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 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>
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>
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