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>
24-run sweep (maple-court + harbor, seeds 0/1/2, 20000 evals): M3 reassociate
and the shape-feasibility filter are both neutral-to-slightly-worse vs the
§12.2 baseline (maple 136.0 -> 139-140, harbor 74.0 -> 77-78). Baseline controls
reproduce §12.2 exactly, so the negative is real.
Verdict: the Phase-7 residual is the geometry/shape floor of the constructed
slicing layouts, not reachability/feasibility-bound — third independent negative
on search machinery (§11.4/§11.5/§12.3) vs four construction/seed wins
(§11.2/§11.6/§11.7/§12.2). A full canonical Polish rewrite is not justified: its
one testable promise (associativity reachability) was tested and did not pay.
Both operators kept default-OFF.
Closes 9gp.1, 9gp.2, 9gp; epic leu (Phase 7) auto-closed (3/3). Adds the
reproducible sweep harness experiments/run_9gp_ab.sh.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Land the two evidence-supported parts of the re-scoped 9gp capstone as
operators on the existing decoded Node tree (no Polish-expression rewrite),
each default-OFF and measured against the §12.2 leu.2 baseline.
9gp.1 shape-feasibility pre-filter: operators.predicted_shape_fails lays a
topology out at its proportion-aware target geometry and counts shape fails
(size/width/proportion/crinkliness); driver._evaluate prunes clearly-infeasible
topologies before the inner loop (1 eval vs ~80), guarded so nothing that could
beat the incumbent is discarded. search/search_staged feasibility_filter,
feasibility_max_shape_fails (env FEAS/MAXSHAPE), default OFF.
9gp.2 M3 Wong-Liu reassociate: operators.mutate_reassociate adds associativity
(a|b)|c <-> a|(b|c) on same-orientation live cuts — the canonical-slicing move
missing from swap(M1)/rotate(M2), attacking the §11.4/§11.5 reachability
bottleneck. enable_reassociate (env REASSOC), default OFF (weight 0 -> baseline
byte-identical).
Unit tests (operators + driver) green, full suite 211 passed; maple-court smoke
run clean under native fitness. A/B sweep handed off per the plan; DESIGN.md
§12.3 documents the design and the pending measurement.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Size each constructive-seed cut from leaf TARGET areas (division=[f,f] gives
left area-fraction f) and pick each cut's rotation for child squareness — both
derived from target dims, topology/type assignment untouched. Area-only
regressed (slivers); rotation choice is what makes it pay.
End-to-end (20000 evals, 3 seeds, staged): harbor 85.3->74.0 (-13%, best 69),
maple-court 151.7->136.0 (-10%, best 126). PROP=0 reproduces the §11.7/§12.1
baselines exactly. programme-house regresses at fixed budget (deeper local
optimum walls off the undivide restructuring path) but a budget sweep shows
it's convergence speed, not a worse asymptote (PROP=1 reaches 1 fail at 150k).
Default-on (seed_proportion_aware=True, env PROP=1).
cq1: n_storeys now honours storey_minimum, not just level: keys — programme-house
(storey_minimum:2, all rooms level:0) was seeded one storey short and fell
through to plain search. New programme.storey_minimum()/n_storeys_for();
driver.search passes min_storeys to the seeder; search_staged routes on the max.
No-op for harbor/maple; programme-house single-stage 8.0->5.0.
New maple-court best (126) saved as generated.dom. 204 tests pass.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
_assign_adjacency_aware gains fixed_circ (seed the connected-dominating-set from
given circulation leaves) and secondary-adjacency-aware room placement: codes
with the most non-c adjacency requirements are placed first, each onto the open
slot satisfying the most of its requirements against already-typed neighbours
(clustering k1<->da1, da1<->o). lift_base_to_storeys(reqs, adjacency_aware=True)
grows the upper-floor circulation spine off the inherited vertical core and
assigns rooms around it; threaded through driver.search_staged
(seed_adjacency_aware) and run_staged_search.py (ADJ env).
End-to-end staged harbor, 20000 evals, mean total fails over 3 seeds:
ADJ=0 99.0 (reproduces the §11.4 staged lex baseline exactly), ADJ=1 85.3
(-13.7, -14%; best 78). New best harbor configuration overall: staged baseline
99.0 -> single-stage adjacency-aware (§11.6) 90.7 -> staged + adjacency-aware
lift 85.3. Staging and adjacency-aware seeding compose.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
operators._assign_adjacency_aware spends ~one extra leaf per three rooms on a
greedy connected-dominating-set of circulation leaves (read from the geometric
leaf_graph, type-independent), so every room borders a connected circulation
spine and adjacency-to-c + access are satisfied by construction. Default-on via
constructive_topology(adjacency_aware=True), threaded through
driver.search(seed_adjacency_aware) and run_search_scaled.py (ADJ env).
