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#reasoning#traces#same#tokens#models#noticed#llm#model#something#why
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Discussion (22 Comments)Read Original on HackerNews
Every field has terms of art, and 'reasoning' is one for LLMs. But that doesn't mean it has the same properties as 'reasoning' in other contexts, because you're not referring to the same thing.
Why doesn't my asteroid belt buckle?
> Our findings consistently challenge the prevailing narrative that intermediate tokens constitute a semantically meaningful reasoning process. First, we observe a pronounced lack of correlation between solution correctness and trace validity—models frequently produce invalid reasoning traces even when they arrive at correct solutions. Second, and more strikingly, models trained on corrupted or semantically irrelevant traces achieve performance comparable to, and often exceeding, that of models trained on correct traces, especially on out-of-distribution tasks.
[1]: https://arxiv.org/abs/2504.09762
It is annoying when the bot seems be “reasoning” correctly and then makes an obvious mistake at the end. And perplexing when it seems to be completely wrong and then pull the right answer out of a magic hat at the end.
I guess it makes sense; the “reasoning” steps aren’t actually doing logic, just adding more context to influence the final generation, right? But it is weird to see.
> models often determine their answers based on implicit biases tied to question templates, then construct reasoning chains to justify their predetermined conclusions > its reasoning was correct right until the final step (Yes/No answer)
It's a relevant comment in this instance because we're discussing concepts you need to be both trained and practiced in to reason about, and that our discipline has traditionally been blind to. Plenty of people working with LLM context issues who've never been exposed to the idea of 'subtext' or could tell you why it would matter to their direction of effort.
You might not've noticed but we aren't talking about natural intelligence.
From March last year: https://transformer-circuits.pub/2025/attribution-graphs/bio...
There's no reason to believe the model's self-reported "thinking" bears any relation to the mechanics by which it arrived at some output.
if not, then there's certainly some bearing, but not necessarily in how we read the tokens as text