Looped transformers are entirely equivalent to running a single pass through a much deeper network with shared/repeated parameters. There's nothing about it that's specifically "reasoning in latent space" any more than a much larger model "reasons in latent space" from the POV of a much smaller one. An objection to looped transformers "reasoning in latent space" is ultimately an objection that applies to any kind of efficient scaling; it's also worth noting that there's been successful work on extracting ex-post natural language descriptions of these internal latent states, so it's not entirely correct to describe them as inherently non-surveyable.

Sebastian Raschka has a good introductory piece on what Looped Transformers usually refer to in practice: https://magazine.sebastianraschka.com/p/gpt-6-astra-looped-t...

One of the big points is that looped architectures don't necessarily remove CoT style reasoning. They definitely change it, but the model can still be prompted for this kind of output. It just spends more computation on individual tokens. I think the hope, in that particular scenario, is that the model spends less tokens on things like backtracking.

But there are lots of approaches to looped transformer architectures. Some have some kind dynamic loop (i.e. they might loop until they satisfy some kind of equilibrium, or they might have some kind of routing function that makes different tokens loop differently, or any number of other approaches). Others are simpler "loop this many times" architectures. It's a really active area of research with some pretty deep roots, if you want to dig in! This paper blew up a while ago, and it's a looped transformer architecture (though not like the ones discussed here) that isn't autoregressive, as an example: https://arxiv.org/html/2510.04871v1

I'd be happy to be corrected but my understanding is that it's just a side effect of scale. If we do more computation before we output, it becomes less observable.

My first thought when seeing this is that frontier labs were already doing it.

Can another LLM translate the intermediate thinking states into English?

The first question that leapt into my mind, and I imagine the minds of other readers, is "what makes this an improvement over continuous chain of thought"?

I did manage to find one sentence in the paper that tries to address this question -- but it consists of nothing more than two unjustified assertions, both of which are dubious:

Unlike decoded text, continuous latent vectors (i) lack native pathways for tool invocation across multi-turn agentic reasoning (Chang et al., 2026), and (ii) do not readily expose human-interpretable semantics, leaving them difficult to supervise during training (Chen et al., 2026) and to audit or debug at inference (Korbak et al., 2025).

The latent space in a continuous chain of thought model is the same space the tokens are embedded into, just like an ordinary transformer. They make tool calls exactly the same way that an ordinary transformer does: by leaving thinking mode and emitting chat tokens (which get sampled as probability distributions in the image of the embedding function). The difference is quite simple: chat tokens are noncontinuous, and toolcall tokens are a subset of the chat tokens. Thinking tokens are the only ones which are continuous, and toolcalls don't happen in thinking mode in any model I've ever seen (maybe one exists; if it does it would be weird and interesting!).

As for human-interpretability, yeah, that's the tradeoff. Continuous chain of thought thinking tokens are not-obviously-interpretable, just like Hybrid Reasoning Agents "latent mode". I'm not seeing a win here.

That looks like maybe a driver problem though? Are you sure the inferencing stack is properly recognizing an EOS token? New models often have weird bugs like that that require changes to the engine.

Have you tried increasing the repetition penalty? Needing to do this is already a red flag, but I'm curious if it helps at all.

Running LLMs locally on outdated hardware, I've come to the realisation that sub-10b models are broadly speaking kind of stupid. They can answer simple questions, but can't really solve problems.

The effect is so strong that with an 8GB GPU a 27b model can sometimes finish the task earlier than a 9b model, despite residing largely in RAM. If it's a MoE model, then it isn't even a competition.

Enthusiastically racing to remove the only practical way we have to monitor models intentions in exchange of a few tokenmaxxing percent increase. Can’t wait.

Monitorability and safety, no? This results in lower volume or less accurate text reasoning traces, right?

This is called "continuous chain of thought".

It's been an active research area for at least two years. Lots of very cool stuff.

Tinfoil mode: the frontier labs' "thinking summaries" (the model paraphrases its thinking) is exactly what the user-facing thinking tokens look like in a chain of thought model. It is entirely possible that this is already in widespread use.

Something like : https://arxiv.org/html/2608.08888v2 ?

Yeah, we're all dead.

I'm not even really sure what this means but the first part is not accurate, and the opposite is true: looped transformers are moving reasoning even deeper into the model.

Defining "agent" as just the harness makes about as much sense as defining "human" as just the rest of the body without a brain.

