SAE-feature operands per operator
The token-operand catalog says which tokens an operator binds; this says which monosemantic SAE features it reads, and whether its OV copies that content (+) or suppresses it (−) — the dossier’s section-G layer. READ = the attention-weighted dominant key-feature (the SAE feature most present where the head attends; content-filtered, glossed by top tokens). copy-score = the OV→unembed diagonal on that feature’s own tokens. Provisional, single corpus (Shakespeare prose); _ = leading space.
Only content / circuit operators bind on content; positional / addressing ops (prev-token, local, sink, self) attend by position, so their read-feature is incidental — the copy-score column is load-bearing.
gpt2 — jbloom/GPT2-Small-SAEs-Reformatted
| operator | head | kind | reads (SAE feature) | copy-score (OV) |
|---|---|---|---|---|
duplicate |
0.5 | content | _you; _the; US | +0.09 (copies) |
self |
0.1 | addressing | _you; US; _the | +0.07 (copies) |
structural |
3.1 | structural | _your/Your/_Your; The; _it/it | -0.03 (≈neutral) |
prevtok |
4.11 | positional | US; cius/ius; I | +0.10 (copies) |
local |
4.11 | positional | US; cius/ius; I | +0.10 (copies) |
induction |
5.5 | content | First; What/_What/_what; US | +0.09 (copies) |
sink |
7.2 | addressing | US/us; _Citizen/_citizens; I | +0.00 (≈neutral) |
s_inhibition |
7.3 | circuit | US/us; MAR/MEN/CI; _you/you/You | +0.01 (≈neutral) |
name_mover |
9.6 | circuit | _Citizen/_citizens; MEN/_men/_Men; _it/’d/_are | +0.04 (copies) |
backup_name_mover |
9.0 | circuit | _it/’d/_are; _Citizen/_citizens; US/us | +0.03 (copies) |
coreference |
9.0 | circuit | _it/’d/_are; _Citizen/_citizens; US/us | +0.03 (copies) |
negative_mover |
10.7 | circuit | ‘d/_not/_and; And/That/_and; _Citizen/_citizens | -0.01 (≈neutral) |
gemma-2-2b — Gemma Scope (gemma-scope-2b-pt-res, JumpReLU)
| operator | head | kind | reads (SAE feature) | copy-score (OV) |
|---|---|---|---|---|
duplicate |
1.4 (SAE L0, head L1) | content | cius/▁belly/VIR; ▁the; UMN/GIL/▁Cai | -0.15 (suppresses) |
sink |
0.3 | addressing | cius/▁belly/VIR; UMN/GIL/▁Cai; ▁Citizen | +0.10 (copies) |
local |
0.0 | positional | cius/▁belly/VIR; UMN/GIL/▁Cai; ▁the | +0.02 (≈neutral) |
induction |
6.3 | content | First; ▁gods/▁run/▁petition; Before | +0.06 (copies) |
prevtok |
21.7 | positional | ▁Citizen/./▁belly; ▁the/▁a/▁to; First/▁first | -0.10 (suppresses) |
self |
25.7 (SAE L24, head L25) | addressing | ▁the/⏎⏎/.; First; ▁the/▁your/▁own | -0.21 (suppresses) |
structural |
24.6 | structural | ▁the/⏎⏎/.; First; ▁with/With/▁With | +0.11 (copies) |
GPT-2 has all-layer SAEs (exact per-head layer); Gemma Scope is 8 layers so each Gemma op uses its nearest available SAE layer (offset ≤1, annotated). Gemma’s read-features come out noisier than GPT-2’s — its heads put heavy attention on <bos>/structural tokens on this non-repetitive prose, so the dominant *content* key-feature is weaker (a corpus + attention-budget effect, not a tooling one); the copy-score still uses the head’s exact OV. The cached Qwen SAE is for qwen2-0.5b (a different model). Data: operator_sae_operands_summary.json. Regenerate: operator_sae_operands.py.