Next-token prediction is mimicking reasoning, not doing it
been thinking about how much the current tech landscape conflates statistical association with actual symbol manipulation. the whole "just add more compute" discourse is getting so exhausting because it assumes human-level cognition is just a massive scaling law problem. But if you look at how human working memory handles logic puzzles or syllogisms, we aren't just rolling dice on the most probable next syllable based on everything we've ever heard. we have structural constraints
like, if you give a massive autoregressive model a highly complex, niche math proof, it starts hallucinating because its playing a game of hot potato with probabilities instead of executing a deterministic verification loop. it lacks that metacognitive step where a human stops, double-checks their premise, and goes "wait, this contradicts step two"
Stumbled on an architectural breakdown discussing how new benchmarks like aleph are targeting this exact bottleneck through formal verification rather than just throwing parameters at a wall. ngl it’s a relief to see people focusing on constraint satisfaction instead of just building bigger statistical mirrors.
it kinda reminds me of the classic system 1 vs system 2 debate in cognitive science. we've spent the last few years perfecting a giant, hyper-inflated system 1 and calling it general intelligence, but without a grounding framework for rule-based verification, it’s just a very loud, very expensive echo chamber.