Adds operators.mutate_ruin_recreate: un-divides one wing of a storey and
rebuilds it with the same adjacency-aware constructor the seeders use
(_assign_adjacency_aware, generalised with a new `scope` param), seeded
from the surviving circulation bordering the wing. Gated behind
enable_ruin_recreate (default off) / --ruin-recreate, same pattern as
reassociate/bridge_circulation.
A/B (qpk protocol, DESIGN.md §23): initial uniform-weight run was
underpowered (fired ~1/32 children), null. A weight=3.0 follow-up
(_MUTATION_WEIGHTS["ruin_recreate"]) showed a statistically significant
win on programme-house across 15 seeds (8W/1L/6T, mean fails 7.07->6.00,
Wilcoxon p=0.041) but no consistent effect on harbor-house across 8 seeds
(3W/2L/3T). Kept default off pending a size-threshold follow-up.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Combined follow-up to 8sh (DESIGN.md §22): raised bridge_circulation's
_MUTATION_WEIGHTS entry to 2.0 (lj3) and re-ran the qi6/qpk-protocol A/B
at 4x the sample size (qjg) in one sweep, since the two variables were
confounded if tested separately. Result is null in the opposite direction
from 8sh's small-N signal -- no total-fail benefit (p=0.71 programme-house
N=20, p=0.69 harbor-house N=12) and a higher rate of trajectory-divergence
-induced new not-connected fails than at the original uniform weight.
Reverted the weight bump; enable_bridge_circulation stays default off.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01GDZjAATDWW1xFfc7xnJqSt
Adds operators.mutate_bridge_circulation: retypes the cheapest path between
two disconnected circulation components to circulation, directly clearing a
'level N not connected' fail instead of relying on the qi6 graded comparator
key (measured negative, DESIGN.md §18). Gated off by default via
driver.search's enable_bridge_circulation flag and evolve.py
--bridge-circulation, mirroring enable_reassociate's clean-toggle pattern.
qi6/qpk-protocol A/B (DESIGN.md §21) is directionally positive but mixed at
N=3/N=5 (never worse on total fails; clears 2/5 baseline not-connected fails
vs qi6's 0/4; one seed's RNG-trajectory divergence adds 2 new not-connected
fails) — kept default off pending a larger-N confirmation sweep
(homemaker-py-qjg) and a mutation-weight bump experiment (homemaker-py-lj3).
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01GDZjAATDWW1xFfc7xnJqSt
20-seed programme-house sweep (vs the original 5) resolves the qpk A/B's
mixed 3/5 result as small-sample noise around a true small positive: mean
fails 7.95->7.10 (~10.7%), 11W/6L/3T, paired t-test p~0.028. Flips
collapse_insearch's default from OFF to ON in evolve.py and driver.py
(_overrides_for/_fitness_for/_evaluate/search/polish_finish); opt out with
--no-collapse-insearch. fitness.Fitness itself is unchanged.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01GDZjAATDWW1xFfc7xnJqSt
conn_grade ON vs OFF (qpk protocol, experiments/run_qi6_ab.sh): harbor-house
(budget 2500, seeds 1-3) byte-identical output in every seed — the secondary
comparator key never fired. programme-house (budget 3000, seeds 1-5) 3/5 seeds
tie exactly; seeds 1/2 diverge to a different topology but the fail delta is
adjacency/crinkliness/width/access/size, never connectivity. Zero of 4 cases
where a not-connected fail was present got cleared by the grade.
Mechanism (b)/(c) (graded proximity as tertiary comparator key) is falsified,
not just unconfirmed. Kept default OFF (already was). Closed qi6; filed
homemaker-py-8sh for the remaining candidate (mechanism (a): an explicit
insert/relocate-circulation operator that doesn't depend on the search
stumbling onto a fail-count tie).
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01GDZjAATDWW1xFfc7xnJqSt
driver.search()/search_staged() gain enable_shape_repair (default off),
mirroring the enable_reassociate clean-toggle pattern: only builds a
fitness.Fitness instance and passes it to operators.mutate() when
enabled, so shape_rotate/deslim (7fm) can actually be selected mid-GA
instead of always no-opping on fit=None.
Full A/B sweep (harbor-house, budget=1M, 4 seeds) shows no improvement:
mean fails 14.50 (off) vs 14.75 (on), within seed noise. Confirms 7fm's
finish-time finding at in-search scale — these operators don't rescue
harbor-house's residual fails even with GA selection pressure.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01GDZjAATDWW1xFfc7xnJqSt
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>
Diagnosed the geometry-intrinsic residual from 94g's collapse: ratio
re-optimisation isn't the bottleneck (1500-eval NM makes zero difference on
the 12-fail collapsed best layout); the causes are upstream area starvation
and cut-orientation mismatch. Added mutate_shape_rotate/mutate_deslim
targeting each, gated on a Fitness instance like the existing reqs-gated
repair ops.
