Modern LLMs don’t fail because they’re “poorly engineered.”
They fail because they are embedded, partially-blind inference systems.
Humans aren’t so different. A newborn doesn’t perform “judgment” either — it simply predicts the next sound (“mom,” “hungry,” “milk”) from limited local cues. Judgment emerges only after countless cycles of abstraction → generalization → specialization → re-generalization.
LLMs do the same thing, just through vector space instead of biology.
So when people say “LLMs rely on probabilities, therefore they can’t judge,” they’re missing the point.
All embedded systems rely on probabilistic inference. Humans included.
We just don’t feel probabilistic because our substrate is biological, not silicon.
The real issue isn’t “lack of judgment.”
It’s the absence of an external reference frame.
No system — human or machine — can guarantee globally correct reasoning when it can see only a fraction of the environment it lives in. RCC simply states this limit in geometric terms:
partial visibility → incomplete models → inevitable failure modes.
LLMs don’t “hallucinate” because they’re sloppy.
They hallucinate because blind inference always produces locally-plausible errors.
Humans aren’t so different. A newborn doesn’t perform “judgment” either — it simply predicts the next sound (“mom,” “hungry,” “milk”) from limited local cues. Judgment emerges only after countless cycles of abstraction → generalization → specialization → re-generalization. LLMs do the same thing, just through vector space instead of biology.
So when people say “LLMs rely on probabilities, therefore they can’t judge,” they’re missing the point. All embedded systems rely on probabilistic inference. Humans included. We just don’t feel probabilistic because our substrate is biological, not silicon.
The real issue isn’t “lack of judgment.” It’s the absence of an external reference frame.
No system — human or machine — can guarantee globally correct reasoning when it can see only a fraction of the environment it lives in. RCC simply states this limit in geometric terms: partial visibility → incomplete models → inevitable failure modes.
LLMs don’t “hallucinate” because they’re sloppy. They hallucinate because blind inference always produces locally-plausible errors.
That’s not a bug. That’s the math.