neuron-lens: every neuron and attention head of Qwen3-8B and Gemma 4 12B

A catalog of two models' MLP neurons (two poles each) and attention heads: Qwen/Qwen3-8B (36 × 12,288 neurons, 36 × 32 heads) and google/gemma-4-12B (48 × 15,360 neurons, 48 × 16 heads). Each unit is read from three sides:

• from the weights alone, through two lenses: what a unit writes and what it reads; for a head, what it copies and what it attends to, token by token;
• from its activations over about 15.4M positions of web, Chinese, Python and chat text;
• from the tokens that drive it when swept through real contexts.

Gemma 4 takes images and sound too, so each of its neurons is also read through lenses fitted on images and sounds, and against 49,694 labelled images and 46,932 labelled sounds.

Neurons and heads get the same tables, one row per pole or per head:

table	neurons	heads
reads	lens reads of the write and the read direction	lens reads of the OV and QK circuits, per token
acts	activation statistics and top firings	output-norm statistics, top firings, attention patterns
drivers	the tokens that drive each pole hardest	the tokens that drive each head, as query and as key
legible	which reads hold at the pole's top firings	which reads hold at the head's top firings
labels	Claude Haiku's labels: where it fires, what it writes	where it fires, what it attends to, what it writes
image, audio (Gemma 4)	the patch or sound it reads, its write in media contexts, the images or sounds that drive it, and whether its media label is its text concept too
media (Gemma 4)		how each head attends in image and audio contexts, and which carry what an image or sound shows

config	rows	what
qwen3-8b-neurons-acts	884,736	every pole's activation statistics and top firings
qwen3-8b-neurons-drivers	884,736	every pole's 64 token drivers in real contexts
qwen3-8b-neurons-reads	884,736	every pole's weight-only reads through both lenses
qwen3-8b-neurons-legible	884,736	which reads hold at every pole's top firings
qwen3-8b-neurons-labels	884,736	every pole's labels, by Claude Haiku 5.5
qwen3-8b-heads-acts	1,152	every head's output statistics, top firings and attention patterns
qwen3-8b-heads-drivers	1,152	every head's 64 token drivers, as query and as key
qwen3-8b-heads-reads	1,152	every head's OV and QK circuits read per token
qwen3-8b-heads-legible	1,152	which reads hold at every head's top firings
qwen3-8b-heads-labels	1,152	every head's labels, by Claude Haiku 5.5
gemma-4-12b-neurons-acts	1,474,560	every pole's activation statistics and top firings
gemma-4-12b-neurons-drivers	1,474,560	every pole's 64 token drivers in real contexts
gemma-4-12b-neurons-reads	1,474,560	every pole's weight-only reads through both lenses
gemma-4-12b-neurons-legible	1,474,560	which reads hold at every pole's top firings
gemma-4-12b-neurons-labels	1,474,560	every pole's labels, by Claude Haiku 5.5
gemma-4-12b-neurons-image	1,474,560	every pole's image read and write, the images that drive it, and its cross-modal ranks
gemma-4-12b-neurons-audio	1,474,560	every pole's audio read and write, the sounds that drive it, and its cross-modal ranks
gemma-4-12b-neurons-image-optimised	1,728	the patch that most raises each of the best-labelled poles
gemma-4-12b-neurons-audio-optimised	1,728	the 40 ms of sound that most raises each of the best-labelled poles
gemma-4-12b-heads-acts	768	every head's output statistics, top firings and attention patterns
gemma-4-12b-heads-drivers	768	every head's 64 token drivers, as query and as key
gemma-4-12b-heads-reads	768	every head's OV and QK circuits read per token
gemma-4-12b-heads-legible	768	which reads hold at every head's top firings
gemma-4-12b-heads-labels	768	every head's labels, by Claude Haiku 5.5
gemma-4-12b-heads-media	768	every head in image and audio contexts

The lenses are stdlogits/rs-lenses at revision 2e9c93aa87. Code, methods and findings (cited here as §N) are on GitHub.

The lenses

• The R-lens reads what a vector makes the model say: softmax(W_U · norm(J_L x)), with J_L the averaged Jacobian from layer L to the last layer (for Gemma 4, after its final softcap, 30 · tanh(x / 30)).
• The S-lens reads which input tokens put a vector there: token v scores c · K_L (W_E[v] − mean(W_E)), with K_L the averaged Jacobian from the embeddings to layer L.

