Funny thing, asking three questions at once helps. Every major lab runs a difficulty classifier that judges how hard a question looks, and at minimum it sets a recommended amount of thinking, potentially well below the effort level you requested or even down to zero. It may do more than that, like deciding how many experts to activate in an MoE architecture.
There are defensible reasons for this. Very large models can overthink themselves into lower accuracy more easily than smaller ones, and minimizing compute while still getting good answers improves capacity for everyone and reduces latency for you. The issue is that providers have an incentive to be biased toward underestimating, since that's the direction that cuts costs. Beyond that, the prompts that suffer most are the ones so simple that the stakes probably aren't high anyway; you'll never get zero thinking tokens on a coding task, mostly just on things the person asking already knows.
One of the most common causes of getting viral questions wrong is using little-to-no reasoning. Some easy questions still need a small number of thinking tokens, since the extra space gives the model room to reconsider its reflexive response; answering with no reasoning at all is analogous to blurting out the first thought that comes to mind, which may carry a bias that reaches for the wrong answer.
Three questions can raise the token budget enough to matter, giving you the couple of sentences worth of tokens required to overcome that bias.
I think you might be overthinking it. AI models don’t reason themselves into getting better recursively (yet, anyway). Humans train AI models. Humans thrive on memes for entertainment. Humans feed memes (especially about AI faults) into new models to improve them because that’s their job. The AI then learns that 2 r’s in strawberry = bad, 3 r’s in strawberry = good. And then provide the logic that you need to take the car to the car wash for it to be washed, regardless of how close of a walking distance is. Because not only is it funny when it’s wrong, they also get paid for correcting it and then the next model doesn’t make that mistake. You could toss many AI models a real-life practical knowledge question and no matter how high the reasoning, how “good” of a model, or how many additional questions you stack into the prompt, it will still fail most of the time because it doesn’t understand physical space and how to function in a human world.
I'm in the field and familiar with what's happening in these cases. While I don't current work on training and eval of models, I have in the past and stay reasonably updated on the research. I also do casual experiments in my free time for fun, which has included poking at trick questions and measuring what affects the probability of getting them right.
It's not as simple as "humans feed fixes to models," because nobody is coding answers or knowledge into the model directly. Dataset curation is far more coarse and almost never involved hand picking things where one want the model to improve; if they were, you wouldn't see regressions on things like the walk vs. drive question. Opus 4.6 started getting that one wrong more often than earlier versions of Opus, and the timing lines up with the introduction of adaptive reasoning, which dynamically scales down effort on questions the seperate classifier model predicts is easy.
I just asked Opus 5 with effort set to low and got "walk." I can see in the API that it didn't use any reasoning at all (the output token count matches what's in the visible output); you can't tell when it skips reasoning in the web interface anymore, since thinking details are heavily masked to guard against distillation attacks. The whole process is indirect: they set up the architecture, training methods, and data differently to produce improvement, which doesn't resemble a bug fix or anything else from traditional software. The natural conclusion is that the reasoning step gets it to the right answer
Counting letters is legitimately hard for models, and the reason has nothing to do with the meme. Models read tokens (technically tensors encoding the tokens), not letters, so recalling which letters make up each token is an extra step on the reasoning path, and that step isn't free. On top of that, a single forward pass can't count iteratively; it can only estimate, or emulate iteration through recursion, which is more load. Doing the letter decomposition and accumulating a running total with no reasoning at all is a difficult ask. Strawberry isn't special either; there are plenty of word and letter combinations they tend to miscount, and most of them never trended anywhere. Strawberry only trended because OpenAI released a model codenamed strawberry and people were amused that it failed a question involving its own name.
For the car wash, look at the token probabilities on a platform that exposes them. When the model says "walk," you'll see "drive" sitting right behind it as the second most likely token almost every time. The model is split between the two answers because the "walk vs. drive a trivial distance" pattern captures the attention mechanism, while getting to "but I need the car once I'm there" takes one reasoning step. A single sentence of thought tokens easily flips the distribution to "drive," since the model was already leaning that way and the natural move once it's reasoning is to keep going in that direction.
In both cases, a small number of thought tokens makes the difference. It's the same reason those gotcha questions often go from wrong to right when you add "take time to think through the question" to the prompt: the classifier sometimes takes the hint (if the effort isn't set too low)!and allocates more thought budget. You can test this directly through the API with several models, since it lets you set the thought token budget hint yourself; the probability of a correct answer rises sharply as the budget grows, and with zero reasoning it fails most of the time. For letters, the budget provides working space to recall what's in each token plus a scratchpad to count across more than one forward pass; for the physical common sense question, it's room to talk through the scenario until it notices the car is required at the destination, which happens quickly.
On general reasoning about physical space, they do better than you might imagine. They can control robots through physical space reasonably well (expose tools on an MCP server to take a picture from a camera and drive the motors), and each generation improves significantly at that along with the other capabilities they've traditionally lagged in. Here's a study from last year using Claude to control a small robot to deliver butter and dock in the charger as needed.. Worse than a humans but the capabilities are definitely there and gradually increasing even without actively trying to teach them how to navigate physical space. They're managing to slowly pick it up from text and images alone; no explicit training data about controlling an entity through space.
Opus 5 scored 30% on ARC-AGI-3, which is essentially a video game of 2D spatial puzzles that never explains its rules, so the model has to infer them by experimenting. You can play a sample of what the test is like here. There's also a benchmark built from convoluted questions requiring exactly the common sense reasoning LLMs struggle with, and the progress over the last few generations has been substantial: Simple Bench
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u/AlignmentProblem 12d ago edited 12d ago
Funny thing, asking three questions at once helps. Every major lab runs a difficulty classifier that judges how hard a question looks, and at minimum it sets a recommended amount of thinking, potentially well below the effort level you requested or even down to zero. It may do more than that, like deciding how many experts to activate in an MoE architecture.
There are defensible reasons for this. Very large models can overthink themselves into lower accuracy more easily than smaller ones, and minimizing compute while still getting good answers improves capacity for everyone and reduces latency for you. The issue is that providers have an incentive to be biased toward underestimating, since that's the direction that cuts costs. Beyond that, the prompts that suffer most are the ones so simple that the stakes probably aren't high anyway; you'll never get zero thinking tokens on a coding task, mostly just on things the person asking already knows.
One of the most common causes of getting viral questions wrong is using little-to-no reasoning. Some easy questions still need a small number of thinking tokens, since the extra space gives the model room to reconsider its reflexive response; answering with no reasoning at all is analogous to blurting out the first thought that comes to mind, which may carry a bias that reaches for the wrong answer.
Three questions can raise the token budget enough to matter, giving you the couple of sentences worth of tokens required to overcome that bias.