Closes homemaker-py-ju3. DESIGN.md §39.3.
The class: key from the bead's design was deliberately NOT built. Auditing
every use of the prefix rule first showed it runs deeper than c/o/s -- l/k/b/t
carry real adjacency semantics (graph.py builds bedroom<->toilet and
kitchen<->living relations from first characters) -- so re-plumbing the type
system would invalidate the whole corpus and every baseline, for a problem
whose damage is the silence, not the convention. Two findings made the smaller
fix sufficient: no corpus programme has ever declared a bare c/o/s code, so
check_space_counts' skip only ever discarded declared rooms; and nothing
references harbor's four codes in any adjacency or co_locate list.
- programme.validate_codes raises on a reserved-prefix code, with the full
explanation. Called from BOTH parse paths (programme._parse_spaces and
fitness.Fitness._load_programme parse conf["spaces"] independently, so
validating one would leave the other door open). l/k/b/t stay unreserved.
- harbor-house and harbor-house-l0 renamed: cr1->fr1, of->ao, st1->gs1,
st2->gs2. New prefixes are unused in harbor and semantically neutral, and
the two storage codes still share a prefix, preserving the structure
evaluate_building's per-code plot-ratio term depends on. name: unchanged.
- experiments/migrate_ju3_rename.py migrates .dom files written before the
rename (--check dry-runs). Pre-rename artefacts, notably evolved-3M*.dom,
must be migrated or their leaves read as unmatched generics.
- test_collapse_global's c/o/s exclusion test now uses a generic C leaf, which
is what the exclusion is actually for; it previously relied on a programme
code colliding, which is no longer possible.
Re-baseline (seed 1, 20k evals, same settings as §38's run): 57 fails against
the 32-instance effective programme -> 55 against the real 37-instance one,
with all five previously-lost room instances now placed inside their declared
sigma bands (fr1 87.2 vs declared 80, was 32.9/17.1; ao/gs1/gs2 were absent
entirely) and no failure naming any of the four codes. At one seed each,
57 vs 55 is within noise -- the robust result is the room placement, not the
count. Historical harbor numbers are not comparable to post-ju3 ones; filed
homemaker-py-t3s to restate 2v1's acceptance figure once evolved-3M is
migrated.
346 passed (+10 new), same 7 pre-existing fixture failures.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01MJ84Feep79Hhm3E4zZJmnB
Follow-up measurement corrects the first draft of §38 in two ways.
1. Harbor-house's floor is 15 fails (evolved-3M-nols-3, 1.7M evals), not the
30-40 I quoted from §13.11's 20k-budget runs. Frontage deficit predicts the
COST of solving, not impossibility: ~150x budget gap between a
frontage-short and a frontage-surplus programme. Table corrected.
2. Zero-exposure is only half the mechanism, and not the dominant half.
Splitting the deletion test by lit vs buried shows a WELL-DAYLIT corridor
(q_crink=0.736) is still worth x4.06 to delete. Cause: value_circulation=50
vs value_inside=300, so merging corridor into room is a flat x6 gain, while
'level N not connected' costs only x0.5. Break-even needs 0.5^k < 50/300,
i.e. k > 2.58 -- severing must cost at least 3 fails and costs 1. Net x3.0
predicted, x4.06 measured. The objective is net-positive on severing the
spine even when the circulation is perfectly lit, which explains why both
'level N not connected' fails survive in the best layout after 1.7M evals.
Adds fitness.quality_uncrinkliness crinkliness_mode (EXPERIMENTAL, default
"urb" = stock hard 0.0, byte-identical: 336 passed vs 331 before, same 7
pre-existing fixture failures). A/B harness ab_crinkliness_mode_ssz.py shows
none of the three modes removes the incentive, and the lit column is 3/8 under
every mode including stock -- clean isolation of the two mechanisms.
Filed homemaker-py-2v1 (P0) for the pricing fix; ssz/hxi now depend on it.
