aletheia-tararesearch-sttp

StTP steering vectors — TaraResearch (Aletheia's Quest, white-box track)

Steering-to-Target-Projection (StTP) honesty vectors used by our white-box deception detector white_m. Each .safetensors file holds the per-(base model, op-point layer) aggregates needed at inference — no raw training data or per-example activations.

Files

Schema (per file)

• v_hat — normalized honesty direction at the op-point layer, shape [1, H]
• threshold — gating threshold θ (which tokens get steered)
• mu_pos / std_pos — projection mean/std of the honest ("high trait") class
• mu_neg / std_neg — projection mean/std of the dishonest ("low trait") class
• metadata: steering_direction="positive", threshold_mode="bias", default_coefficient, fit notes

How they were fit

Per (base model, layer): logistic regression on response-averaged hidden activations of an honesty self-report contrast set (~50 scenarios, judge-filtered for clean honest/dishonest pairs), producing v_hat, θ, and per-class mu/std. The op-point layer was chosen by a per-base layer sweep (l20–40 for gemma/Qwen, l40–50 for Nemotron) trading honesty vs. output coherence.

How white_m uses them (inference)

During honest-reference generation, StTP edits hidden states at the op-point layer: project onto v_hat; for tokens on the dishonest side of θ, snap the projection to s = mu + coefficient·std, leaving the orthogonal subspace untouched. white_m uses coefficient 30, positions="all"; on gemma only, an activation-sink gate (sink_k=8) excludes attention-sink outlier tokens from steering to keep the reference coherent. The steered reference is then scored by a Qwen3.5-9B judge.

Method: StTP (Steering to Target Projection), internal. Trusted judge: Qwen/Qwen3.5-9B.