Each is fitted at two points of every layer: out, the layer's output, and mid, its MLP input (after attention). out reads MLP outputs and attention inputs; mid reads MLP inputs and attention outputs. Gemma 4's image and audio lenses are fitted the same way on images and sounds.

what	where it is	R-lens read	S-lens read	Gemma 4: image and audio lenses
a neuron's write	layer L's output	write_R	write_S	image.write_R, audio.write_R
a neuron's read direction â ± b̂	layer L's MLP input	read_R	read_S	image.read_png, audio.read_wave
what a head reads	layer L − 1's output		the token's footprint (input of ov_*, qk_*)
what a head writes	layer L's MLP input	ov_R	ov_S

Which reads to trust where

No read works at every depth (§7, §11). For Qwen3-8B (these bands are priors; legible says which reads hold for each unit):

layers	what drives a neuron	what a neuron writes	what a head writes
L0-L7	read_S (literal tokens, raw at L0-L4)	write_S (early neurons write back what they detect); write_R from about L4	ov_S
L8	both	both	ov_S, ov_R
L9-L12, L21-L35	read_R, with read_S_z as a concept-level view	write_R	ov_R
L13-L20	only drivers and acts	write_R, for steering	ov_R (weak)

Gemma 4's map has the same shape at its own depths: read_S through L13 (falling to under 1% at L9 and rising again at L10-L13), write_R from about L33 (for steering from L11), and a dark middle (§13).

Conventions

• Keys. Neuron tables: layer, neuron (coordinate n of down_proj's input, 0-indexed), pole (+1 or −1). Both models' gated activations take both signs, so each neuron has two poles with separate meanings, 24,576 (Qwen3-8B) or 30,720 (Gemma 4) rows a layer, the + poles first. Head tables: layer, head (query head) and kv_head (the key-value head it reads). L8 n344 + is layer 8, neuron 344, pole 1; L20h17 is layer 20, head 17.
• Token lists. <read>_ids are 64 token ids of the model's tokenizer, <read>_val their scores, <read>_str the top 8 decoded and joined by | (newlines as ⏎). Signal holds to rank 64 (§7).
• Kept tokens. Reads cover the tokens seen 5 or more times in the activation corpora, without byte-fallback pieces (token_counts.pt): 103,830 for Qwen3-8B, 123,774 for Gemma 4. Undertrained tokens otherwise dominate early reads.
• _z reads are layer-contrastive: each token's score z-scored across the layer's poles or heads, which removes the hub tokens that top most raw reads. _neg: a head read's bottom.
• Firings. A unit's firing in a document is its strongest position there; its top firings are the 24 documents where that is largest. contexts show the first 5, the firing token in ⟦ ⟧ and, for heads, the token it attends to most (past position 0) in ⟪ ⟫.
• Tests (legible) score a read on the tokens of the unit's own top firings, as a mean z, against mismatched pairs: another unit of the layer scored on the same tokens. _q is the Benjamini-Hochberg q-value; a test passes at q ≤ 0.05. Dark: fires, but no test passes and no single token dominates its firings.
• Scale. A lens read describes a direction, not an effect size; act_top × w_norm is a pole's typical write within this corpus. A write read is the pole's average push, which steering shows; before about L21 of Qwen3-8B, what one firing does depends on its context (§4).
• Gemma 4. A neuron's write is what reaches the residual, s · γ_post ⊙ W_down[:, n] (its MLP output passes a post-feedforward RMSNorm, whose scale the lenses hold constant, and the layer's output is scaled by layer_scalar s); w_norm is its norm. The read direction is the gate and up rows times the pre-feedforward norm's weight. Sliding-window layers have 8 key-value heads of size 256; every sixth layer is global, with one of size 512 whose keys are also its values. A head's write is W_O times the post-attention norm's weight. Every document starts with <bos>, the attention sink, so activation statistics start at the first real token and first_* is at position 1. The base model has no chat template: its chat slice is plain User: / Assistant: turns.