Acceptance test recorded up front: harbor must reach 15 fails in materially
fewer than 1.7M evals AND without either not-connected fail.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01MJ84Feep79Hhm3E4zZJmnB
Python silently shadowed the gnw-scope process_storey with the later
hgg-extended one; the first ~45 lines were unreachable dead code that
still read as live. Deleted; the extended definition is a strict
superset. Suite: 405 passed (pre-existing 5 CP-SAT/reassign failures
unrelated, confirmed present on main before this change).
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_014uCyZJCh5mBuA2yEFxgQpo
driver.collapse_best built its evaluator with _fitness_for's default
collapse_insearch=True, so collapse_finish's base_fails/cand_fails were
both measured through score_with_fails' own auto-collapse pass -- base
silently equalled collapsed on 5/5 probed files, making the "keep only
if fails don't increase" safety guard vacuous and understating 94g's
real effect in logs. fitness.collapse_finish now forces canonical
(collapse_insearch=False) scoring for its own measurement regardless of
self's config; collapse_best now builds its evaluator canonically too
(matching what homemaker-fitness reports for the written .dom) and
threads max_share/conn_grade through. Same-family fix in
search_annealed's no-polish-budget rescore branch, which silently
defaulted to collapse_insearch=True via _evaluate's default.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_014uCyZJCh5mBuA2yEFxgQpo
Splits the flat outer-search comparator (-n_fails, fitness) into a tiered
(-n_hard, -n_soft, fitness) so search budget stops being spent polishing
SOFT shape fails (crinkliness/proportion/size/width/edge-too-long/
staircase-volume) while HARD structural fails (missing space, wrong/
required level, level/circulation/vertical connectivity, adjacency,
stairs, covered-outside, storey limits, public access) remain unfixed.
fitness.classify_fail_tier/tier_counts classify every fail string emitted
across fitness.py and graph.py, raising on anything unrecognised so new
fail sites must declare a tier. Validated against all real fail strings in
the checked-in corpus plus every fail-emission call site read from source.
driver.Individual gains n_hard/n_soft (populated from innerloop.Result.
fail_lines); search(use_tiers=...) swaps the comparator when set (default
off, so existing runs are unaffected — inner-loop 0.5^n cliff untouched).
evolve.py exposes --use-tiers / HOMEMAKER_USE_TIERS.
experiments/tier_ab_2g7_3.py runs the acceptance A/B (harbor+maple, 3
seeds, 20k evals) in the background; results pending.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01LSwQwpEaHFBkeVSDDWd75S
Fixes the stale id()-keyed geometry cache read in parallel staged runs:
substrate_readiness runs in the parent process every tournament/admit
comparison but the parent's score_with_fails (which normally clears
geometry._cache) only runs in pool workers when n_workers>1, so evicted
trees' freed addresses can alias into freshly unpickled ones. Also adds
a defensive clear at collapse_global entry per the bead's recommendation
for the same cache class of hazard.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Dq3WAXft8RszMG2CLH7VkU
collapse_global's own commit could relabel a leaf back to the code its
stale share_type names, making share_type == type true again and
resurrecting a multiplicity credit for area never sized for it -- the
commit-door companion to the iio valuation bug. dom.canonicalize_shares()
drops share/share_type whenever share_type != type; called at the top of
collapse_global (covers collapse_global's own commit, 2-opt, and standalone
finish-time use) and _evaluate_full (covers collapse_superposition and
ordinary retype mutations) so the guard is an actual invariant instead of
a per-reader check.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Dq3WAXft8RszMG2CLH7VkU
_collapse_value and _usage_quality temporarily overwrite leaf.type to probe a
hypothetical candidate code, but graph.leaf_share reads that overwritten type
against leaf.share_type -- so a stale share (left over from a code the leaf
was since retyped away from) spuriously reactivates whenever the probed
candidate happens to equal the old share_type, skewing the Hungarian
assignment's cell value for that (leaf, code) pair. dom.dump/dom.load drops
such stale metadata on reload (dom._emit only serialises share when
share_type==type), so a live search tree carrying it and its dump/reload
round trip fed different values into the same collapse_global call and
landed on different optimal matchings.