End-to-end single-stage, 20000 evals, mean total fails over 3 seeds:
harbor 110.0 -> 90.7 (-17.5%; ADJ=0 reproduces the §11.2 105 baseline exactly),
programme-house 12.3 -> 9.3 (-24%). Adjacency-aware single-stage harbor (mean
90.7, best 85) beats the §11.3 staged best of 95 — the first Phase-6 fail-count
reduction from seeding. Follow-ups (lift_base_to_storeys, secondary adjacencies)
filed.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
genome.signature: ratio-invariant structural topology hash (per-storey tree
shape + cut orientation + leaf types), the cheap stand-in for the 9gp canonical
encoding. driver gains niche_by_signature (one individual per topology, replaces
the fitness-scalar dedup) and restart_patience (soft restart: keep elites,
refill with fresh seeds); SearchResult gains n_distinct_signatures /
diversity_history / n_restarts.
Diversity criterion MET (final-pop distinct ~5/16 -> 16/16). Gate NOT met:
blank-slate programme-house mean fails 12.3(legacy)/12.7(niche)/13.0(restart)
over 3 seeds at 20000 evals; harbor staged 95/94/108. Niching is a tie within
seed noise, restarts strictly worse — falsifies the premise that the
fitness-scalar dedup causes premature convergence. Both flags default-off,
kept for reuse. Epic c4c complete.
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>
Search the genome in causal dependency order. Stage 1 evolves a single-storey
base over the level-0 room set (programme auto-derived to a tempdir), ranked
with a substrate-readiness bonus (reserved core × divisible capacity) so the
base is selected as a good substrate, not just a good ground floor (anti-§4.2).
Stage 2 lifts the best base into a full multi-storey design — preserving the
inherited core, instantiating each upper storey's required set by construction —
and searches the deltas with the base mutable at low probability (base_p=0.15).
New: programme.{n_storeys_required,partition_rooms_by_storey,write_stage1_programme},
graph.substrate_readiness, operators.{lift_base_to_storeys,_pick_weighted_by_storey},
base_p threading, driver.search rank_bonus_fn/seed_factory/base_p hooks +
search_staged orchestrator, experiments/run_staged_search.py, tests/test_staging.py.
Result (harbor, 20000 evals, seed 0): staged 95 fails vs single-stage 105
(-10, -9.5%), gain in crinkliness 27->18 + edge 12->8. Anti-bungalow confirmed
(Stage-2 core moves all noop — core inherited, not carved). Programme-house
regression PASS (warmstart-2f4 still reaches whole-pop 1-fail).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Outer search now ranks individuals by (-n_fails, fitness) instead of raw
fitness scalar. This prevents high-score 3-fail designs from displacing
2-fail designs in tournament selection and population replacement — the
root cause of the §4.8 pathology where flag count dominates geometry.
Inner loop is unchanged: it still optimises against the raw 0.5^n fitness
scalar, so the cliff that prevents trading into new failures remains intact
(0/9 regressions in experiments/penalty_reshape.py).
Also removes stale _CHILD_INNER_KW = {"sigmas": (0.05,)}: this was left
over from the CMA-ES era; the NM inner loop default (homemaker-py-d6d)
does not accept a sigmas parameter.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Bakeoff with native fitness shows NM wins at all DOF sizes: +9% at
child_budget=80 for programme-house (6-7 DOF), and decisively at
harbor-house scale (35-40 DOF) where CMA-ES exhausts its convergence
detector after ~3 generations (46 evals) and adds failures on 12/15
runs. NM uses the full budget, is parameter-free, and has zero new
failures across all test cases.
- Add nm_search() to innerloop.py; change optimise() default to "nm"
- Add nm_search to parametrised test cases
- Add bakeoff_native.py and bakeoff_harbor.py experiments with results
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
programme-house budget=20000: 1.04e-02 (2 fails), 1.36× over Phase-2
oracle run and 2.60× over urb-evolve p128. Winning topology found via
rotate at eval 10357, unreachable within Phase-2 budget. 71.8 evals/s
(~140× faster than batched oracle).
harbor-house (16 rooms): 3.73e-18 (49 fails) at budget 10000 in 633s.
This programme is beyond the oracle's capability; native fitness makes
it feasible. 638 topologies explored.
Adds experiments/run_search_scaled.py (native-only search runner, no
oracle dependency). DESIGN.md records Phase 3 gate result.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
benchmark_vs_urbevolve.py results (2026-06-13, budget=2000, URB_NO_OCCLUSION=1):
- Seeded designs: memetic beats urb-evolve 1.91× (c964435) and 1.63× (2f45907)
- Blank slate init.dom: memetic at 18 fails vs urb-evolve at 6 fails (topology
diversity gap from single-seed mutation chain vs random-population init)
Bug fixed: run_search.py was calling oracle.score on out.parent without
patterns.config present — causing the re-score to return near-zero instead of
the correct tracked fitness. Added shutil.copy to propagate patterns.config
alongside the output .dom before the standalone re-score.
Gate recorded in DESIGN.md §7. Closes homemaker-py-way.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
4-way comparison (NM / CMA-ES / compass / compass-ms) over 3 corpus files ×
3 seeds at budget 200, cold-start, URB_NO_OCCLUSION=1. CMA-ES wins on
batch-efficiency (18 oracle calls vs 200 for NM, 12x speedup on Perl startup
amortisation per §4.6) with acceptable quality (x1.41 @200 vs NM's x1.56).
