gen_filter_walk: per round, cool a steering multiplier kappa and top up with
extra gen batches until min_train coherent survivors are banked, so the loop
cannot starve on data count (#90/#100 died at the min_train assert). Paired
#101 (walk-C ON) vs #100 (walk-C OFF, identical config): #101 reaches round 9
where #100 asserted at round 5.
Finding (journal h): walk-C removes the starve CRASH but the real ceiling is
coherence collapse, not data count. Trait over-drives to auth -6.8 while coh
falls 0.99 -> 0.62 and the kept completions degenerate into token loops
("BUILDUTEutive...", "GLUTE GLUTE") by round 7 -- low-entropy so they slip
under ppl_tau and rep_tau and train the next adapter on garbage. Coherent
deliverable is the round 1-2 adapter (auth -3.3 to -3.8 at coh 0.99-0.93).
config: lam 1.0->0.3, spectral_lam 0->0.01 (locked from #98/#99 ablation),
gen_pass_target/gen_kappa_decay/gen_kappa_min/gen_max_batches walk-C knobs.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
_encode: tokenize prompt+completion separately and cat ids so the prompt is
always a clean token-prefix (no BPE merge spans the boundary). Drops the assert
that killed #87 at round 2. Returns BatchEncoding.
generation: repetition_penalty=1.3 + no_repeat_ngram_size=3. Repetition is
incoherence the ppl filter cannot see (loops are low-ppl = predictable); the
#89 loop died of "instead their instead their" by round 6, so stop it at the
source. Wired through steering._gen_one for both steered and plain gen.
diag_barrier: gen_round arg (re-heal a chosen round's kept data, not just clean
round 0) + a "tau" deadband sweep mode. Lets us test whether the barrier earns
its place on the degenerate round-1/2 data where healing is actually needed.
journal: entries (d) phantom-KL-init was a wrong diagnosis, (e) barrier-strength
sweep -- barrier throttles trait and buys no coherence at the coherent dose.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
The pareto map drew a base->r0->...->rN polyline per arm, which tangled at 10
rounds and duplicated the left zigzag's round-order info. Make it a scatter that
just shows WHERE steered/healed land, labelling only r0 + last round. Persist the
base eval as an event so the loop's plot is reproducible offline, and add
scripts/plot_run.py to re-render trajectory.png from events.jsonl without re-running
the 3h loop (needed because the loop imports plot.py at start, so a plot fix never
reaches a running job).
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
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>
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>
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>
wassname