Unverified70% confidenceFactExact time
The universal approximation theorem is a purely existential conclusion that guarantees the existence of an approximation scheme but provides no tight upper bound on the number of neurons required.
2
Sources
70%
Confidence
Long-term
Relevance
8/21/2026
First Seen
Sources
Related Claims
UnverifiedSuperposition theory holds that neurons are insufficient, so the network stuffs unrelated features into the same neuron; this theory was once the mainstream hypothesis in the interpretability field.73% similarUnverified连结主义不追求精确函数，而是通过寻找足够接近的近似解来解决问题73% similarUnverifiedLatent space reasoning lacks natural supervision signals—there is no ground truth to determine whether a given hidden vector represents correct thinking—which remains an engineering challenge that has not been fully resolved.71% similarUnverifiedThe study found that the length of generated reasoning traces is largely independent of the difficulty of the underlying problem.71% similarUnverifiedThe universal approximation theorem only guarantees the existence of such a network; it does not tell how to efficiently find the parameters, nor does it guarantee generalization ability.71% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/784078API
curl https://kongchang.com/api/v1/knowledge/claims/784078MCP
get_claim(id=784078)