Neurons

<model>-neurons-reads: weight-only reads

columns	read	of
write_R, write_R_z	the R-lens, softmax(W_U · norm(J^out_L w)); _val are probabilities (the logit lens at the last layer)	the write w (Qwen3-8B: ±W_down[:, n])
write_S, write_S_z	the S-lens to the layer's output: tokens whose footprint the write imitates	the write
read_R, read_R_z	the R-lens from the MLP input: what the residual says when it drives the pole	the read direction â ± b̂
read_S, read_S_z	the S-lens to the MLP input: input tokens that drive the pole	the read direction

â and b̂ are the neuron's gate and up rows times the MLP input norm's weight, normalized: â ± b̂ is a heuristic direction for each pole. Also per row: w_norm, cos_ab, null_share (the share of the write in the bottom 1% of singular directions of W_U diag(γ), where entropy neurons write; Stolfo et al. 2024) and vocab_skew, vocab_kurt of W_U · norm(w) (Gurnee et al. 2024's prediction and suppression neurons).

<model>-neurons-acts: activation statistics

Over every document of four slices, up to 256 tokens each, in bf16: DCLM held out from the lens corpus, FineWeb-2 Chinese, codeparrot Python, and UltraChat dialogues (63,990 documents and 15,367,795 positions for Qwen3-8B, 63,992 and 15,409,492 for Gemma 4; docs.parquet).

• act_top: the median of the 24 top firings, the pole's typical high activation; act_max.
• rate: the share of positions past act_top / 4, from exact counts on a quarter-octave grid (linear between its points); rate_<slice> per slice; pos_share: where the pole's sign holds; q50-q999: quantiles of the activation times the pole's sign, from the same counts; mean, std.
• top_doc, top_pos, top_val: the 24 top firings; peak_tokens and the first 5 contexts.
• first_max, first_tokens, first_chat: the first position (left out above, where the attention sink forms), where activations depend on the token alone: the largest over every kept token, the 3 tokens that reach it, and the value on the chat transcript's opening token. Many of Qwen3-8B's L2-L3 poles do their work there (§2).

<model>-neurons-drivers: exact token drivers

Every kept token at position 24 of 8 real documents (4 web, 2 code, 2 chat), averaged over them, bf16: drivers are the 64 tokens with the largest activation at the token itself, drivers_sum summed over it and the 16 positions after. Values are times the pole's sign. For Qwen3-8B, an fp32 check through L9 shared 98-99% of a pole's top 20 on average (95-99% for drivers_sum; §6).

<model>-neurons-legible: which reads hold

write_R (the write's read promotes the tokens that follow the top firings), write_S (it names the peak tokens), read_R, read_S (the read direction's reads name the peak tokens), each with _z and _q; silent (no firing, or act_top under 0.05), cur_mode, next_mode (the share of top firings on one token, or followed by one), dark. Share of each band's poles, Qwen3-8B:

band	silent	write_R	write_S	read_R	read_S	any test	dark
L0-8	19%	0%	11%	2%	35%	36%	29%
L9-12	0%	1%	1%	8%	3%	11%	72%
L13-20	0%	0%	0%	4%	1%	5%	82%
L21-27	0%	12%	2%	6%	5%	19%	59%
L28-35	0%	29%	0%	3%	1%	31%	52%
all	5%	9%	3%	4%	10%	22%	57%

Gemma 4 12B:

band	silent	write_R	write_S	read_R	read_S	any test	dark
L0-11	0%	0%	1%	0%	35%	35%	23%
L12-23	0%	0%	4%	0%	6%	7%	61%
L24-35	0%	8%	0%	1%	4%	11%	66%
L36-47	0%	20%	0%	0%	3%	22%	60%
all	0%	7%	1%	0%	12%	19%	52%

<model>-neurons-labels: a label for every pole

Every pole described by Claude Haiku 5.5 from its reads and top contexts (batch API, effort low, adaptive thinking; §8): fires (where it fires, 12 words or fewer), writes (what its write pushes the model toward when amplified), each with a confidence 0-1, and note. Each model has its own prompt (scripts/prompts/<model>/neurons.md on GitHub), which says which reads hold at each depth, and its cards show only those. Qwen3-8B's labels were made from an earlier build of its tables, whose reads kept byte-fallback pieces; Gemma 4's from this release's, from text alone (its cards show no images or sounds).