Evaluated as a finish-time exhaustive hill-climb on the same 6-layout
harbor-house sweep 94g used: zero improving moves found anywhere — every
candidate move traded the shape fail for a new adjacency/access fail on the
co-evolved layout (§4.2's lesson, now confirmed for topology repair). Closes
homemaker-py-7fm; spun homemaker-py-161 for the open in-search-GA question.
See DESIGN.md §19 for the full writeup.
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
9o5 §7.5 escape hatch: a per-space `interchange: false` opt-out in
patterns.config removes a code from auto-derived interchange classes,
letting the architect veto a harmful grouping (harbor-house's transitive
8-code chain) without disabling superposition globally.
SpaceReq gains an `interchange` bool (default True). Honoured as an S0
short-circuit in interchangeable() and by filtering derive_interchange_
classes() input. Superpose default stays OFF regardless (xi7 verdict), so
this only bites when superposition is enabled on a real config.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01M8566xAxTnwtJTkpXjYNZm
harbor-house 3M (500k/grain x3 + 1.5M polish, workers 4, ~22h): the in-run
grain anneal reached 1.26e-08 / 23 fails (canonical byte-for-byte), losing
decisively to both the direct --no-leaf-sharing baseline (5.14e-06 / 15) and
yaa's single-hard-transition warm chain (4.19e-06 / 15) — ~400x worse fitness,
+8 fails.
Each grain step spikes the fail count as its unfolded leaves acquire
independent shape fails (phase-end 19->21->27, final de-share 27->36); the
per-phase budget re-polishes a partially-materialised state the next step
materialises further, so coarse-grain gains do not carry forward. The polish
phase started from a deeper hole (36) than the warm chain's single clean
transition and 1.5M evals recovered only to 23. The sharing-phase topology
skeleton is best cashed in once, at full grain — not annealed.
Machinery retained (search_annealed, --anneal-grain, unfold above=, seed_pop,
max_share override): correct, tested, honest, reusable. Default finish stays
§15's single-transition unfold+polish. DESIGN §16 records the verdict; closes
homemaker-py-kpu.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01M8566xAxTnwtJTkpXjYNZm
Ramp the leaf-share grain down within one continuous run (e.g. 4->3->2->off),
carrying the whole population across each step — graduated non-convexity over
the single hard sharing->off transition of the §15 finish.
- operators.unfold_shared_leaves(above=cap): unfold only leaves whose share
exceeds the new grain cap, leaving smaller-share leaves collapsed for the
next step. above=1 (default) keeps the full-unfold §15 behaviour.
- driver: max_share override threaded through _overrides_for/_fitness_for/
_evaluate so a phase can rebuild the evaluator at a lower leaf_share_max cap;
search(seed_pop=) evaluates an explicit initial population so a phase hands
its whole population to the next instead of restarting from a single best.
- driver.search_annealed: one phase per descending grain then a de-share
polish; unfold-above-cap between steps; cumulative accounting + grain-tagged
history; honest canonical best (byte-for-byte verified vs homemaker-fitness).
- evolve: --anneal-grain LADDER CLI (self-finishing; §15 finish not applied).
8iv settled the primitive (grid unfold beat the circulation-aware slice), so
the ramp reuses the plain balanced-grid unfold at every step.
Tests: unfold above-cap selectivity, seed_pop seeding, search_annealed phase
stitching / honest finish / degenerate-ladder fallback. 258 pass. DESIGN §16.
Head-to-head A/B on harbor-house still to run; verdict pending (issue open).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01M8566xAxTnwtJTkpXjYNZm
Investigated homemaker-py-8iv (route access to unfolded shared-leaf children).
Built + A/B-tested circulation-aware slicing vs the existing balanced grid; the
150k-eval warm-start polish shows slice loses decisively (41 fails/3.5e-14 vs
grid 25 fails/2.4e-09, grid ahead at every milestone). Reverted the code to the
grid unfold; closed 8iv negative and corrected kpu (Schedule B) to use the grid
unfold, not slicing. Also closed yaa (investigation complete).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01M8566xAxTnwtJTkpXjYNZm
Add DESIGN.md §15 recording the leaf-sharing output-honesty bug (internal
sharing objective diverges from canonical scorer), yaa's conclusive
unfold-then-polish investigation, and the driver.polish_finish auto-finish
fix + --polish-budget CLI knob. Matches §13.10's documentation of the
original leaf-sharing feature.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01M8566xAxTnwtJTkpXjYNZm
Leaf-sharing evolve runs optimised a sharing-credited objective (a shared
leaf of code X with share=k counts as k programme rooms, size re-centred on
k*target) but wrote the un-materialised genome, which the canonical
homemaker-fitness rates catastrophically worse (harbor-house: internal
1.03e-05 vs canonical 6.73e-29, 15 critical missing-room fails).
Fix: before write, driver.polish_finish() unfolds every live shared leaf
into k distinct rooms (operators.unfold_shared_leaves — pays down the count
deficit) then warm-starts a leaf_sharing=False polish search from the
unfolded genome so the materialised rooms get proportion/width/size cleanup.
