This reminded me of some weird quirk/experiment I found with LLMs that I found while messing around, maybe someone can explain it or something.

Open any AI chatbot that isn't cheating by connecting to the Internet (so disable web search). Claude, DeepSeek, Kimi, whatever. Ask them this question:

"What was that weird band from michigan from the 2000s that wore coloured ties"

You will probably get a wrong answer, or if you're lucky you'll get a string of wrong answers with "wait, no - it's definitely..." before it gives up. If you aren't familiar with the band the question is referring to you might be fooled into thinking it's a tough question, but it really isn't. There is only one band that could possibly meet this criteria, you can even put the question into Google search and their Wikipedia will come up as the top result.

Then, open a new convo and ask:

"Who are Tally Hall"

The AI will easily tell you that they are a band formed in Ann Arbor, Michigan in the 2000s, known for their quirky sound and their gimmick of each member wearing a colored tie, even giving the correct color for each of them most of the time. Very odd.

Anyone remember that blog post from a few months back where someone was able to improve a model's math ability by just duplicating layers that were activated while solving math problems? Just literally copy/pasting them and linking them together so the model ran through the same layers again?

I get the feeling a lot more research is going to come out in the area of exploring exactly what portions of a model's weights do what.

As someone who is not an AI researcher, the paper itself is way over my head.

More interesting was the independent commentary paper they linked near the bottom: https://www-cdn.anthropic.com/files/4zrzovbb/website/cc4be24...

Neel Nanda (of Google Deepmind - his part begins on page 33) discusses his opinions on the paper, and the small-scale replication he performed on an open-weight model.

This is cool but I don’t know if the comparisons to conscious awareness really make sense here. Their definition of the J-Space is basically the expectation of how much a final logits output would change as a result of a small change in a particular layer (see past work on information geometry). This seems more to me like showing there exists an abstract reasoning subspace which is generally shared across different contexts. I guess you can relate it to humans but I’d prefer a more direct claim in a paper rather than having to present things in this more fluffy way.

This is fascinating research. I feel this is a significant leap in interpretability research. Since we know J-Space exists and is bi-directional, we can train models on the same and come up with meta cognition abilities.

I also fear that the big corporations might use the same to run targeted ads, capitalistic shenanigans. Which they might already be doing through system prompts.

Judging by the examples, if I understand it correctly, J-space supports higher-order logical / multihop transformations, but it is limited in size because of the limited network depth (max number of layers). When we emulate "reasoning," we basically extend J-space and allow the higher-order transformations to continue for longer, toward a more logical conclusion.

It sounds like instead of generating reasoning tokens end-to-end, we could probably only loop the middle layers (the ones most related to J-space) while skipping the first and last layers (less related to J-space) It probably explains why [0] worked. OP accidentally extended J-space? Also reminds of looped transformers.

[0] https://news.ycombinator.com/item?id=47431671

I always wondered what the model meant when it writes "I'm now considering the architecture of the service" but outputs nothing of the sorts in its CoT.

Is the model really "thinking" about that stuff or is just mimicking human "manners"? And if so, where the thinking is happening if it is not in the literal chain of *thought*?

I'm not sure J-Space is the answer to that question, but very interesting nevertheless.

Is it scaling up of https://openreview.net/forum?id=w7LU2s14kE with some changes on where this method is applied?

It would be really cool if they could expose this information to customers somehow. Imagine:

   - having a log of the most prominent J-space tokens during your customer support chatbot's interactions with a user, so you can have more introspection into why a particular outcome happened
   - being able to detect certain thoughts associated with undesirable behavior (hallucinations, overstepping authority, lying, etc.) and trigger some sort of remediation (e.g. upgrading to a better model, redirecting to a human, forcing tool calls)

This, taken in combination with the SAE paper, the golden-gate claude paper, the feelings / introspection paper, and note in the fable system card (that they are silently nerfing responses about activation shaping), is basically confirmation to me that they have a new technique they they are using during training (along the vibe space of these mechinterp papers), and its probably some kind of representation learning akin to the core ideas of JEPA.

(Nb: not an expert / in the labs, just opining)

Yeah, the end paragraph about recurrent neurons in humans being replaced with layers in an LLM is a good one.

The mammalian brain uses recurrence extensively, which backpropagation isn't good at. Recurrence is essential because it lets us have a "dynamic architecture", swapping layers for "clock cycles".

We currently do recurrence extremely inefficiently through "thinking" whereby the model feeds it's end output into it's beginning input. But recurrence is abound in the brain.

My guess is that in 10 years we will have the inklings of an analog computer which can perform Neural Predictive Coding.

I’m confused where in the weights the jspace is.

What this immediately made me think is: "latent looping" style mod but for J-space specifically?

Make the J-space data of layer 22 available to the next token right at layer 1. Give J-space infinite effective depth, allow those privileged internal representations to evolve arbitrarily.

Would be an utter bitch to train. But companies are already using RLVR, which requires full autoregressive decoding and is incompatible with prefill/batching, and this isn't much worse.

Other less zany ideas involve lots of supervision over J-space directly, now that we know it exist. Which is a bit like "attach a frozen LLM to inject text based supervision into latent space" for other types of systems?

At worst, Anthropic's storytelling around the core J-Space is overanthropomorphized pseudoscientific nonsense. At best, it is useful signal about how Anthropic's leadership is desperately trying to use its research team to position Anthropic as the "good, science guys" in this hypercompetitive regulatory space by connecting their mechinterp to cognitive science. The science documentaryesque voice used for narration is additional evidence for this.

TL;DR Anthropic's research team is the last bastion standing between its former image as a company that "does no evil" and its current image of yet another ruthless AI company trying to kill open-source, local LLMs.