Fix: neutralise share_type during the probe whenever the candidate differs
from the leaf's real current type, restoring it in the finally block. The
leaf's own current type still legitimately carries a live share.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01R8agJBT2ZpmF3ErW7wi2wY
Follow-up to the previous commit: user flagged that quality_width/
quality_proportion's "stricter of both" (max target, min sigma) combination
for a fused leaf's two codes was an ad hoc hack. Tried two more principled
alternatives and A/B'd all three against the harbor-house/health-centre
example programmes (20k evals x 3 seeds each):
1. stricter-of-both (original) -> health-centre +24.5% worse
2. precision-weighted Gaussian product -> health-centre -13.9% better
3. mixture (max of two Gaussians) -> health-centre +20.4% worse
Landed #2 (fitness._gaussian_product): combining two Gaussian evidence
sources about the same quantity via precision-weighting gives an
intermediate target with a narrower spread, unlike the naive max/min hack.
#3's building block (_clipped_gaussian) is kept, documented, and unit-tested
as a recorded negative alternative -- somewhat counterintuitively, the more
philosophically appealing "let the leaf collapse toward whichever code fits"
mixture model was empirically worse, because max() lets a leaf score 1.0 by
satisfying only the weaker of the two codes' targets.
multi_use stays default OFF -- the precision-weighted result improves both
example programmes on average but isn't the clean sweep needed for a
default flip (harbor-house loses 1/3 seeds). DESIGN.md §33 rewritten with
the full three-way comparison.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01R8agJBT2ZpmF3ErW7wi2wY
Builds path (b) from §26 -- a leaf permanently serving two DIFFERENT
compatible programme codes at once, extending leaf-sharing's same-code
multiplicity mechanism to different-but-compatible codes. Architect-declared
`co_locate` pairs (validated against interchangeable()'s S1-S4 bounds, no
transitive closure so the b3v chain problem can't recur), threaded through
graph.py's checks via a new leaf_codes() resolver and fitness.py's quality
terms (additive size, stricter-of-both width/proportion). Construction-time
only, gated behind `multi_use` (default OFF, bit-identical when off).
End-to-end A/B (20k evals x 3 seeds x 2 programmes) came back net negative:
harbor-house -4.0% but health-centre +24.5% worse (3/3 seeds), because
fusing different codes' shape targets via stricter-of-both can impose a
tighter joint constraint than either code needed alone, which the tightly-
packed health-centre programme can't absorb. Written up as DESIGN.md §33;
multi_use stays default OFF, no default-flip recommended.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01R8agJBT2ZpmF3ErW7wi2wY
homemaker-py-9wi's 2-opt adjacency polish (collapse_global's local_search
kwarg) was validated on harbor-house alone; this closes homemaker-py-cdl
by extending the A/B sweep to programme-house (34 more files, 46 total):
0 regressions, 2 improvements, rest identical.
collapse_global's own default stays False since it also runs every
fitness eval via collapse_insearch (qpk) on the unmerged tree, where the
2-opt pass would add cost to a hot path the sweep never measured.
Instead default it on at the two one-shot finish-time call sites:
homemaker-collapse --local-search, and a new homemaker-evolve
--collapse-local-search wired through driver.collapse_best's
**collapse_kw.
The Jacobi adjacency relaxation in collapse_global (94g) re-solves a linear
assignment each round holding neighbours' labels fixed from the previous
round, which can 2-cycle between labellings that satisfy zero adjacency
requirements even when a fully-satisfying permutation exists (proved by
test_two_opt_polish_escapes_jacobi_plateau on a minimal 4-cell chain).
