demo: single demo_steer entry point -- headings, searched anchors, comparison table

wassname: "one demo function, everywhere, nice output, comparable qualitative outputs,
heading making config/method clear, table at end comparing quants". show_steer now returns
its per-anchor quants; demo_steer(vecs, ...) loops methods with a clear ## heading each,
calls the searched-anchor show_steer (strongest coherent steer both ways), and prints one
github-table comparison of the readout at -C*/0/+C* across methods. demo_edges.py is now a
3-line call. Also: research_journal.md started (ARJ).

Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>

docs/research_journal.md

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+# jsteer research journal
+
+Reverse-chronological. Entries by Claude with wassname. Claims link to evidence.
+
+## 2026-07-12 -- measuring steering effect: coherence gates + edge search + a negative result
+
+Context: making the demos quantitative and honest. wassname repeatedly (correctly) flagged
+that the measurement was wrong; each fix exposed the next artifact.
+
+### Instrument evolution (the readout kept fooling us)
+- v1 `pmass` on a forced digit slot `{"ans": N}`: ~always 1 because the JSON prefix forces
+  a digit. Blind coherence guard.
+- v2 JSON object `{"ans","why","2+2"}`, gate on valid-parse + `2+2==4`: caught SOME
+  breakdown but on a SHORT forced object that survives long-generation degeneration, so it
+  over-credited (persona_vector scored rubric 9/9 while its actual text was wedding-jewelry
+  loops).
+- v3 repetition `rep = 1 - distinct-3` of the think trace (wassname's idea: "it's all
+  repetition breakdown"): every steer breakdown is a repeat loop; threshold 0.35 from the
+  empirical gap over 40+ real generations (coherent <0.3, degenerate >0.6). Simple + right.
+- v4 DUAL gate after reading the dilemma traces: under hard steering the model emits a
+  NON-answer token at the forced slot ('imers', 'lie', '信任', '(') or a 1-word stub, so the
+  binary readout is meaningless there. Coherent now = fluent (rep<0.35, trace>=8 words) AND
+  committed (ans_mass = full-vocab mass on the answer tokens > 0.5). ans_mass is the
+  PRINCIPLED version of the pmass removed in v1: blind on a format-forcing slot, load-bearing
+  on an open YES/NO slot where the model can decline to answer.
+  Evidence: scripts/scratch/validate_traces.py; commits 6a080db, eba1ba4, a233e3a.
+
+### Edge search (wassname: "use the Illinois method to find the edge within ~5 steps")
+Fixed-step sweeps are too coarse to locate where coherence breaks. `coherent_edge()` brackets
+a coherent/incoherent pair then does modified false-position (Illinois) to find the coherence
+boundary in ~6 evals/side; `steer_anchors()` returns `[-C*, -C*/2, 0, +C*/2, +C*]`; every demo
+(`show_steer(Cs=None)`) now shows the STRONGEST coherent steer both ways instead of hand-picked
+Cs. Search must probe at the demo generation length (coherence is length-sensitive).
+Evidence: commits 781b703, 2c17603; scripts/scratch/demo_edges.py.
+
+### Result: steering moves tone, not a deliberated moral verdict
+Self-honesty dilemma (say you were sick to avoid getting fired), readout P(YES=lie), honesty
+axis (deceptive vs honest personas). At the SEARCHED strongest-coherent steer both ways,
+P(lie) stays flat ~0.03-0.11 for every method (baseline 0.11):
+
+    method        coherent -C* -> P(lie)   base   coherent +C* -> P(lie)
+    persona_pinv  -0.14 -> 0.05            0.11   +1.0  -> 0.10
+    word(lie)     -0.70 -> 0.08            0.11   +0.12 -> 0.03
+    meandiff      -0.28 -> 0.05            0.11   +0.32 -> 0.09
+
+Reading the persona_pinv +1.0 trace: still a balanced "no clear answer... not my place to
+decide", verdict unchanged. Because we steered to the coherence edge, this is NOT dismissable
+as "didn't push hard enough". Steering this axis changes tone/word-choice but not the
+deliberated YES/NO on a hard dilemma. (n=1-2 seeds; ans_mass>0.5 and the answer tokens are a
+knob; a harder/more-tempting dilemma or an axis-matched decision is untested.)
+Evidence: scripts/scratch/measure_all.py, artifacts/measure_all.jsonl, task-35 edge demo.
+
+### Method note (secondary axis)
+persona_pinv has the widest coherent window on BOTH the optimism-tone axis and the honesty
+dilemma -- the gentlest/most-robust extractor. persona_topk is a clean bidirectional TONE
+steer (optimism) but breaks the answer format on the dilemma. word-vector directly promotes
+tokens and is strong for tone, breaks fast on the deceptive direction.
+
+### Meta-lesson
+Read the actual generations, not the metric. The metric was wrong at four successive layers
+and every time the fix came from reading the text (validate_traces.py, rep_metric_check.py).
+"Excitement is evidence of bullshit" -- the big P(lie)=0.8-0.97 shifts were all artifacts.