Noise robustness — GPT-2 medium

CIFAR MNIST Modular addition Language Models Go
GPT-2 XL (1.56B) GPT-2 medium (355M) Pythia-70m (70M) Pythia-410m (405M) Gemma 3 270M (268M)
Overview 1d perturbations 2d perturbations Cells
tokenshost promptembedblock 0block 1…block k…block 23final normunembedlogitsnext tokenL01024 last positionL11024Lk1024 before block k's normfinalreadout, pre-norm not a cut
GPT-2 medium: a cut Lk is the residual stream entering block k, at the last token of the host prompt. L0 is the embedding; the stream after the last block is the final readout, not a cut. The host's other positions are held at their own values at the same depth.
ln_1attentioncausal, 64-d headsln_2MLP4d, gelu_newLk1024 pre-ln_1Lk+11024
GPT-2 medium block: pre-layer-norm, attention then MLP, each with its own addition. The cut is the raw stream before ln_1.
Substrategpt2:24L
Bankopenwebtext(front 50000)[n=4000,ctx=10] host=#0, 4000 inputs
Checkpoints0
PartitionSmooth: no linear regions, so no exact partition.

Activation scale at each cut

cutwidthmedian normmedian radiuscentre / spread
L010243.112.2240.9746
L11024116.735.63.163
L21024123.544.692.629
L31024130.147.762.608
L41024136.454.222.365
L51024143.361.092.181
L61024152.167.182.095
L71024160.872.282.03
L81024168.978.11.962
L91024174.881.821.927
L101024178.687.541.814
L111024184.393.271.743
L121024190.799.731.67
L131024197.6106.61.601
L141024207.3114.41.548
L151024219.5124.21.49
L161024236.3134.81.461
L171024260.9147.81.459
L181024291.4161.71.488
L191024329.3177.51.541
L201024378.5195.61.635
L211024435213.21.761
L221024548.7240.22.066
L231024769.7273.82.588

The median radius is the sphere a rotation rides on and the median norm is what relative displacement is divided by, so both sit in the denominators of every panel below. Centre over spread says how far the activations sit from the origin relative to their own scatter: where it is large, rotating about the origin and about the bank mean are materially different measurements.