem to get closer and closer, also regulation discussions are starting to get very serious. I have used these models and they are good, especially Fable, but not groundbreaking. With intelligent guiding I actually feel better using Opus 4.6 as I feel more in control, having less hidden away from me.by pllbnk
- The insiders that said every tech workers would be unemployed in 6 months and every white colar would be unemployed in 12 months like 2 years ago? The insiders who are about to file for IPO?
I'd trust anyone but them personally
by toasty228 - Link to the actual paper: https://arxiv.org/abs/2607.27191by dgellow
- The actual title of the paper is: "Can AI agents conduct open-ended AI research? Early evidence from two case studies"
While I appreciate that the article is throwing a web blanket on doomer claims, the actual study doesn't really get into AI self-improvement. That doesn't require writing papers. That just requires autonomously writing a software system that can produce a better AI agent then the one that created it. That said, I have little worry about this being possible as I have seen no evidence of AI agents being able to produce a working software system of that scale.
by joshheitzman - 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/by 0xDEAFBEAD
- It is easier to imagine the end of the world than the pausing of AI.by strgrd
- 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.by daavidhauser
- Yeah, it’s crazy how fast things have changed in a month. I couldn’t find a more recent replication or similar study but it would be interesting to see it done with the current frontiers. Though I don’t think that would change much about the overall conclusion of the paperby dgellow
- I made this same reply to another thread, so I'm sorry to say essentially the same thing twice, but -- the comment has a familiar structure, "it doesn't work for you because you used an [old / suboptimal / non-frontier] model. If you use X you'll see that it works". These sorts of claims push the onus back onto the other person (or in this case tfa), without really accepting the result, or taking on any work for yourself. It gets tiresome to retest with the newest model every other week. Is there any data you can provide to support your claim, or any result you can contribute here?
- 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.by smackeyacky
- Gauging state of the art against what is available for free or for very cheap per token cost is like gauging the maximum theoretical transport potential by riding a bicycle.
You’re using something that is very energy efficient; you cannot extrapolate that experience to conclude that SOTA models are not doing something much different.
- > Gemini
That's definitely part of your problem.
In my recent experience, error rates for astra/fable are at or below human level. Just like when directing humans, it pays to ask probing questions ('Are you sure about X?', 'Did you check for Y?', 'Please run Z just to double check.') if you really care about the result being correct.
by drodgers - They can only do it in the (narrow) domains that are verifiable.by kakugawa
- As someone who used to use Gemini a lot, if you are predominantly using Gemini you don't know what the current state of things is like
- I can't see a pathway for these things to be able to learn from experience in any meaningful way - the energy budget seems to prohibit it. Artificial neural networks are already many orders of magnitude more energy intensive than natural neural networks. Despite this, large nervous systems are very energy intensive as well. For example in humans 20% of the energy budget goes to the brain which is 5% of the body weight.
So I refrer to LLMs as language extrusion confabulation machines. Language extrusion was a term I heard the linguist Emily Bender use. Confabulation because my observation is that talking to an LLM is very much similar to my experience of interacting with Korsakov syndrome patients some years ago.
I look forward to the hype settling down to see what we end up with.
by singingfish