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Finding non-equilibrium quantum states would be evidence of pilot wave theory since they're only possible in a pilot wave theory.


If you can find non-equilibrium quantum states, they are distinguishable.

(Seems pretty unlikely we'd ever be able to definitively say a state was non-equilibrium instead of some other weirdness, though.)


I can help confirm that your blind assumption is false. Source: my undergrad research was with a couple of the people who have tried hardest, which led to me learning a lot about the problem. (Ward Struyve and Samuel Colin.) The problem goes back to Bell and has been the subject of a dedicated subfield of quantum foundations scholars ever since.

This many years distant, I can't give a fair summary of the actual state of things. But a possibly unfair summary based on vague recollections is: it seems like the kind of situation where specialists have something that kind of works, but people outside the field don't find it fully satisfying. (Even people in closely adjacent fields, i.e. other quantum foundations people.) For example, one route I recall abandons using position as the hidden variable, which makes one question what the point was in the first place, since we no longer recover a simple manifest image where there is a "real" notion of particles with positions. And I don't know whether the math fully worked out all the way up to the complexities of the standard model weakly coupled to gravity. (As opposed to, e.g., only working with spin-1/2 particles, or something.)

Now I want to go re-read some of Ward's papers...


This is great, until Spotify is ready this will be the best way to share on social media.

May I suggest adding lyrics, either in the description or as closed captions or both?


If you are willing to share, can you say more about what got you into this line of investigation, and what you were hoping to get out of it?

For my part, I don't feel like I have many issues/baggage/trauma, so while some of the "fundamental debugging" techniques discussed around here (like IFS or meditation) seem kind of interesting, I don't feel too compelled to dive in. Whereas, techniques like TYCS or jhana meditation seem more intriguing, as potential "power ups" from a baseline-fine state.

So I'm wondering if your baseline is more like mine, and you ended up finding fundamental debugging valuable anyway.


It seems we have very different abilities to understand Holtman's work and find it intuitive. That's fair enough! Are you willing to at least engage with my minimal-time-investment challenge?


The  agent is indifferent between creating  stoppable or unstoppable subagents, but the  agent goes back to being corrigible in this way. The "emergent incentive" handwave is only necessary for the subagents working on sub-goals (section 8.4). Which is not something that either Sores et al. or your post that we're commenting on are prepared to tackle, although it is an interesting followup work.

I suggest engaging with the simulator. It very clearly shows that, given the option of creating shutdown-resistant successor agents, the agent does not do so! (Figure 11) If you believe it doesn't work, you must also believe there's a bug in the simulation, or some mis-encoding of the problem in the simulation. Working that out, either by forking his code or by working out an example on paper, would be worthwhile. (Forking his code is not recommended, as it's in Awk; I have an in-progress reimplementation in optimized-for-readability TypeScript which might be helpful if I get around to finishing it. But especially if you simplify the problem to a 2-step setting like your post, computing his correction terms on paper seems very doable.)

I agree with the critique that some patches are unsatisfying. I'm not sure how broadly you are applying your criticism, but to me the ones involving constant offsets (7.2 and 8.2) are not great. However, at least for 7.2, the paper clarifies what's going on reasonably well: the patch is basically environment-dependent, and in the limit where your environment is unboundedly hostile (e.g., an agent controls unbounded utility and is willing to bribe you with it) you're going to need an unbounded offset term.

I found that the paper's proof was pretty intuitive and distilled. I think it might be for you as well if you did a full reading.

At a meta-level, I'd encourage you to be a bit more willing to dive into this work, possibly including the paper series it's part of. Holtman has done some impressive work on formalizing the shutdown problem better than Sores et al., or this post we're commenting on. He's given not only rigorous mathematical proofs, but also a nice toy universe simulation which makes the results concrete and testable. (Notably, the simulation helps make it obvious how Sores et al.'s approach has critical mathematical mistakes and cannot be implemented; see appendix C.) The followup papers, which I'm still working through, port the result to various other paradigms such as causal influence diagrams. Attempting to start this field over as if there's been no progress on the shutdown problem since Sores et al. seems... wasteful at best, and hubristic at worst.

If you want to minimize time investment, then perhaps the following is attractive. Try to create a universe specification similar to that of Holtman's paper, e.g. world state, available actions, and utility function before and after shutdown as a function of the world state, such that you believe that Holtman's safety layer does not prevent the agent from taking the "create an unstoppable sub-agent" action. I'll code it up, apply the correction term, and get back to you.


