Model Steering — Proving Causation

Steering is the definitive proof of your research. By manipulating specific features during generation, you demonstrate that a feature causally influences model behavior — not just correlates with it.

Steering Modes

miStudio provides three distinct steering modes:

Mode	What It Does	Best For
Individual	One feature at multiple strengths	Understanding a single feature's dose-response curve
Comparison	Multiple features at the same strength, side-by-side	Comparing related features (e.g., "French" vs "German")
Combined	All selected features applied simultaneously	Discovering synergistic effects between features

Strength Values

Steering strengths are raw coefficients added to the model's residual stream, compatible with Neuronpedia's scale:

Range	Effect	Example
0	No intervention (baseline)	Unsteered output
0.07 – 5	Subtle influence	Slight shift in topic or tone
5 – 50	Moderate effect	Clear behavioral change
50 – 100	Strong effect	Dominant feature influence
100 – 200	Very strong	Feature overwhelms other signals
200 – 300	Extreme	Often causes repetition or collapse
Negative	Suppression	Inhibits the feature's concept

Strength Calibration

The effective range depends on the SAE and layer. Start with strengths around 5–20 and increase gradually. Values above ±100 frequently cause the model to "collapse" into repetitive or incoherent output.

Multi-Strength Testing

Each feature supports up to 3 additional strengths tested simultaneously. Set a primary strength and additional values to see the dose-response curve in one generation pass. For example: primary=10, additional=[5, 20, 50].

Generation Parameters

Parameter	Default	Range	Effect
Max Tokens	100	1–2,048	Length of generated text
Temperature	0.7	0–2.0	Randomness. 0 = deterministic, 1.0 = creative
Top-P	0.9	0–1.0	Nucleus sampling threshold
Top-K	50	0–500	Vocabulary restriction per token
Repetition Penalty	1.15	0.5–2.0	Penalizes repeated tokens. Increase if output loops.
Seed	—	Optional	Set for reproducible results across runs

The Matrix Testing Workflow

Unlike tools with a single slider, miStudio uses a grid approach:

1. Select Features: Add up to 4 features (e.g., "Honesty" + "French Language")
2. Add Prompts: Multiple test prompts processed in batch
3. Configure Strengths: Set primary and additional strengths per feature
4. Include Baseline: Toggle "Include Unsteered" to compare against the natural output
5. Execute: miStudio runs all combinations, presenting results in a structured comparison view

Combined Mode Synergies

Steering multiple features simultaneously is NOT the same as running them separately. "Scientific Tone" + "Excitement" combined may produce different text than either alone. Combined mode reveals circuit behavior where features interact.

Viewing Results

After execution, results are presented in a structured comparison view showing baseline and steered outputs side-by-side with perplexity metrics:

Multi-layer circuits — steering across SAEs

Everything above steers features from one SAE at one layer. A discovered circuit, though, spans layers — its members live on the layers each was found on, and each layer has its own SAE. Feature 015 makes steering follow the circuit: every member steers through the SAE trained on its own layer, in a single generation.

The own-layer rule

Each feature carries the SAE it belongs to. When you load a multi-layer circuit into the steering panel, the members arrive with their per-layer SAEs already set — an L13 member steers through the L13 SAE, an L14 member through the L14 SAE, in the same Blended run. You cannot steer an L14-trained feature at L10: that's rejected with a clear message naming the offending member, never quietly served through the wrong decoder (which would inject a direction from the wrong layer's basis — the bug this feature exists to fix).

Per-layer budgets, one dial

The budget model runs independently per layer — each layer gets its own budget bar (total budget B, cohesion gain G, similarity-weighted allocation) computed against that layer's SAE. One global intensity dial (λ) scales the whole circuit at once, so you tune the entire cross-layer behaviour with a single control while each layer keeps its principled starting strengths. The applied-features summary groups members by layer so you can see, and verify, that each one steered through its own SAE.

Hazard warnings — compounding and cancellation

Steering an upstream feature that drives a downstream feature you're also steering makes their influences compound (or, with opposite signs, cancel). miStudio surfaces this before you generate — an amber banner listing the pairs — but never silently corrects it; you decide.

The warning is as strong as the evidence:

• If the circuit has a validated edge between the pair (a rung-2 edge that survived causal validation), the warning is quantified from the measured effect size — "validated edge, ES=0.8 — combined influence ≈ higher than the naive sum."
• If there's no validated edge, a weight-prior heuristic (how aligned the upstream feature's output direction is with the downstream feature's input direction) still warns — but every such warning is explicitly labeled heuristic, never presented as causal. It's a hint to check, not a proven mechanism.

This is the payoff of the whole circuits arc: the same validated evidence that earns a circuit its rung-2 badge is what makes the steering hazard warning trustworthy instead of a guess.

VRAM

Each distinct SAE the circuit references loads onto the GPU (~130 MB for an 8k-feature SAE). A typical two- or three-layer circuit adds well under a gigabyte; only the SAEs the circuit actually uses are loaded, and exiting steering mode frees them all.