| The argument that “LLMs lack judgment because they only guess the next token probabilistically” starts from an overly simplistic model of how human judgment actually forms. Humans also begin as probabilistic next-word predictors.
Look at early language formation in infants: “Mom → food”
“Mom → poop” This is literally a next-token model.
There is no semantics, no reasoning—only repeated patterns, reinforced predictions, and gradual abstraction.
As children grow, they expand the sequence window: “Mom I’m hungry” → “Mom can you go to the store and get the ice cream I like” This is the emergence of abstraction → generalization → specialization,
the exact loop LLMs run internally. Human cognition is biochemical; LLMs are computational.
Different substrate, similar functional loop. And “judgment” is not a mystical faculty.
It can be decomposed into:
1. forming a generalized baseline,
2. comparing specific cases to that baseline,
3. updating through iteration,
4. selecting an output. LLMs do exactly this.
Pretraining forms the baseline,
attention performs comparison,
decoding performs selection. If your definition of judgment is
“access to a global, external truth-frame,”
then humans do not possess judgment either.
For most of history people believed the Earth was flat because their local frame of reference made it the most reasonable inference. Judgment is always local for embedded agents—biological or computational. This is precisely what RCC explains:
LLM failures are not due to “probabilistic prediction,”
but due to embeddedness and partial observability,
the same geometric constraint that applies to humans. The reliability issue is structural, not moral or mystical. |