Polysemanticity can persist without superposition

active tested empirical positive falsifiable
v1 · Initial seed from Anthropic 2023 monosemanticity paper

summary
Models prefer polysemantic neurons even when monosemantic solutions are available, because polysemantic representations achieve lower cross-entropy loss.
Anthropic (2023) showed that training models with 1-hot activations (eliminating superposition) does not eliminate polysemanticity. Models achieve better performance by making neurons polysemantic even when there is no superposition pressure.
trust profile
dimensions
evid
81%
repl
30%
cons
100%
meth
100%
cred
0%
scop
100%
brdg
0%
cont
0%
derived scores
supp
66%
fron
28%
stab
72%
claim_support_vector v1.0 · 2026-03-09 13:27 UTC
evidence 2
↑ supporting 1
supports · artifact
Towards Monosemanticity (Anthropic, 2023)
The Anthropic 2023 paper provides empirical support for the claim.
Towards Monosemanticity (Anthropic, 2023) · 81% — Support link from results section
• asserting 1
asserts · artifact
Towards Monosemanticity (Anthropic, 2023)
The Anthropic 2023 paper asserts the claim that polysemanticity can persist without superposition.
Towards Monosemanticity (Anthropic, 2023) · 86% — Direct extraction from paper discussion
evidence bundles
Evidence for polysemanticity-without-superposition
weighted_sum
Polysemanticity can persist without superposition → holds_in w=0.93
Towards Monosemanticity (Anthropic, 2023) → supports w=0.81
scope
holds_in Transformer cross-entropy training training_regime
neighborhood 1
holds_in Transformer cross-entropy training context
attestations
Curator (Human) verifies 0.9
Claim wording acceptable for scoped empirical use.
domains
Mechanistic Interpretability 100%
view status
Strict Empirical included
computation trace
show raw trace data
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