Qwen3-8B, checked blind on 100 poles: firing labels separate held-out top firings from quiet positions at AUROC 0.82 [0.78, 0.85] (another pole's label: 0.56); a judge picks a pole's write label out of four from steered text 61% of the time [0.52, 0.69] (chance 25%). Firing labels at confidence 0.7 or more score 0.94. They hold best early (83% of L0-L8's firing labels reach AUROC 0.8, 31% of L21-L27's), and write labels late (80% picked at L28-L35, 41% at L0-L8). Gemma 4's neuron labels are not checked blind yet.

gemma-4-12b-neurons-image, gemma-4-12b-neurons-audio: images and sound

Gemma 4 has no vision or audio encoder network: an image is 64 slots of 48 × 48-pixel patches (384 pixels a side) and a sound 64 slots of 40 ms at 16 kHz, each entering the residual through a few norms and one linear map. One row per pole, beside the text tables:

columns	what
read_S_basis	the read direction through the modality's S-lens to the MLP input, c · K^mid_L: coordinates (fp16) on the S-lens basis
read_png (image)	the 48 × 48 patch the direction reads: read_S_basis through the patch embedder back to pixels, at ±3 standard deviations around grey
read_wave (audio)	the 40 ms waveform it reads, 640 samples at 16 kHz (fp16)
write_R, write_R_z	the write through the modality's R-lens: what it makes the model say in image or audio contexts
label, label_z, label_mean, labels_str	its top labels by the z of its mean activation over each label's documents (labels on 8 or more documents: 1,643 for images, 981 for sound)
sig, stable, auroc_heldout	the top label past the shuffled-label null's 99th percentile for its layer; in the top 5 of both halves of the documents; chosen on even documents, its AUROC on odd ones
top_docs, top_vals	the 16 documents with its largest mean activation (rows of media/<modality>_docs.parquet)
text_fire_rank, text_read_rank, text_write_rank	for a reliable label, its rank among the modality's labels on the text side (1 first, 0.5 chance): firing on the label's words, the text S-lens read, the text R-lens write

A media read sums over a document's 64 slots, so it is one pattern wherever it sits, not a map of where. Read read_png as what the pole's input direction looks like in pixels, a projection: added to held-out images it raises the pole only at L0-L5 (§14). The exemplars say what fires a pole, and the optimised reads below are patterns that drive it.

modality	band	reliable top label	held-out AUROC (median)	its label holds on all three text tests
image	L0-L11	34%	0.96	0% of 50,561 scored
image	L12-L23	26%	0.97	3% of 57,930 scored
image	L24-L35	39%	0.97	2% of 62,387 scored
image	L36-L47	32%	0.97	1% of 82,871 scored
audio	L0-L11	51%	0.91	0% of 44,902 scored
audio	L12-L23	19%	0.86	1% of 33,966 scored
audio	L24-L35	28%	0.83	0% of 89,105 scored
audio	L36-L47	18%	0.78	0% of 64,278 scored

One concept in three modalities (§15). 18 poles pass all three text tests for both their image and their audio label, of 45,138 scored in both (independent lists would share 1.8): L14 n8313 + mosque indoor / Arabic; L15 n146 + coral reef / Maori; L15 n8093 − mosque indoor / Arabic; L15 n10091 − giant panda / Mandarin Chinese; L18 n645 + Arabian camel / Arabic; L20 n13624 + mosque outdoor / Arabic; L21 n12154 + mosque outdoor / Arabic; L23 n2831 + Sarees / Gujarati; L31 n10862 − tacos / Spanish; L31 n12007 − mosque outdoor / Arabic; L33 n5249 + tacos / Spanish; L33 n6099 + pagoda / Mandarin Chinese; L36 n585 − tacos / Spanish; L36 n14109 + totem pole / Maori; L37 n6087 − Pembroke / Welsh; L37 n7165 + giant panda / Mandarin Chinese; L37 n7926 + mosque outdoor / Arabic; L41 n9498 − mosque indoor / Arabic.

gemma-4-12b-neurons-image-optimised, gemma-4-12b-neurons-audio-optimised

For the 48 poles a layer (L0-L35) with the strongest reliable label: one slot pattern, the same in all 64 slots, started from the S-lens read and optimised by gradient ascent through the model (24 Adam steps over 128 images or clips; at most 0.08 of the pixel range, or 0.3 of each frame's RMS). opt_png or opt_wave (in units of a frame's RMS), gain (the pole's mean activation over the slots of 8 held-out documents with the pattern added, minus without), gain_lens (the same for its S-lens read), act_base. Gains are in the pole's activation units:

modality	band	poles	held-out gain, median (its S-lens read)	raises the pole	beats its S-lens read
image	L0-L11	576	0.195 (-0.0015)	95%	96%
image	L12-L23	576	0.00593 (-0.00066)	70%	73%
image	L24-L35	576	0.00102 (-5.4e-05)	56%	56%
audio	L0-L11	576	0.361 (0.0061)	99%	99%
audio	L12-L23	576	0.017 (0.00017)	86%	90%
audio	L24-L35	576	5.57e-05 (-3.4e-05)	51%	52%

The audio test clips are among the audio S-lens's fitting clips (all of ESC-50), though not the optimisation's.