Returned best.fitness is the canonical score (leaf_sharing off => internal
== canonical). This is yaa's proven unfold-then-polish path, made automatic.
evolve.py: new --polish-budget (env HOMEMAKER_POLISH_BUDGET; -1=auto=
budget//2, 0=unfold+rescore only). Interrupt forces polish_budget=0 for a
fast honest output. Default stays --leaf-sharing on (its topology-search
speed retained; output made honest by the finish). Schedule B in-run
annealing remains homemaker-py-kpu.
Verified (harbor-house 3000+1500): reported polish fitness 4.79788e-27
matches canonical homemaker-fitness exactly, 0 critical fails. Tests: 254
pass (+3 polish_finish).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01M8566xAxTnwtJTkpXjYNZm
operators.unfold_shared_leaves(): materialise each live shared leaf
(share=k) into k distinct same-code sibling leaves, splitting its
footprint into k equal-target children with squarest per-cut rotation
(_size_subtree_equal), clearing the share stamp. Surgical — siblings'
evolved geometry is untouched.
Investigation result (harbor-house, evolved-3M sharing seed):
- unfold alone (zero search) closes all 15 critical missing-room fails
and lifts the canonical score 6.73e-29 -> 1.46e-19 (90->59 fails).
- warm-starting a --no-leaf-sharing evolve from the unfolded seed runs
~7-8 orders of magnitude ahead of the naive (un-materialised) warm
start at equal budget. The count deficit, not the sizing, was what
stranded Schedule A deep in the fail hole.
Adds test_unfold_shared_leaves_materialises_deficit.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01M8566xAxTnwtJTkpXjYNZm
Ran harbor-house init.dom under 3M-eval memetic search. Default --leaf-sharing
scored 6.7e-29 (90 fails, 15 missing rooms) when re-scored by canonical
homemaker-fitness, vs 5.14e-06 (15 fails, 0 critical) for the honest
--no-leaf-sharing warm-start chain. Head-to-head confirmed a naive
sharing->no-sharing warm-start plateaus ~60x behind, motivating programmatic
unfold of shared leaves at the phase transition (yaa).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01M8566xAxTnwtJTkpXjYNZm
A/B at equal budget (collapsed score): --superpose vs --no-superpose.
- programme-house (budget 3000, seeds 1-5): OFF wins 4/5
- harbor-house (budget 2500, seeds 1-3): OFF wins 2/3
Relaxation gap (§7.4) small (ratio 1.01-1.23); per-eval collapse removes it
by construction, so the failure mode is geometry-floor dominance, not the gap.
harbor-house 8-code chain misgroups and adds fails -> filed b3v (interchange:false).
Verdict: keep --superpose default OFF.
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>
R_size=1.5 / R_width=1.3 / R_prop=1.5 for the interchangeable-class similarity
gate (S2); class-size cap C=4 confirmed; interchange:false veto hatch deferred
to a later fix only if auto-derivation misgroups on real configs. All open
questions resolved.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Resolve open Q1: collapse runs per fitness eval (search optimises the
condensed objective directly, removing the relaxation gap), bounded by a
derivation-time class-size cap C=4 (<=24 perms/eval). Note the collapse is a
separable linear-sum assignment, so Hungarian solves it exactly beyond the
cap if a real class ever exceeds it.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Spec the multi-use-leaves feature per Bruno's framing: superposition as a
search relaxation over auto-derived interchangeable equivalence classes
(requirement-similarity), condensed to specific usage at the end by
brute-forcing the in-class assignment (3 usages/3 leaves = 6 perms). Records
the reversal of the issue's 'path b preferred' note, the relaxation-gap /
0-3 search-easing prior, default-OFF baseline gate, and open Qs (collapse
cadence, similarity thresholds, veto hatch).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Prime a population from N independent converged elites + crossover-heavy
migration phase, vs best-of-N at equal total budget. Island does NOT win:
harbor 68 vs control 67 (within parallel noise), maple 124 vs control 116
(decisive). Default-off child_probe hook on driver.search instruments the
deciding mechanism: area-matched crossover across independently-converged
elites rarely synthesizes (1/65 harbor, 3/63 maple beat the better parent,
max fail-drop 2-5), confirming the alignment hypothesis (non-canonical 9gp
encoding -> disruptive splice). Search-machinery null #3; residual stays
geometry/shape-bound. 233 tests pass.
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
experiments/diag_edge_too_long.py: the 6 harbor edge-too-long fails are 2
locations — a share=3 combined leaf (247 m², aspect 1.2; flat 8 m cap not
share-aware, unlike quality_size's k×target) accounting for ~4, and one
1.2×16.7 m narrow sliver (~2, also caught by width/proportion). No corridors.
Files homemaker-py-hph (share-aware edge-too-long fix).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01JygRv4n2dcyDQqMiDRe7TN
