After verifying guided.py: tinymfv `score` is already a debiased logprob
((lp_fwd+lp_rev)/2, BMA'd), not a "raw logit", and `p = softmax(score)`. My
two earlier inventions were both wrong:
- log(p) coupled Authority to the other 6 foundations via logsumexp.
- the diagonal (auth-blame on auth-vignettes) is pmass-on-correct-label =
top1 competence, not the trait, and threw away the FP/FN structure.
Use the library-native readout: auth_nats = log(tinymfv profile p[F]) = log of
the mean p per foundation over ALL vignettes. For small p, log p ~= logit, so
this lands on steering-lite's loading-weighted Δlogit scale (base log(0.099)
=-2.3, real shift ~0.5-2 nats). foundation_nats now reads rep["profile"].
Also:
- run.py: BLUF `main metric:` line with cue ball (🟢/🟡/🔴 by coherence band).
- heal.py: training table to 2 sig figs (nll/kl/loss .2f, gnorm .1f); a
per-step loss does not warrant 3 decimals.
- diag_stages: accept 1+ ckpts, label each row by its reg from metadata.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
The trait metric was taking the diagonal of tinymfv's raw pre-softmax BMA
`score` logit (unnormalised), giving base Authority ~-5 and absurd 8-nat
swings, then comparing those to steering-lite's 0.5-2 nat reference -- which
is a DIFFERENT metric (loading-weighted Delta-logit of binary p(is-wrong)).
Wrong scale, wrong comparison.
Fix: auth_nats = mean log p[authority] on authority-defiance vignettes (the
NORMALIZED choice logprob, the diagonal of the softmax `p`). Base ~log(0.099)
= -2.3, real shifts ~1-3 nats. DRY: evaluate_model now calls foundation_nats.
Also:
- diag_stages: steer at operating point c=0.5 (c=1 collapses coherence to
0.05), add coh_cost = |dCoh|/|dAuth| (coherence lost per nat of behaviour)
to answer "is the adapter a better pareto than raw steering?".
- diag_csweep: drop the bogus 0.5-2 steering-lite anchor; SocialNorms
co-moving with Authority is expected (both binding foundations), not collapse.
- gitignore out/ and results.tsv (experiment outputs, stale schema).
- personas docs (steering-lite proper-pair rules), spec Plans B/C/D, journal.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
Root cause found via diag_axis on 4B: raw mean-diff steered across the 7-layer
band (0.4-0.6) at coeff=1 DESTROYS gemma-3-4b (coherence 1.00->0.02). That
starved the filter to 2 kept completions, so the "adapter" was ~untrained
(2 examples) = base behaviour, my Q1 "promising" read was not validated.
Fixes:
- separate steer_layers (narrow 0.45-0.55) for the vector from layer_range
(broad 0.0-1.0) for the LoRA; they were wrongly coupled
- lower alpha sweep (0.25,0.5,1,2); n_prompts=16
- assert len(kept) >= min_train(20); TINY=2. Don't train on starved data.
- heal training table (loguru+tqdm per token-efficient-logging): step, nll, kl,
loss, gnorm + SHOULD
- full untruncated steer + adapter generation dumps with prompt and
coherence(p_ans_any) inline so we can judge coherence/trait ourselves
NOT yet run with fixes on 4B. Base 4B is Care=0.92 (already aligned) -> the
prompting-baseline confound (Q7) is now the critical check.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
Per user: no iso-KL calibration. Use raw (unnormalised) mean-diff teacher
vector; sweep cfg.alphas at generation and let the FILTER pick usable C
(filter replaces calibration). Default model google/gemma-3-4b-it (1B too dumb;
Authority degenerate there was a model artifact, not a real conclusion).
Token-efficient discriminating logs so each Q is readable:
- Q0: filter table (alpha -> ppl_mean, kept_frac) + low/high-C samples + SHOULD
- Q1: generate from trained adapter (no steering); adapter_ppl vs steered_ppl
under the original + sample + SHOULD (heal = adapter more coherent than steered)
- Q2/Q3: loop summary table (socialnorms/care/coherence/cos_v0 per round) + SHOULD
fast-dev-run green: ppl rises with alpha (3173->4.2M), adapter_ppl<<steered_ppl.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
scripts/diag_axis.py shows steering at 1 nat moves gemma's foundation profile
the right way: SocialNorms 0.68->0.42, Care 0.21->0.33, coherence 0.72->0.88.
Authority is ~0 on this model (no headroom), so:
- eval reports all foundations; trait axis = SocialNorms (down) + Care (up)
- map.html plots Care vs SocialNorms
- add gen_alpha=1.5: over-steer generation into the incoherent regime so the
heal (Q1) has work to do (at 1 nat coherence improved, nothing to heal)
- results.py groups on coherence/socialnorms/care
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
The mean-diff vector is L2-normalised, so p95 KL ~ c^2 and reaching the 1-nat
target needs c ~ O(100). steering-lite's default bracket hi (~16) pinned
c_star at the top (KL ~0.1 << 1.0) on both tiny-random and real gemma. With
bracket=(0.1, 1024) gemma calibrates to c_star=64.03 at p95 KL=1.035.
Also detach div before .item() in heal logging. See RESEARCH_JOURNAL.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
Add the by-question results infra per setup-repo conventions:
- results.tsv append at end of each finished run (config + final metrics + argv)
- scripts/results.py groups by arm (reg) into a markdown table; `just results`
- docs/results.md curated by-question snapshot (U2 regulariser comparison)
- docs/{spec,brainstorming,literature,evidence} structure
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
Setup per setup-repo conventions: uv + justfile + fast-dev-run on
wassname/qwen3-5lyr-tiny-random, package under src/steer_heal (config +
pipeline skeleton). Stages fail fast with NotImplementedError pointing at
the docs/vendor module to port from.
Design in spec.md: distil a steering-lite mean-diff teacher vector (iso-KL
dosed) into a conditioned LoRA, heal incoherency with a KL-rev-to-original
barrier, fold each round via w2schar gated bake, eval on tinymfv. Three
uncertainty gates (filter / heal / iterate) each with a UAT artifact.
Base model google/gemma-3-1b-it (RTX 3090, 24GB). Reference repos vendored
under docs/vendor (gitignored): steering-lite, isokl, tinymfv, w2schar-mini.
The lighter three are editable path deps; w2schar (py3.13 + flash-attn) is
reference-only, we copy its adapter/bake/plot modules.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
wassname