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GPT-2 (small) — per-head disassembly

GPT-2 / absolute-position. GPT-2 disassembly (12 layers x 12 heads + MLP). first-order; operand basis nt=40. ADDR=where-to-read WRITE=copy/transform (OV) bind=top content binding (B_h) role=circuit (shared mean-write ‘default’ direction omitted — see write_bus_check)

Operator roles referenced (hyperlinked inline below): induction · prevtok · structural. Full raw listing: gpt2_disassembly.txt. See the operator catalog for what each role means.

Discovery pass (causal overlay). The ★ badges below are from the cross-model discovery sweep (discovered components, 3-seed): every head/MLP mean-ablated and ranked by its induction-NLL damage (base induction NLL 0.50). A head is flagged ⚠ UNNAMED-candidate when it is load-bearing but matches no catalogued operator — a lead to dossier. 2 unnamed load-bearing here: 0.9, 0.8. Only the sweep’s top-ranked components carry a badge (most heads are not individually load-bearing).

First-order, single-component reads (+ the induction idiom); provisional. Each head line: head · ADDR (where it reads) · WRITE (copy/transform) · top content binding · operator role · ★ discovery-pass causal (when load-bearing). Lines like L.MLP.n#### are MLP neurons (the COMPUTE class — n#### is the neuron’s index in that layer’s gated-MLP intermediate dimension, e.g. Gemma-2-2B has 9216/layer), not attention heads; each lists the top read-tokens → write-tokens (the layer’s most salient few).

Layer 0

Layer 1

Layer 2

Layer 3

Layer 4

Layer 5

Layer 6

Layer 7

Layer 8

Layer 9

Layer 10

Layer 11

Generated from the committed listing + discovery sweep by disassembly_pages.py.