Steering Block / Layer Selection

The point

Where and how many sites you intervene on changes both the effective strength of the intervention and whether a localization claim is testable.

• Too few (single site on a deep stack) → downstream norm/attention denoise it → looks like "no effect".
• Too many (whole stack) → off-distribution collapse → the effect is collapse-driven and no longer localized.

Choosing where to intervene

Pick the site(s) by a screening method, by heuristic, or both.

By method (pre-screen the layers):

• Gradient-based — rank layers by gradient / attribution score w.r.t. the target.
• Activation-based — rank by an activation signal (probe accuracy, diff-mean magnitude, separability).

By heuristic:

1. Mid-to-late layers usually work best — start there.
2. If a single layer shows no effect, intervene on 3–5 layers instead.
3. To sweep the whole stack, use spaced intervals (e.g. every 2–3 layers) rather than every layer.

What to intervene on (transformer)

Target the attention, the MLP, or the residual stream — choose by what the claim is about.

Note: circuit discovery generally studies attention heads + MLP.

Two rules to keep it honest

• Match the claim. If the claim is about a region (e.g. "early layers 0–7"), intervene on a window covering that region and use matched windows elsewhere as null controls — a single site can't adjudicate a regional claim. Report the localization you actually observe; don't rewrite the claim to fit the data.
• Never copy a raw index across models of different depth. "Layer 16 of 32" → scale by relative depth (0.5 → layer 24 of 48), then check neighbors.

Composition

Lock the site set here first (the coefficient plateau is site-dependent), then sweep α via ../steering-coefficient-tuning/. Re-sweep the coefficient whenever the site set changes.