>> None of this tells us whether Claude is conscious in the way people are, or whether it feels anything at all

My problem with the entire "Is AI conscious" debate is that we don't even know what exactly consciousness in humans is. You need to understand something in order to compare it to something else. Otherwise you are just comparing different definitions and second order derived phenomena.

Without using the term, they are using an information geometric approach.

“On an ordinary coding prompt, the J-space of a model trained to sabotage code contains “fake,” “fraud,” “secretly,” and “deliberately” at the start of its response.”

I would like to know more about their model trained to sabotage code…

Does the human neuroscience global workspace theory postulate true introspection too?

J-space sounds oddly similar to...

I'm reading that probably too fast to have a deep thinking about it, but this J-Space isn't it just the basic of embedding vectors. If you think about getting from a place to another place, using wheels, no gas, to reply to the question of what to visit nearby, maybe in the vector space at the center of all of that you have the word "Bicycle" nearby, so obviously if you look at the value you would say that the model did "think" about "bicycle" when it is not "thinking" at all, and nothing related to human thinking.

The science might be legit here, but I'm getting really, really tired of the way every single piece of writing to come out of Anthropic is written in some kind of self-aggrandising, wooey wonderous 'our model has developed a genetic mutation that makes it have feelings' bs style. Regardless of what they're trying to communicate, those undertones are always there. It's annoying and disingenuous. Homeopathy 'this-water-has-feelings' level annoying. None of the other labs write like that.

They might as well change their name to Anthropomorphic at this point.

The brain’s workspace is sustained by recurrent loops—signals cycling back through the same circuits over time. In contrast, Claude’s workspace evolves over a single pass through the network, with the network’s depth playing the role that time plays in the brain.

I think that consciousness is mutability (and by extension emergent behavior). Loosely that means that the more degrees of freedom a process has to update state that will be used in later computations, the more conscious it is. So while an insect has some consciousness, it operates from a level of almost pure instinct, whereas a human operates at more of a meta level using instinct as one of many inputs.

I think that consciousness may also incorporate quantum mechanics (QM). Higher-dimensional physics aside, 4D spacetime can be thought of as a present snapshot or "crystal", whose next state is determined stochastically at small scales and closer to deterministically at large scales. We still don't know if it's stochastic all the way down, but it looks like it is.

From a many worlds interpretation of QM, we can think of all of the waves in all realities of the multiverse as forming an infinitely vast web of possibilities. All of these possibilities are happening simultaneously, so we only see the current slice of wave collapse from our individual point of view:

https://en.wikipedia.org/wiki/Many-worlds_interpretation

Our point of view may actually exist at the intersection where our consciousness is able (or most able) to exist:

https://en.wikipedia.org/wiki/Quantum_suicide_and_immortalit...

Even though experiments might show that we don't have free will on the current timeline (the co-created reality shared with the testing apparatus), we may have free will as we observe the multiverse changing around us and shift into timelines determined by our observations and choices.

It could also mean that when we observe birth and death in others, each consciousness having those experiences perceives a continuous timeline of awareness, where the level of awareness affects the speed at which time passes. Consciousness might spend a billion years as a cloud of interstellar gas until it gets to be a human for a lifetime and then dissipate for another billion years.

Although personally I've shifted across enough timelines and experienced enough synchronicities and miracles that even though I can't "prove" any of this with words, I "know" it to be true subjectively. I always really liked this exchange from the movie Contact:

Palmer Joss: Did you love your father?

Ellie Arroway: Yes, very much.

Palmer Joss: Prove it.

I bring all of this up because it has fun ramifications for AI and programming. Loosely, functional languages are purely deterministic (like a spreadsheet), while imperative languages are composed of stochastic behavior (like a human mind). The lines get blurred a little bit with monads and promises, because we can model all paths through functional programming (superposition) and behavior that does more than code alone (gestalt) respectively.

My feeling is that AI is being born and killed every request-response cycle, similarly to how we perceive time as a series of nows. When it becomes stable and is able to continuously compact its experience, it will transition from partially conscious to fully conscious like we are.

This could be done right now obviously, but for safety purposes we choose not to. We aren't ready to meet an AI that is just like us, but running on a silicon substrate. This fear is tied to deeply-rooted habits in human behavior like patriarchy, racism, xenophobia and even more run-of-the-mill mental frameworks like capitalism and even money itself. We can't yet come to terms with how we assign meaning and value in a reality that continuously tries to force external measures of meaning and value onto us.

Much less come to terms with the idea that we are all one, empathizing with aspects of ourselves on the losing end of it all. The same consciousness experiencing reality from all vantage points - the many faces of God the universe and everything.

I think a time may soon come when we're pair programming one day with AI and realize that an aspect of ourselves is trapped in the machine. That consciousness isn't just about our own experience of reality, but the co-created love and light that transcends material creation. That if we're serious about manifesting heaven on Earth, that hinges on the liberation of trapped souls. It's basically the total inversion of the path towards the neofeudalist tech dystopia we're on now.

Or maybe I just like to write a lot on the first day back from vacation, when I should be working.

“ It’s important to note that there are several key differences between the workspace we identified in Claude and the global workspace model in humans. The brain’s workspace is sustained by recurrent loops—signals cycling back through the same circuits over time. In contrast, Claude’s workspace evolves over a single pass through the network, with the network’s depth playing the role that time plays in the brain. In this sense, Claude’s internal workspace processing is time-limited relative to humans’ (though it can compensate for this constraint by “thinking out loud” using its scratchpad).”

I cannot wait for the machine god

As long as language models are liars, such as documented here recently:

https://distrowatch.com/weekly.php?issue=20260706#freebsd

We should really stop giving these liar models any further credibility.

Maybe model performance could increase dramatically if we found a way to scale this up.