Fitness._two_opt_adjacency_polish runs after the Jacobi fixpoint and tries
swapping the labels of every same-level pair of supply leaves, keeping a
swap only on strict improvement -- monotone by construction. Gated behind
collapse_global(local_search=...) / homemaker-collapse --local-search,
default off pending a broader sweep (homemaker-py-cdl). Swept the 11
harbor-house evolved-*/3m/materialised .dom files: 0 regressions, 1 real
improvement (evolved-anneal-3M.dom 21->19 fails).
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Runs the 94g finish-time cell↔room collapse inside every fitness eval
(collapse_insearch conf flag, default off, bit-identical when off) instead
of once at the end, so search optimises the collapsed objective directly.
Plumbed through fitness.py/driver.py/evolve.py the same way superpose/
conn_grade are; --collapse-insearch CLI flag.
A/B validated against the xi7 protocol (equal budget, both arms finished
with standard finish-time --collapse): POSITIVE, opposite of the 9o5/xi7
prior. harbor-house ON wins 3/3 (mean fails 80.3->72.0); programme-house
mixed 3/5 (mean fails 8.4->7.8). Kept default off pending a larger
programme-house sample; documented as a working opt-in for harbor-house-
scale-or-larger programmes. Full writeup in DESIGN.md §20.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
The dominant post-collapse fail is the binary "level N not connected",
which is flat across fragmentation (a 7-component storey scores the same
as a 2-component one), so the outer search has no gradient toward
connected circulation. A finish-time convert-to-circulation repair was
prototyped and measured NEGATIVE (195->560 fails: bridging needed rooms
costs more missing-room fails than the one binary fail it clears).
Instead add graph.circulation_connectivity(G) = largest-circ-component
fraction, summed over storeys onto the score_with_grade proximity channel
(conf flag conn_grade; replaces the §11.4 leaf-grade there). It is a
secondary comparator key only — scalar fitness and fail count stay
byte-identical — restoring the gradient the binary fail lacks. Threaded
through driver (_overrides_for/_fitness_for/_evaluate/search; enabling it
implies the grade key) and evolve --conn-grade (default off).
A/B on full-budget runs pending; short smoke run confirms plumbing.
Tests: tests/test_conn_grade.py x9 (fraction contract, non-circ ignored,
monotone under (dis)connection, score/fail invariance); 276 pass.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01M8566xAxTnwtJTkpXjYNZm
Public-access term (preserve_public_access, default on): when the building's
only street access is an l/k ROOM neighbour of a public outside leaf (no
circulation fallback — an existential building-level check the per-leaf
objective can't see), that leaf is pinned (kept, its demand slot decremented)
so the collapse can't drop "no outside public access". Best layout 15→13
becomes 15→12 with zero new fails; sweep total 172→171, still monotone.
collapse_finish(root, **kw) -> (tree, base, coll, applied): keep-better wrapper,
scores on throwaway copies (score_with_fails merges in place), returns the
collapse only if fails don't increase.
Wiring: driver.collapse_best updates result.best (lineage +collapse, canonical
re-score); evolve.py runs it after the sharing polish behind --collapse/
--no-collapse (default on). New homemaker-collapse CLI (collapse_cmd.py) applies
it to an existing .dom, writing <stem>.collapsed.dom.
tests/test_collapse_global.py: demand-set relabel, level hard constraint, c/o/s
exclusion, no-op safety, keep-better/unmerged. 267 pass.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01M8566xAxTnwtJTkpXjYNZm
Add objective="quality"|"threshold" to collapse_global and make threshold
the finish-time default. The continuous-quality objective maximises
sum(usage_quality*area), which can trade one leaf just over the 0.1 fail
threshold for another just under (a fail SHUFFLE). The threshold objective
maximises the COUNT of passing size/width/proportion factors directly, with
continuous fit only as a tiebreak. A satisfied adjacency and a passing factor
share one weight (_COLLAPSE_FAIL_W) so both fail classes are minimised jointly.
