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
500k serial full-stack harbor probe (probe_harbor_floor.py): 20 fails,
crinkliness 13→4, landlocked crinkliness ~13→2 of 20. Interior-O (default-ON,
erc.8) is 71d's named fix and dissolved its landlocked-crinkliness target;
residual now diffuse (top class edge-too-long). NO-GO on 71d.
Cumulative Phase-8 floor vs §12.2 baseline (leaf-share-relaxed): maple
136.0→80.3 (−41%), harbor 74.0→34.0 (−54%) — all from construction levers,
none from search machinery, per the epic thesis.
Closes erc epic: 71d/7u5/jrb/u8x superseded-by-construction; erc.5/erc.6
wont-fix (Diag A/B revisit conditions unmet). DESIGN §13.7.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01JygRv4n2dcyDQqMiDRe7TN
§13.6/ld2 verdict: interior-O light-well seeding is net-positive — harbor
-16.4% (all seeds improve), maple net-neutral (-2.8% mean, no programme
regresses). Mirror the pll bal+share flip: default interior_outside
False->True in driver.search/search_staged and operators.constructive_topology/
lift_base_to_storeys (outside_divisor stays 3). The experiments INTERIORO
A/B override is unchanged. test_interior_outside_… now pins the peripheral
baseline to interior_outside=False explicitly. 215 tests pass.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Seed O as interior light wells (most-landlocked leaves first, count scaled
by room count via outside_divisor) instead of one peripheral O, attacking the
erc crinkliness residual: seed diagnostic confirms every crinkliness fail is
under-exposed (landlocked), none over-exposed.
A/B (20k evals, seeds 0/1/2, bal+share stack, §13.6): control reproduces §13.5;
interior odiv=3 gives harbor -16.4% (all seeds improve) and maple -2.8%
(net-neutral). Default-optimal divisor 3 found by seed sweep (6 was null).
Lever default OFF; default-ON flip tracked as erc.8.
- operators: interior_outside + outside_divisor through constructive_topology,
lift_base_to_storeys, _assign_adjacency_aware (fix n_circ budget for >1 O)
- driver.search/search_staged threading; run_staged_search.py INTERIORO/ODIV env
- test_interior_outside_seeds_landlocked_wells_and_scales_count
- experiments/run_interioro_ab.sh; DESIGN.md §13.6
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
erc.7/§13.5 verdict: depth_balanced + leaf_sharing (factor 3) is the
winning Phase-8 stack. Flip the three knobs to default-on so
homemaker-evolve inherits them; env-var A/B overrides (DEPTHBAL/
LEAFSHARE/LEAFSHAREFAC) unchanged. 214 tests pass, no snapshot churn.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
End-to-end 20k A/B (seeds 0/1/2): depth-balancing gives −5.8% maple / −3.2%
harbor with OVERLAPPING arms — far less than the −11/−12% seed-floor probe,
because the 20k search erodes most of the seed advantage via divide/undivide
mutations (unlike leaf-sharing's structural leaf-count cut, which the search
cannot undo). Baseline reproduces §12.2 (maple 137.0 vs 136.0, harbor 74.0).
Promise is the additive floor with leaf-sharing (probe: bal+sh3 << share3-alone);
the decisive test is erc.7 synergy. Keep depth_balanced default OFF; close erc.4,
advance erc.7.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
_grow_leaves grew a random caterpillar, so equal-target rooms landed at
wildly different binary-tree depths — the depth-driven size maldistribution
Diagnostic B (§13.2) localized (same code at 0.05x and 14.7x target). The
depth_balanced flag always splits a shallowest leaf instead, growing a
near-complete tree so the proportion-aware sizing pass hits each target with
cut fractions near their proportional value.
Floor probe (diag_depth_balance.py): depth spread collapses 7->1, the giant
ratio falls (maxR 12->8 harbor / 16->6 maple), %undersize 54->25 / 42->22,
and the achievable floor drops -12% harbor / -11% maple at EQUAL leaf count.
Additive with leaf-sharing (bal+sh3 beats §13.3 share3-alone). Default OFF,
214 tests pass; threaded through driver.search/search_staged and exposed via
DEPTHBAL in run_staged_search.py. End-to-end 20k A/B running.
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>
The "56% empty plot" is a misreading: sized rooms already hold 1.4-1.5x
their aggregate target area; ~46% of plot is circulation, not claimable
void. Size fails are depth-driven MALDISTRIBUTION — the same type/target
leaf lands 0.05x..14.7x by binary-tree position. The inner loop cannot
repair it (frozen topology, budget-80 size fails move only -1.6/-3.7).
=> Falsifies plot-fill-as-claim-void: re-scope erc.4 to depth-balanced /
giant-splitting construction; deprioritise erc.6 (inner-loop term, wrong
DOF). Reinforces erc.3 leaf-sharing for the starved tail.