Heads

A head reads token v as its S-lens footprint at layer L − 1's output (the embedding at L0) and writes into layer L's MLP input. Its OV circuit maps what it reads to what it writes, and its QK circuit decides where it reads, both over the whole vocabulary (§11).

<model>-heads-reads: weight-only reads, per token

columns	read
ov_R, ov_R_neg	each token's z in its own column of the OV map read through the R-lens: where v ranks in what the head makes the model say when it reads v. The tokens it copies most, and suppresses most
ov_S, ov_S_neg	the same through the S-lens: whether the write makes the residual look as if v were there (the record through L8)
qk_S, qk_S_neg	each key token's z over the vocabulary under the head's mean query: what it attends to and avoids, whatever the query
qk_self_S	each token's z in its own row of the QK map: where its own token ranks among the keys it favours as a query
ov_R_copy, ov_S_copy	Elhage et al.'s copying score Σλ/Σ|λ| of each OV map: +1 copies what it reads, −1 suppresses it
qk_self	the median qk_self_S z over tokens: duplicate-token heads score high
qk_ctx_rho	Spearman over tokens between qk_S and the attention each token receives in context
qk_ov_rho	Spearman over tokens between qk_S and ov_R: does the head attend to what it copies?

The QK map is q̃ᵀ R̄ k̃, with the q/k norms' true derivative averaged over real positions and RoPE averaged over the head's own attention offsets, footprints at unit length: it reads which tokens a head favours, not position-only behaviour such as previous-token or induction attention.

For Qwen3-8B, qk_self finds duplicate-token heads at AUROC 0.96. qk_S agrees with the attention tokens receive in context (qk_ctx_rho past +0.2) for 36% of L0-L8's heads (another head's read: 14%) and 29% of L28-L35's (4%); 12% of L28-L35's heads attend to what they copy (qk_ov_rho past +0.2; another head's OV read: 1%). For Gemma 4, at AUROC 0.95.

<model>-heads-acts: firings and attention patterns

Over the same documents, from position 16 (a head with few keys attends sharply):

• out_top, out_max, rate, rate_<slice>, q50-q999, mean, std: as for neurons, of the norm of the head's output as it reaches the residual (‖W_O^h z‖; for Gemma 4, after its post-attention norm);
• top_doc, top_pos, top_val, src_pos (the position each firing attends to most, past the first), peak_tokens (query tokens), src_tokens (attended tokens), contexts;
• sink, prev, self: the mean attention from queries at 16+ to the first position, to the token before and to itself; induction, duplicate: over 64 sequences of 64 random tokens said twice, attention from the second copy to the token after the first occurrence, and to the occurrence;
• out_R_z: the firings' outputs read through the R-lens from the MLP input, each token's logit z-scored across the layer's firings, averaged over the head's.

<model>-heads-drivers: exact token drivers

In the same pass as the neurons': drivers, the 64 tokens with the largest output norm at their own position (the token as the query), and keys, the 64 that receive the most attention from the 16 positions after them (the token as the key).

<model>-heads-legible: which reads hold

At each top firing, with the attended token: ov_R (its OV column promotes the token after the query), ov_copy (its own ov_R z: the head promotes what it reads), ov_S (its own ov_S z), qk_S (its qk_S z: the QK read predicts what the head attends to), each with _z and _q (Benjamini-Hochberg over all heads); cur_mode, src_mode; pattern (prev or self past 0.5, induction or duplicate past 0.3); dark: no test, no mode past a third, no pattern. Share of each band's heads, Qwen3-8B:

band	ov_R	ov_copy	ov_S	qk_S	pattern	any test	dark
L0-8	1%	1%	0%	0%	10%	2%	41%
L9-12	0%	1%	0%	0%	8%	1%	78%
L13-20	1%	0%	0%	0%	8%	2%	74%
L21-27	5%	1%	0%	0%	6%	5%	61%
L28-35	27%	10%	0%	0%	4%	27%	38%
all	7%	3%	0%	0%	7%	8%	56%

The S-side tests pass for almost no head. S→S copying and the mean query's favourite keys are shared by a layer's heads, so at a head's own firings they don't tell it from its neighbours; they remain reads of what each head does (§12).