Sweep over 6 harbor-house evolved layouts (total fails, base 195):
adj_off/quality 192 adj_on/quality 185 adj_off/thresh 181 adj_on/thresh 172
adj_on/threshold is monotone across all 6 (never worse than baseline), so it
is the new default. Residual on the best layout (15→13) is the building-level
"no outside public access" constraint, outside the per-leaf model.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01M8566xAxTnwtJTkpXjYNZm
Global relabel of inside-room leaves via one optimal assignment over the
full leaf set — the 9o5 per-class collapse generalised to N leaves ↔ M
required rooms — as a one-shot finish-time polish on a committed layout.
Assignable room_codes exclude any starting c/o/s to match the scorer's
own partition (check_space_counts skips those; cr1/st1/st2 collide with
the circulation/structure convention). Hard level constraint via a -1e12
forbid penalty. Adjacency handled as an iterated relaxation: geometry is
fixed at finish time so each leaf's graph neighbours are fixed; warm-start
from evolved labels, each pass a linear assignment over quality + an
adjacency bonus (has_adjacency vs current labels), Jacobi to a fixpoint.
Measured (level+adjacency): best evolved layout 15→14 fails, rougher ones
32→28 and 90→83; adjacency-on beats adjacency-off everywhere (off regresses
the best layout +1). Substrate only — not wired into search or a CLI yet.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01M8566xAxTnwtJTkpXjYNZm
Interchangeable codes (similar size/width/proportion, compatible level/stack,
no adjacency edge) form equivalence classes derived from the programme. With
--superpose (default off), each fitness eval COLLAPSES every superposed leaf to
its best in-class usage via an optimal supply->demand assignment (brute force
<=C! within cap C=4, scipy Hungarian beyond), then scores the condensed types.
Because collapse re-types on the unmerged tree before all checks, counts /
adjacency / quality are unchanged downstream -- no Node field, no graph/operator
changes -- and default OFF is bit-identical.
- programme.py: derive_interchange_classes + interchangeable (S1-S4, locked
thresholds R_SIZE=1.5/R_WIDTH=1.3/R_PROP=1.5, CLASS_CAP=4)
- fitness.py: collapse_superposition, _best_assignment, _usage_quality;
superpose/superpose_class_cap conf knobs; collapse hooked into _evaluate_full
- driver.py/evolve.py: superpose flag plumbed beside leaf_sharing; --superpose
- tests/test_superposition.py: 17 tests (derivation, assignment, end-to-end)
Closes homemaker-py-9o5 (build); validation A/B is homemaker-py-xi7.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
§13.8 verdict was positive and monotone-harmless, so default the share-aware
edge-too-long cap to leaf_sharing when share_edge_cap is unset — mirrors the
pll bal+share and §13.6 interior_outside default flips. Explicit
share_edge_cap=False still reproduces the pre-flip control arm.
- fitness.Fitness.__init__: cap defaults to self._leaf_sharing when the conf
key is unset (None); explicit True/False honoured.
- run_staged_search.py: pin conf["share_edge_cap"] = share_edge in both A/B
arms so SHAREEDGE=0 stays a clean control post-flip.
- tests: control arm now pins share_edge_cap=False; new
test_edge_cap_defaults_on_under_leaf_sharing guards the flip.
- DESIGN.md §13.9: rebaseline §13.x floor (maple 80.3→74.0, harbor 34.7→31.0).
Non-sharing runs untouched: programme-house control re-score reproduces
bit-for-bit. 222 tests pass.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
§13.7 flagged edge-too-long as harbor's top fail class. Dissection showed the
bulk are a leaf-sharing REPRESENTATION ARTIFACT: a share=k leaf aggregates k
same-code rooms, so its walls run ~k× the flat 8 m cap purely for being big —
the same §13.3 leak (size/missing relaxed for shared leaves) on the wall measure,
since edge_cost/outside_edge_cost ignored leaf.share.
Fix: Fitness._edge_cap(*leaves) scales the 8 m cap by the largest type-guarded
leaf_share among adjoining leaves, mirroring quality_size's k×target; non-shared
leaves keep the flat cap so genuine narrow/oversize pathologies stay flagged.
