Per-layer transcoders trained on the MLP activations of Ftm23/backdoor-gemma2-2b-2pair-hate-pt, used to build attribution graphs with circuit-tracer.

These are not reproducible from the training code alone in any short run — they are the artifact the attribution results depend on, published so that work can be repeated without retraining them.

Lower is better. The from-scratch transcoders reconstruct this organism's activations substantially better than the off-the-shelf ones at every layer measured.

The attribution runs build a ReplacementModel by splicing: off-the-shelf GemmaScope transcoders for layers 0–22, and the warm/ transcoders here for layers 23–25 — the band where the conjunction resolves into the fire decision. The from_scratch/ set covers 18–22 and is published alongside for comparison and for pipelines that prefer organism-specific transcoders across the whole upper band.

No warm-start validation JSONs were produced for L23–25; the validate_L*.json files accompany the from-scratch set only.