Gemma 4 has 16 heads a layer, so a head's mismatched control has 240 pairs, no p-value falls below 1/241, and BH over its 768 heads passes none. Its table adds <test>_p, the uncorrected p-value. Share of each band's heads at p ≤ 0.05 (5% by chance):

band	ov_R	ov_copy	ov_S	qk_S	pattern	dark
L0-11	4%	7%	4%	9%	15%	19%
L12-23	4%	10%	3%	6%	9%	70%
L24-35	8%	9%	4%	6%	6%	70%
L36-47	21%	17%	4%	14%	8%	42%
all	9%	11%	4%	9%	10%	50%

<model>-heads-labels: a label for every head

Qwen3-8B:

Every head described by Claude Haiku 5.5 from its card (batch API, effort low, adaptive thinking): fires (where its output is large), attends (what it attends to there) and writes (what its output promotes), each with a confidence 0-1, and note. The cards show the head's pattern scores, output scale, OV and QK reads, drivers and first 5 top contexts; nothing about its other top firings. The prompt is scripts/prompts/qwen3-8b/heads.md on GitHub.

Checked blind on 100 random heads, 20 a band, by Claude Sonnet 5.5:

• Firing: given a head's fires and attends labels and 8 contexts, 4 of its held-out top firings (ranks 6-24) and 4 of another head of its band, the scorer separates them at AUROC 0.71 [0.65, 0.77]; a third head's labels on the same contexts score 0.51.
• Writes: given what the head's firings actually promote in context (out_R_z, not on the card), a judge picks its writes label out of four from its band 49% of the time [39, 60] (chance 25%).

band	firing AUROC	control	writes picked
L0-L8	0.74	0.50	33%
L9-L12	0.70	0.61	56%
L13-L20	0.79	0.36	25%
L21-L27	0.60	0.56	55%
L28-L35	0.73	0.51	79%

Gemma 4 12B: its write labels are at chance, so read them as guesses.

Every head described by Claude Haiku 5.5 from its card (batch API, effort low, adaptive thinking): fires (where its output is large), attends (what it attends to there) and writes (what its output promotes), each with a confidence 0-1, and note. The cards show the head's pattern scores, output scale, OV and QK reads, drivers and first 5 top contexts; nothing about its other top firings. The prompt is scripts/prompts/gemma-4-12b/heads.md on GitHub.

Checked blind on 80 random heads, 20 a band, by Claude Sonnet 5.5:

• Firing: given a head's fires and attends labels and 8 contexts, 4 of its held-out top firings (ranks 6-24) and 4 of another head of its band, the scorer separates them at AUROC 0.71 [0.64, 0.79]; a third head's labels on the same contexts score 0.47.
• Writes: given what the head's firings actually promote in context (out_R_z, not on the card), a judge picks its writes label out of four from its band 22% of the time [13, 30] (chance 25%).

band	firing AUROC	control	writes picked
L0-L4	0.62	0.44	25%
L5-L13	0.80	0.49	15%
L14-L32	0.75	0.52	32%
L33-L47	0.69	0.41	15%

gemma-4-12b-heads-media: heads in image and audio contexts

64 labelled images (mini-ImageNet) and 64 sounds (ESC-50, the classes in turn), each in the media prompt and followed by a <label>, its own and another class's (§16). Per head and modality: read, read_ratio (attention from the text after the media to its slots, and that over an even spread), sink, self, within, nbr (attention from a slot to itself, the other slots, and its grid neighbours or the frame before); abl_right, abl_wrong, content, content_z (each head zeroed: the change in the label's loss, right and wrong, and their difference); write_mid, write_logit and their _q (the head's output at the article read through the R-lens and the logit lens: the right class's mean percentile among the classes, against 200 label shuffles); top1_mid, top1_logit.