Are you aware of how Holtman solved MIRI's formulation of the shutdown problem in 2019?, my summary notes at

Skimming through your proposal, I believe Holtman's correctly-constructed utility function correction terms would work for the scenario you describe, but it's not immediately obvious how to apply them once you jump to a subagent model.


This is a well-executed paper, that indeed shakes some of my faith in ChatGPT/LLMs/transformers with its negative results.

I'm most intrigued by their negative result for GPT-4 prompted with a scratchpad. (B.5, figure 25.) This is something I would have definitely predicted would work. GPT-4 shows enough intelligence in general that I would expect it to be able to follow and mimic the step-by-step calculation abilities shown in the scratchpad, even if it were unable to figure out the result one- or few-shot (B.2, figure 15).

But, what does this failure mean? I'm not sure I understand the authors' conclusions: they state (3.2.3) this "suggests that models are able to correctly perform single-step reasoning, potentially due to memorizing such single-step operations during training, but fail to plan and compose several of these steps for an overall correct reasoning." I don't see any evidence of that in the paper!

In particular, 3.2.3 and figure 7's categorization of errors, as well as the theoretical results they discuss in section 4, gives me the opposite impression. Basically they say that if you make a local error, it'll propagate and screw you up. You can see, e.g., in figure 7's five-shot GPT-4 example, how a single local error at graph layer 1 causes propagation error to start growing immediately. Later more local errors kick in, but to me this is sort of understandable: once the calculation starts going off the rails, the model might not be in a good place to do even local reasoning.

I don't see what any of this has to do with planning and composing! In particular I don't see any measurement of something like "set up a totally wrong plan for multiplying numbers" or "fail to compose all the individual digit computation-steps into the final answer-concatenation step". Such errors might exist, but the paper doesn't give examples or any measurements of them. Its categorization of error types seems to assume that the model always produces a computation graph, which to me is pretty strong evidence of planning and composing abilities!

Stated another way: I suspect that if you eliminated all the local errors, accuracy would be good! So the question is: why is GPT-4 failing to multiply single-digit numbers sometimes, in the middle of these steps?

(It's possible the answer lies in tokenization difficulties, but it seems unlikely.)

OK, now let's look at it from another angle: how different is this from humans? What's impressive to me about this result is that it is quite different. I was expecting to say something like, "oh, not every human will be able to get 100% accuracy on following the multiplication algorithm for 5-digit-by-5-digit numbers; it's OK to expect some mistakes". But, GPT-4 fails to multiply 5x5 digit numbers every time!! Even with a scratchpad! Most educated humans would get better than zero accuracy.

So my overall takeaway is that local errors are still too prevalent in these sorts of tasks. Humans don't always >=1 mistake on { one-digit multiplication, sum, mod 10, carry over, concatenation } when performing 5-digit by 5-digit multiplication, whereas GPT-4 supposedly does.

Am I understanding this correctly? Well, it'd be nice to reproduce their results to confirm. If they're to be believed, I should be able to ask GPT-4 to do one of these multiplication tasks with a scratchpad, and always find an error in the middle. But when trying to reproduce their results, I ran into an issue of under-documented methodology (how did they compose the prompts?) and non-published data (what inaccurate things did the models actually say?). Filed on GitHub; we'll see if they get back to me.

Regarding grokking, they attempt to test whether GPT-3 finetuned on these sorts of problems will exhibit grokking. However, I'm skeptical of this attempt: they trained for 60 epochs for zero-shot and 40 epochs with scratchpads. Whereas the original grokking paper used between 3,571 epochs and 50,000 epochs.

(I think epochs is probably a relevant measure here, instead of training steps. This paper does 420K and 30K steps whereas the original grokking paper does 100K steps, so if we were comparing steps it seems reasonable. But "number of times you saw the whole data set" seems more relevant for grokking, in my uninformed opinion!)

Has anyone actually seen LLMs (not just transformers) exhibit grokking? A quick search says no.


I've had a hard time connecting John's work to anything real. It's all over Bayes nets, with some (apparently obviously true ) theorems coming out of it.

In contrast, look at work like Anthropic's superposition solution, or the representation engineering paper from CAIS. If someone told me "I'm interested in identifying the natural abstractions AIs use when producing their output", that is the kind of work I'd expect. It's on actual LLMs! (Or at least "LMs", for the Anthropic paper.) They identified useful concepts like "truth-telling" or "Arabic"!

In John's work, his prose often promises he'll point to useful concepts like different physics models, but the results instead seem to operate on the level of random variables and causal diagrams. I'd love to see any sign this work is applicable toward real-world AI systems, and can, e.g., accurately identify what abstractions GPT-2 or LLaMA are using.

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