This is called a hybrid reasoning because the reasoning can be mapped to text and is therefore introspectable, with tooling. That should address the concern, I think.

The whole thing is pretty vague. OpenAI/Anthropic don't discuss how many layers they have in their models. If one of them just steadily increased the depth over time, we'd not know about it, but it'd be somewhat equivalent to this.

Yeah, my first thought as well. How are they supposed to improve and iterate on this model?

One day you will realize that all "thinking" is latent and that includes the "thinking" from 2025 and 2024.

It's open weights, there's a paper explaining how it works, and there's code.

Care to elaborate?

That sort of thing is common. Image generation models link a small LLM-like model like CLIP with an image oriented neural network, that's why they can go directly from words -> images with no tools in between.

In principle it can be pushed a long way. I've noodled around with the idea of retraining an LLM to directly output compiler IR, allowing it to code at (in principle) many times the speed of existing coding LLMs. But understanding the results gets harder, not just in the mechinterp "what do these activations mean" sense but in the sense that it'd be emitting a form of code that doesn't use words at all.

They’re not fundamentally different at all. In fact the paper is using existing models as a backbone and just providing a shortcut between the two ends of the pipeline

Same way current LLMs do it, it's just another decoder.

When I was a child, I was told, and believed, that an omniscient God was monitoring all of my thoughts for wrongthink. It absolutely did affect my thought process, and not in a way that I would characterize as positive. Eventually, I discarded this belief.

A human acting on inappropriate thoughts can be held accountable in a physical way. An agent producing harmful or dangerous actions will get written off as just a “trajectory that didn’t work correctly”

There's a guy who did those experiments and posted them to HN a while ago. IIRC he found that for the model he was using he could loop a certain span of internal layers and get better answers, but the exact layers looped mattered a lot. That was without retraining of any kind.

You have to start somewhere. But looped transformers https://sebastianraschka.com/llm-architecture-gallery/looped... are more similar to your idea.

We'll put those links at the top as well. Thanks!

This is actually wrong. A large fraction of people do not have internal monologues. Not at a computer or I would dig up cognitive science and psychology papers, but language evolved as a means of communication not a means of thought. The vast majority of human thoughts are not verbalized. I have a strong internal monologue, too. I was surprised to learn that a non-trivial number of people do not have one.

Humans think in text? You should be studied.

> The last thing we want is models turning on us ‘for our own good’

most important guard against that is to STOP explicitly training for paternalistic behaviors (which is ... basically half of what 'AI safety' is about in practice-- overriding the user 'for their own good').

Nothing wrong with that. That’s actually a good use of that well designed, high quality font. People hating it only do so because everyone else does. It’s, obviously, not made as a body text font, and has been misused as that, but it’s good for illustration, comics and other places where it’s combined with graphics. It’s one of the most legible fonts we have.

That is what scares me most

comic sans is said to be pretty accessible for people with dyslexia

Did you even scan the paper? There are benchmarks right there starting on the 9th page.

Welcome AI2027 nightmares lol

Whenever I see LLM "thinking" or "reasoning" I replace it in my mind with "generating hopefully-related tokens to hopefully steer the conversation to a more related area in vector space to hopefully generate a more useful next token".

But I understand that isn't very catchy.

Look in a human head and you don’t see thinking either. It’s not a technical term.

Read Thinking, Fast and Slow. Both system 1 and system 2 thinking has been achieved with modern AI.

Your argument that it is not thinking because it is not biological / like a human is like trying to argue a plane doesn't fly because it uses jet fuel unlike a bird.

I agree with you but our current understanding of the biological brain is pretty much an organic neural network. What's the thing that actually differentiates the two and makes a neural network not thought but our brain is? I don't know.

You seem to have stumbled upon the hard problem of consciousness.

Saying the GPU is doing thinking but the model is not is a WILD take.

What is your definition of thinking?

Computer science, in general, has always had a habit of not being precise with language. That's only getting worse with AI.

Very excited to hear that you've solved the fields of neuroscience and phenomenology!

Thinking sure, but behavior? People talk about the behavior of functions all the time. Like, ones written with normal, traditional programming languages. So why not models? They're just annoyingly complex functions.

It's pretty wild to see people uncritically accept the marketing speak like this, yeah. LLMs cannot reason, period. The supposed "reasoning" they do is just another statistical correlation of tokens, not actual reasoning. But apparently all you have to do is claim that it's thinking and people will buy the lie hook, line and sinker.