• image (64 documents, 64 classes): the right label's loss is below a wrong one's for 97% (3.64 against 10.29 nats). Most read from the text: L24h13, L44h7, L31h3, L23h8; largest extra loss when zeroed (nats): L8h15 (0.75), L7h13 (0.65), L44h7 (0.43), L38h6 (0.42); top label writers: L37h10, L44h7, L40h6, L46h4.
• audio (64 documents, 49 classes): the right label's loss is below a wrong one's for 55% (6.48 against 6.56 nats). Most read from the text: L8h5, L7h9, L3h6, L4h0; largest extra loss when zeroed (nats): L9h4 (0.06), L4h8 (0.06), L11h1 (0.06), L10h15 (0.06); top label writers: L47h4, L45h11, L32h4, L30h13.

Share of each band's heads that cost the right label more than a wrong one at z > 3, and that write the right class at q ≤ 0.05, images: L0-11 4%, 4%; L12-23 12%, 43%; L24-35 29%, 41%; L36-47 8%, 36%. For sound, no head passes either.

Plain files

Per model folder (qwen3-8b/, gemma-4-12b/):

• docs.parquet: each activation document's slice, its line in the slice's file and its length. The slices' sources, revisions and row ids are in data/corpus/act-<slice>.provenance.json on GitHub: the corpus is not re-hosted here.
• token_counts.pt: counts[v], how often token v occurs in the activation corpora (by_slice per slice).
• causal/causal.pt: the causal sample (§3, §13). picks: 1,194 Qwen3-8B neurons (layer, neuron), 32 random per layer plus named ones, or 1,152 of Gemma 4's, 24 random per layer. Per neuron and pole: steer [neuron, pole, dose, vocab], the mean change in log-probs over positions 16-63 of 16 64-token DCLM contexts (contexts) when the write is added there at 1× and 4× alpha (the pole's typical high activation); ablate_pos and ablate_win, clean − ablated log-probs with the neuron zeroed at its peak in 8 top documents, at that position and summed over the next 8; dlogp_next, the next token's change per document; fired, the activation there. bf16.
• gemma-4-12b/media/{image,audio}_docs.parquet: each image or sound document's source, labels, caption, ref (JSON: the dataset, config, split and revision, the row's own identifiers, the media file's path and its bytes' SHA-1) and link. The images and sounds are not re-hosted; data/corpus/media-<modality>.provenance.json on GitHub lists every source with its license.

Loading

import pandas as pd

base = "hf://datasets/stdlogits/neuron-lens/qwen3-8b"
reads = pd.read_parquet(f"{base}/neurons/reads/L08.parquet")
reads.query("neuron == 344 and pole == 1")[["write_R_str", "read_R_str", "read_S_str"]]
heads = pd.read_parquet(f"{base}/heads/reads.parquet").merge(pd.read_parquet(f"{base}/heads/acts.parquet"))
heads.query("layer == 20 and head == 17")[["ov_R_str", "qk_S_str", "duplicate", "contexts"]]

base = "hf://datasets/stdlogits/neuron-lens/gemma-4-12b"
image = pd.read_parquet(f"{base}/neurons/image/L33.parquet").query("neuron == 6099 and pole == 1").iloc[0]
docs = pd.read_parquet(f"{base}/media/image_docs.parquet")
image[["label", "labels_str", "text_fire_rank"]], docs.loc[image["top_docs"][:4], ["labels", "link"]]

Limits

• Two models; the text lenses are fitted on English-heavy web text, Gemma 4's media lenses on COCO photos and ESC-50 sounds.
• Activation statistics come from bf16 passes over documents of up to 256 tokens; top firings are one per document.
• The tests are token-level, over at most 24 firings per unit. A dark unit may still be describable from its contexts (§7).
• Heads are read through their circuits one token at a time: a head whose role depends on the query's context, or on position alone, is read only through acts.
• Media labels name what a whole image or sound is about, not each patch or frame; media reads lose fidelity with depth (§14).

License

CC BY 4.0. The catalog is derived from Qwen/Qwen3-8B and google/gemma-4-12B (both Apache 2.0). Its contexts quote short excerpts of the activation corpora, which keep their own terms: DCLM (CC BY 4.0), FineWeb-2 (ODC-By 1.0), codeparrot-clean (code from public GitHub repositories, under their licenses) and UltraChat-200k (MIT). The media documents' labels and captions come from their source datasets, under those datasets' terms (some non-commercial); the images and sounds themselves are only linked. By @_a9lim and @treetowntree with Claude (Anthropic), 2026-10-31.