Operator self
addressing operator (universal/addressing — measured across all models in the catalog).
Cross-model (catalog row) — signal/causal are mean ± σ over 3 probe-resample seeds
| model | arch | signal (±σ) | #heads | top head | depth | causal ΔNLL (±σ) |
|---|---|---|---|---|---|---|
| gpt2 | GPT-2/absolute | 0.839 ± 0.001 | 10 | 0.1 | 0.00 | +0.024 ± 0.010 |
| gpt2-medium | GPT-2/absolute | 0.447 ± 0.000 | 21 | 5.13 | 0.22 | +0.033 ± 0.008 |
| gpt2-large | GPT-2/absolute | 0.549 ± 0.003 | 74 | 0.14 | 0.00 | +0.012 ± 0.002 |
| gemma-2-2b | RoPE | 0.966 ± 0.001 | 154 | 25.7 | 1.00 | +0.476 ± 0.027 |
| Llama-3.2-1B | RoPE | 0.956 ± 0.001 | 60 | 15.14 | 1.00 | +0.103 ± 0.003 |
| Qwen2.5-1.5B | RoPE | 0.999 ± 0.000 | 43 | 15.7 | 0.56 | +3.733 ± 0.059 |
SAE-feature operands (section G)
What this operator reads/writes in feature space (monosemantic SAE latents), via the per-layer GPT-2 SAEs / Gemma Scope — see the full SAE-operand table. READ = dominant key-feature where the head attends (glossed by top tokens); copy-score = OV→unembed on those tokens (+ copies / − suppresses). Provisional, single corpus; for positional/addressing ops the read-feature is incidental.
| model | head | reads (SAE feature) | copy-score |
|---|---|---|---|
| gpt2 | 0.1 | _you; US; _the |
+0.07 (copies) |
| gemma-2-2b | 25.7 | ▁the/⏎⏎/.; First; ▁the/▁your/▁own |
-0.21 (suppresses) |
Data: runs/disassembly/operators/dossiers/self/ + the catalog. Regenerate: operator_catalog_doc.py.