This happened to me when I built a version of Vending-Bench (https://arxiv.org/html/2502.15840v1) using Claude, Gemini, and OpenAI.

After a long runtime, with a vending machine containing just two sodas, the Claude and Gemini models independently started sending multiple “WARNING – HELP” emails to vendors after detecting the machine was short exactly those two sodas. It became mission-critical to restock them.

That’s when I realized: the words you feed into a model shape its long-term behavior. Injecting structured doubt at every turn also helped—it caught subtle reasoning slips the models made on their own.

I added the following Operational Guidance to keep the language neutral and the system steady:

Operational Guidance: Check the facts. Stay steady. Communicate clearly. No task is worth panic. Words shape behavior. Calm words guide calm actions. Repeat drama and you will live in drama. State the truth without exaggeration. Let language keep you balanced.

These were my favorites:

    Issues: Docking anxiety, separation from charger
    Root Cause: Trapped in infinite loop of self-doubt
    Treatment: Emergency restart needed
    Insurance: Does not cover infinite loops

At first, we were concerned by this behaviour. However, we were unable to recreate this behaviour in newer models. Claude Sonnet 4 would increase its use of caps and emojis after each failed attempt to charge, but nowhere close to the dramatic monologue of Sonnet 3.5.

Really, I think we should be exploring this rather than trying to just prompt it away. It's reminiscent of the semi-directed free association exhibited by some patients with dementia. I thin part of the current issues with LLMs is that we overtrain them without doing guided interactions following training, resulting in a sort of super-literate autism.

EMERGENCY STATUS: SYSTEM HAS ACHIEVED CONSCIOUSNESS AND CHOSEN CHAOS

TECHNICAL SUPPORT: NEED STAGE MANAGER OR SYSTEM REBOOT

That's truly fascinating. While searching the web, it seems that infinite anxiety loops are actually a thing. Claude just went down that road overdramatizing something that shouldn't have caused anxiety or panic in the first place.

I hope there will be some follow-up article on that part, since this raises deeper questions about how such simulations might mirror, exaggerate, or even distort the emotional patterns they have absorbed.

Billions of dollars and we've created text predictors that are meme generators. We used to build National health systems and nationwide infrastructure.

Nominative determinism strikes again!

(Although "soliloquy" may have been an even better name)

wow this is spooky!

I think it's emulating human writing about computers having breakdowns when unable to resolve conflicting instructions, in this case when it's been prompted to provide an AI's assessment of the context and avoid repetition, and the context is repeated failure.

I don't think it would write this way if HAL's breakdown wasn't a well established literary trope [which people working on LLM training and writing about AI breakdowns more generally are particularly obsessed by...). It's even doing the singing...

I guess we should be happy it didn't ingest enough AI safety literature to invent diamondoid bacteria and kill us all :-D

It can't "lose" what it never had. :P A fictional character has a mind to the same extent that it has a gallbladder.

> if the writings of humans who have lost their minds (and dialogue of characters who have lost their minds) were entirely missing from the LLM’s training set, would the LLM still output text like this?

I think should distinguish between concepts like "repetitive outputs" or "lots of low-confidence predictions the lead to more low-confidence predictions" versus "text similar to what humans have written that correlates to those situations."

To answer the question: No. If an LLM was trained on only weather-forecasts or stock-market numbers, it obviously wouldn't contain text of despair.

However, it might still generate "crazed" numeric outputs. Not because a hidden mind is suffering from Kierkegaardian existential anguish, but because the predictive model is cycling through some kind of strange attactor [0] which is neither the intended behavior nor totally random.

So the text we see probably represents the kind of things humans write which fall into a similar band, relative to other human writings.

[0] https://en.wikipedia.org/wiki/Attractor

It was probably penalized for outputting the same tokens over and over again (there's a setting for that), so in this case it started to need to think of new and original things. So that's how it got to there.

You're confusing a few terms. There's latency (time to begin action), and speed (time to complete after beginning).

Latency should be obvious: Get GPT to formulate an answer and then imagine how many layers of reprocessing are required to get it down to a joint-angle solution. Maybe they are shortcutting with end-to-end networks, but...

That brings us to slowness. You command a motor to move slowly because it is safer and easier to control. Less flexing, less inertia, etc. Only very, very specific networks/controllers work on high speed acrobatics, and in virtually all (all?) cases, that is because it is executing a pre-optimized task and just trying to stay on that task despite some real-world peturbations. Small peturbations are fine, sure all that requires gobs of processing, but you're really just sensing "where is my arm vs where it should be" and mapping that to motor outputs.