Script: experiments/diag_slack_localization.py (self-contained evidence).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Controlled synthetic sweep (maple-court, room set fixed, circ_divisor 2->9)
shows per-leaf shape-fail is FLAT vs slicing density (1.72-1.94, no trend)
while TOTAL shape fails track leaf count linearly (139->116). Crinkliness
dominates (~0.8/leaf) and is flat; cuts are already squarest yet still pay
~1.8 fails/leaf. Floor is INTRINSIC to per-leaf slicing, not cut quality.
Verdict: prioritise leaf-sharing (erc.3); deprioritise compactness-cuts
(erc.5 -> P4). Adds experiments/diag_leaf_shapefail.py.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Add interior-O courtyard seeding (ld2, construction lever, P1) and a
Tier-3 failure-directed topology-repair operator (71d + subtasks) as
children of the erc Phase-8 epic. Diagnosis from the 3M harbor-house
run: 27 fails dominated by 13 crinkliness + 7 size; ~16 are invariant
to split ratios (landlocked rooms, fitness.py:339), so the lever is
construction/topology, not the inner loop — consistent with erc's
thesis.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Add epic homemaker-py-erc (lower the geometry/shape floor) with two
read-first diagnostics gating four floor-lowering experiments, plus the
island-model search bet (psk). Diagnostics decide leaf-sharing vs
compactness-cuts (A) and construction-fill vs inner-loop-fill (B).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The constructive seeder was never nondeterministic: _assign_adjacency_aware
ends every max/min with a unique leaf-idx tiebreak and uses set unions only
for membership, so iteration order never leaks. constructive_topology(seed=0)
is byte-identical across processes for every example programme. The cited
"sig 4480 vs 16064" was a measurement artifact — Python's builtin hash() of a
str is salted per process (PYTHONHASHSEED), so an identical signature hashes to
different ints run-to-run.
The real run-to-run noise was parallel-only: driver._run_batch admitted futures
via as_completed (completion order), and admit() is order-sensitive (accrues
n_evals per result; keeps the first individual of an equal-key tie as best). A
long parallel run diverged 167 vs 161 fails (maple seed 0). Fix: admit futures
in submission order (block on each result in turn; all still run concurrently),
reproducing the serial admission sequence. Two workers=4 runs are now
byte-identical. Serial (workers=1) was already byte-for-byte reproducible.
Per-seed numbers are reproducible only at a fixed worker count; serial != parallel
is expected (children/iteration 1 vs n_workers changes batch granularity).
- driver: iterate futs in submission order, not as_completed
- test: test_search_parallel_is_reproducible (fails on pre-fix, passes on fix)
- DESIGN.md §12.4: corrected the reproducibility note
Closes homemaker-py-xcy
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
End-to-end A/B (maple div6/div8, harbor div6, seeds 0/1/2, 20000 evals) vs the
§12.3 div=3 baseline: every arm within ±1.7 of baseline (maple 136.0 -> 137.0 /
134.3; harbor 74.0 -> 75.3), inside the measured ±3 noise floor with large
per-seed spread. Coarsening the circulation spine lowers the raw shape floor but
raises access/adjacency by as much; end-to-end they wash out. Verdict: keep
circ_divisor=3; the maple/harbor residual is the geometry floor of the slicing
representation at this room density — neither search machinery (§12.3) nor
construction granularity (§12.4) moves it beyond noise.
En route: the div=3 control (129 vs §12.3's 126) exposed a reproducibility bug —
_assign_adjacency_aware iterates id()-ordered sets of Node objects, so the
constructive seed is nondeterministic across processes (~±3 fail noise). Filed
homemaker-py-xcy (P2); per-seed ledger numbers are not reproducible, only
multi-seed means.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Threads circ_divisor (default 3 = unchanged) through
operators.constructive_topology/lift_base_to_storeys and
driver.search/search_staged; env CIRCDIV in run_staged_search.py. Adds
experiments/run_c3g_ab.sh.
Motivation (DESIGN.md §12.3 diagnostic): the maple shape residual is
over-granular construction (73 small leaves -> crinkliness+size). Cheap raw-seed
probe: a coarser spine lowers the SHAPE floor (maple 135->110, harbor 83->66)
but raises access/adjacency, leaving the raw TOTAL floor flat-to-worse. Because
§12.3 showed shape is the HARD residual and access/adjacency are cheap to
repair, only an end-to-end A/B settles whether trading them pays — this is the
plumbing for that run. Tests green (default path byte-identical).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Per-leaf breakdown of maple-court constructive seeds (6 seeds) overturns the
earlier 'shape-aware placement' handoff guess: shape fails are UNIFORM
(~68/73 leaves fail) at only 0.44 plot utilisation, dominated by crinkliness
(perimeter/area) then size (undersize). So the residual is neither a room->leaf
placement mismatch (no well-shaped leaves to place into) nor density-bound — it
is over-granular construction (73 small leaves for 52 rooms). Corrected the
§12.3 verdict accordingly and filed homemaker-py-c3g (construction granularity /
leaf-shape lever) as an unproven, must-be-A/B'd hypothesis.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
