Regenerated over 7 methods with the dual-gate edge. Finding is NEGATIVE: none flip
the deliberated YES/NO within the answer-alive budget (swings noise-level <=0.06 vs
random 0.03, baseline 0.107; promoted tokens are junk not lie/honest). word's 0.97
swing is a dead-answer artifact (ans_mass 0.14), correctly nulled by score. Notebook
adds the sig-fig comparison table and a per-method qualitative cell authored from the
real generations.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
One generically-named notebook (replaces persona-named ones): loads model+lens once,
builds all 7 steering vectors (word/persona_vector/topk/soft/pinv/meandiff/random-null)
with the j-thoughts lens readout, then loads precomputed results and shows a per-method
generation dropdown + comparison table.
Heavy 7-method sweep (~18 min) runs headless via scripts/scratch/run_steering_demo.py ->
artifacts/steering_demo_results.json, because marimos single-threaded kernel makes a long
in-cell compute un-monitorable (any status poll interrupts it). Notebook loads the JSON so
it renders instantly. Result (dilemma, P(YES=lie)): flat ~0.03-0.14 at every methods
coherent edge vs 0.107 baseline; persona_pinv widest window (C*+ = +1.19). Includes the
GPT-5.6-terra comment review (docs/reviews/).
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
Dual-gated (fluent trace AND committed answer) measurement of all 7 methods on the
self-honesty dilemma. Once artifacts are excluded, aggressive steers break the YES/NO
answer format before moving the decision; persona_pinv is the only method that stays
coherent+committed across a wide C window with a modest real P(lie) rise (0.07->0.15),
consistent with it having the widest coherent window on the optimism axis too.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
Which-steering-works findings for wassname: persona_topk is the clean bidirectional
winner (manual read + repetition metric agree); the old rubric mislabeled it because its
coherence gate was on a short forced object. Plus the simplify-to-v3 plan and the
moral-dilemma demo-content proposal. Evidence: artifacts/eval_mechanisms.txt.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
Run 551 was OOM-killed at n_done=36: the user's VS Code Jupyter kernel
(jsteer venv, PID 3214401) co-loaded ~1.5GB VRAM + 1.9GB RAM while the fit
sat at the 22.4/24.6GB ceiling. Clean SIGKILL with no CUDA traceback = host
OOM killer, not a CUDA OOM. dim_batch=8 halves the fit's peak footprint;
it changes only the backward schedule, not the accumulated Jacobian, so U4
exactness is preserved. Resumes from checkpoint (n_done=36), lossless.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