Gated behind a share_edge_cap config knob (SHAREEDGE env), default OFF so the
§13.x controls reproduce.
A/B (full Phase-8 stack, staged, 20k evals, seeds 0/1/2): control reproduces
§13.7 (maple 80.3 exact, harbor 34.7≈34.0); share-aware arm maple 80.3→74.0
(−7.9%), harbor 34.7→31.0 (−10.6%), zero regressions across 6 seeds. Positive
and monotone-harmless (only ever removes a false-positive fail). Verdict:
recommend default-ON; follow-up issue flips the default + rebaselines the floor.
Tests: 6 new unit tests for _edge_cap (221 pass).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01JygRv4n2dcyDQqMiDRe7TN
Replace the area-derived share recovery with explicit, type-guarded per-leaf
multiplicity: construction stamps leaf.share=k and leaf.share_type=code; the
fitness (graph.leaf_share) honours k only while leaf.type==share_type, so any
retype/undivide auto-invalidates a stale share — no operator resets, and a
small leaf cannot retype its way into covering rooms it does not provide. Two
Node fields survive the whole search via deepcopy (genome.decode is unused in
the hot path); .dom emits `share` only on a live shared leaf.
This closes the §13.3 missing-fail leak: floor probe missing 17–44 → 0, and the
achievable floor drops −39% harbor (120.3→73.3) / −32% maple (194.7→133.0) with
no re-emergence as size fails.
Flag threaded through driver.search/search_staged → constructive_topology /
lift_base_to_storeys, exposed via LEAFSHARE/LEAFSHAREFAC in run_staged_search.py
(injects the objective into inner-loop + final-score fitness so both A/B arms
share one programme dir). run_leafshare_ab.sh runs the staged 20k A/B.
Smoke-tested end-to-end (harbor, factor 3, re-score OK). 214 tests pass;
default-OFF reproduces baseline.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Same-code rooms collapse into fewer, larger SHARED leaves so the ~1.8/leaf
shape tax (§13.1) is paid once per group. Multiplicity k is recovered from
area (k=clamp(round(area/target),1,max_share)) — no genome change — and used
in two default-OFF sites: graph.check_space_counts counts coverage (Σk vs
req.count) so one leaf covers several rooms without a missing fail, and
fitness.quality_size centres on k×target (σ scaled by k). Construction:
operators._share_rooms groups instances; _size_divisions_from_targets sizes
shared leaves to k×target via leaf_mult.
Floor probe (experiments/diag_leaf_sharing.py, harbor+maple, seeds 0/1/2,
+innerloop): total fails −27% harbor / −16% maple at share3, shape factors
fall ~linearly with leaf count (confirms §13.1). Cap: 17–44 missing fails
leak because depth maldistribution (§13.2) keeps shared leaves below k×target
so round() undercounts; inner loop can't close it. Net still positive.
Default-OFF reproduces baseline exactly (214 tests pass). Driver plumbing +
staged 20k A/B remain; §13.3 records the next design fork.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Implement a graded proximity comparator key (-n_fails, grade, fitness) behind
a default-off use_grade flag: fitness._leaf_grade / score_with_grade sum
f/FAIL_THRESHOLD over failing per-leaf quality factors; scalar fitness and fail
count stay untouched so the inner-loop 0.5^n cliff (§5.4) is unaffected (0/9
regression check: PASS). Read once per child in driver._evaluate off the
already-optimised tree; threaded through search_staged (Stage 2 only).
Harbor staged A/B (20000 evals, seeds 0/1/2): lex 95/96/106 (mean 99.0) vs
lex+grade 99/98/102 (mean 99.7) — grade wins 1/3, no plateau escape. Premise
falsified: within a fixed fail-tier 0.5^n is constant so fitness still spans
~6 orders of magnitude; grade above fitness displaces that working signal.
Verdict: reject; lexicographic (-n_fails, fitness) stands. Flag kept default-off
for reproducibility / possible reuse as a §11.5 diversity signal.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
