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PaperclipPerfector  1mo ago (text post)   25585 thread views

Culture War Roundup for the week of February 16, 2026

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This is interesting to me for a few reasons. The first is that the common "shitty free models" defense crops up rapidly; commentors will say that this is a bad-faith example of LLM shortfalls because the interlocutors are not using frontier models. At the same time, a comment suggests that Opus 4.6 can be tricked, while another says 4.6 gets it right more than half the time.

I'd expect to the degree any model gets it 'right' without modification, it reflects some weirdness in the model rather than something inherently better about the strengths of the model. A couple local (and thus not-updated-specifically-to-this-question) Thinking-style models I tried the same question on gave the 'wrong' answer, but had Thinking components specifically highlighting that the question was strange and must have involved unstated assumptions (either picking up materials from the car wash to do the work at home, or the car already being there). A dumber old model got it right occasionally, but that's probably as much a result of the high temperature I was running it rather than any actual consideration.

Ask a stupid question, get a stupid answer.

Do modern models similarly have environmentalism baked in? Do they reflexively shy away from cars in the same way that a human baby fears heights? It would track with some of the other ingrained biases that people have found.

Yes. There's a fun question of whether it's just Reddit-brain, or was actively cultivated by the people training it. But since it's present in both heavily decensored or trained-out-of-US models, my bet's that the former is at least part of the problem.

The basic idea is that you are more trusting of sources if you are not particularly familiar with a topic. In this case, it's hard not to notice the flaws - most people have walked. Most have seen a car. Many have probably washed a car. However, when it comes to more technical, obscure topics, most of us are probably not domain experts in them. We might be experts in one of them. Some of us might be experts in two of them, but none of us are experts in all of them. When it comes to topics that are more esoteric than washing a car, we rapidly end up in the territory of Dick Cheney's unknown unknowns. Somebody like self_made_human might be able to cut through the chaff and confidently take advice about ocular migraines, but could you? Could I? Hell if I know.

It's... not exactly a hard trick to learn skepticism. Nor one useful only when considering LLMs. As much as that summary of "you can not actually outsource the requirement to evaluate truth" has aged like milk, that doesn't change whether it's a good idea. The core question of 'what do you know, and how do you know it' can't solve everything, but where a matter matters, you shouldn't be trusting one secondary source without verification no matter what substrate it's running on.

Thinking-style models I tried the same question on gave the 'wrong' answer, but had Thinking components specifically highlighting that the question was strange and must have involved unstated assumptions (either picking up materials from the car wash to do the work at home, or the car already being there).

Mine got hung up wondering why you would try to optimize 50 meters, it's too inconsequential a distance to matter for greenhouse gas emissions or exercise.

Part of me - a large part - most of me, really - hopes that the processing of that question by that instance of that LLM released more GHG to the atmosphere through marginal power usage than a typical car releases from a round trip to some place 50 meters away.

It's... not exactly a hard trick to learn skepticism

It's not, but it's one that a lot of people never seem to learn, if my social circle is any example.

Claims that skepticism isn't hard to learn seem pretty common, but I'm skeptical of this. My own anecdotal observations have convinced me that it's a slightly harder thing to learn than rocket science and quantum physics combined.

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