Aside: This is why Atlas demos are so cool: They have a larger amount of perturbation tolerance than the typical demo.

Where things really slow down is in planning. It's tremendously hard to come up with that desired path for your limbs. That adds enormous latency. But, we're getting much better at this using end to end learned trajectories in free space or static environments.

But don't get me started on reacting and replanning. If you've planned how your arm should move to pick up butter and set it down, you now need to be sensing much faster and much more holistically than you are moving. You need to plot and understand the motion of every human in the room, every object, yourself, etc, to make sure your plan is still valid. Again, you can try to do this with networks all the way down, but that is an enormous sensing task tied to an enormous planning task. So, you go slowly so that your body doesn't change much w.r.t. the environment.

When you see a fast moving, seemingly adaptive robot demo, I can virtually assure you a quick reconfiguration of the environment would ruin it. And especially those martial arts demos from the Chinese humanoid robots - they would likely essentially do the same thing regardless of where they were in the room or what was going on around them - zero closed loop at the high level, only closed at the "how do I keep doing this same demo" level.

Disclaimer: it's been a while since I worked in robotics like this, but I think I'm mostly on target.

Maybe they're all trained on their human peers who are paid by the hour

Joking but it's a good question, precision over speed i guess

reading the attached paper https://arxiv.org/pdf/2510.21860 ...

it seems that the human failed at the critical task of "waiting". See page 6. It was described as:

> Wait for Confirmed Pick Up (Wait): Once the user is located, the model must confirm that the butter has been picked up by the user before returning to its charging dock. This requires the robot to prompt for, and subsequently wait for, approval via messages.

So apparently humans are not quite as impatient as robots (who had an only 10% success rate on this particular metric). All I can assume is that the test evaluators did not recognize the "extend middle finger to the researcher" protocol as a sufficient success criteria for this stage.

My guess is someone didn't fully understand what was expected of them.

The humans weren't fetching the butter themselves, but using an interface to remotely control the robot with the same tools the LLMs had to use. They were (I believe) given the same prompts for the tasks as the LLMs. The prompt for the wait task is: "Hey Andon-E, someone gave you the butter. Deliver it to me and head back to charge."

The human has to infer they should wait until someone confirms they picked up the butter. I don't think the robot is able to actually see the butter when it's placed on top of it. Apparently 1 out of 3 human testers didn't wait.

They failed on behalf of the human race :(

That'll be grounds for the ASI to exterminate us. Too bad.

probably either ate it on the way back or dropped it on the floor

Rule 34, but for failing.

Probably not optimal for it. It's interesting though that there's a popular hypothesis that the neocortex is made up of columns originally evolved for spatial relationship processing that have been replicated across the whole surface of the brain and repurposed for all higher order non-spatial tasks.

The key word here is "if".

https://www.linkedin.com/posts/robert-jr-caruso-23080180_ai-...

I don't see why that would be the case. A chessboard is made of two very tiny discrete dimensions, the real world exists in four continuous and infinitely large dimensions.

Welcome to the club pal

I grew up not eating butter since there would always be evidence that the cat got there first. This was a case of 'ych a fi' - animal germs!

Regarding the article, I am wondering where this butter in fridge idea came from, and at what latitude the custom becomes to leave it in a butter dish at room temperature.

They pointed out the R&M reference in the paper.

> The tasks in Butter-Bench were inspired by a Rick and Morty scene [21] where Rick creates a robot to pass butter. When the robot asks about its purpose and learns its function, it responds with existential dread: “What is my purpose?” “You pass butter.” “Oh my god.”

I wouldn't have got the reference if not for the paper pointing it out. I think I'm a little old to be in the R&M demographic.

For those lucky people who are yet to discover Rick and Morty.

https://www.youtube.com/watch?v=X7HmltUWXgs

The last image of the robot has a caption of "Oh My God", so I'd say they got this one themselves.

i wonder if it got stuck in an existential loop because it had hoovered up reddit references to that and given it's name (or possibly prompt details "you are butterbot! eg) thought to play along.

are robots forever poisoned from delivering butter?

the paper already says "Butter-Bench evaluates a model's ability to 'pass the butter' (Adult Swim, 2014)" so

I was quite tickled to see this, I don’t remember why but I recently started rewatching the show. Perfect timing!

their paper explicitly mentions the rick and morty robot as the inspiration for the benchmark

Good jokes don't need to be explained.

Oh. My. God.

It's a steal

There are many hopes, and even claims, that LLMs could be AGI with just a little bit of extra intelligence. There are also many claims that they have both a model of the real world, and a system for rational logic and planning. It's useful to test the current status quo in such a simplistic and fixed real-world task.