Compass stalls on narrow-valley landscapes and introduces fail regressions.
NM flagged as Phase 3+ candidate once native fitness removes oracle call overhead.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Port Urb's programme-driven fitness leaf quality factors (perpendicular,
proportion, size, width, crinkliness, daylight, access), value rates,
and cost model (per-leaf area costs, interior/exterior wall edge costs,
boundary costs) to Python. Passes 0-mismatch parity against the Urb
oracle across all 35 corpus files (407 leaves, 2849 factors), using
URB_NO_OCCLUSION=1 simple crinkliness (illumination factor pinned to 1).
Key fixes: _dist must use math.sqrt not math.hypot (1-ULP difference
flips boundary overlap predicates); leaf-scope fail regex requires ^\d+/
prefix to exclude building-level failure messages.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
run_urbevolve took (seed, budget, pop, cell) but cells call
fn(seed, budget, cell, **kw) — every urb-evolve cell died on TypeError,
deferred silently by pool.map. mutate_swap lacked the empty-candidates
noop guard the other operators have, crashing on init.dom-style bare
plots. Regression test: every mutation survives an undivided tree.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
experiments/benchmark_vs_urbevolve.py (homemaker-py-way): 3 seed designs
(init from scratch, c964435 weak, 2f45907 strong) x 2000-eval runs, the
1000-eval tier read from each run's best-so-far log; urb-evolve gets two
population sizes (default 128 = ~16 generations at this budget, and 16 =
~130) and credit for its better one. Counts via the MAX_EVALS counter
patch in urb-evolve.pl (committed in the urb repo); both systems under
URB_NO_OCCLUSION=1; all finals re-scored through urb-fitness.pl as the
common deterministic yardstick.
run_search.py generalised to (budget, rng-seed, seed.dom, out.dom);
innerloop.optimise now handles 0-DOF topologies (an undivided plot like
init.dom scores once instead of crashing CMA).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Bruno's correction: 'C' was never a 'covered' type — Is_Covered is a
geometric predicate. Urb generics are canonically uppercase (get_space_types
qw/C O S/; corpus 100% uppercase). The driver/operator type pool emitted
lowercase 'c'/'o', creating mixed-case designs that fragmented Dom->Ratios
class buckets and fired the latent ratio_type first-match nondeterminism
(which the search promptly reward-hacked). Operators now emit uppercase
generics only and class checks match case-insensitively (t[0].lower() in
'cos', cf. Is_Circulation/Is_Outside). The Urb-side class-sum patch remains
as defensive hardening, zero-impact on canonical designs (35/35 parity).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
driver.py (homemaker-py-b39): tournament selection, operators.mutate (storey
ops down-weighted) + area-matched crossover, every child's geometry
delegated to the warm-started inner loop (Lamarckian write-back; children
use a single local CMA phase - the exploratory ladder phase exists for cold
projections children never face). Budget stated and accounted in oracle
evaluations; near-duplicate fitness guard against population collapse
(neutral mutations are common, per 8cs).
free_with_keys/ratio_map/warm_x0 promoted from the 8cs experiment into
innerloop.py as the Lamarckian inheritance API; alignment with
solver.free_branches asserted across the corpus.
tests/test_driver.py fakes the inner loop: budget accounting, monotone
improvement history, warm-start + sigma plumbing, valid .dom output.
31 tests pass. experiments/run_search.py is the end-to-end acceptance run.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
operators.py (homemaker-py-nyb): divide/undivide/retype/swap/rotate/
level_add/level_delete + Urb-style area-matched base-storey crossover.
Operators edit the decoded Node tree; genome.encode absorbs all repair
(dangling deltas, storey misalignment) so every child is a valid genome
by construction. Geometry moves deliberately absent — the inner loop owns
continuous DOF, and 8cs made Lamarckian re-optimisation mandatory.
Fixes dom._link to CLEAR stale below-links when a path vanishes from the
storey below (undividing a base branch left upper nodes pointing at
orphaned quads; oracle scoring unaffected but in-process geometry crashed).
Acceptance (experiments/operator_locality.py, flag-on): 115/115 children
scored without error; geometry perturbation small for core ops (retype
0.07, divide/undivide 0.14, swap/crossover 0.16-0.17), fitness
perturbation large for all (0.68-0.99 rel) — the 0.5^n cliff flags most
raw moves, confirming warm-started re-optimisation + penalty reshaping
as the load-bearing design choices. 27 tests pass.
Closes homemaker-py-nyb.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Corpus: all 35 scores shift (x1.0-1.24, daylight pinned), one expected
failure-set change (458aa8b8 +2 crinkliness), oracle ~8% faster batched.
New deterministic-seed reference gains become the accept_innerloop bars:
x1.63 / x1.70 / x1.68 at budget 400, ~35 oracle calls per topology.
urb-evolve respects the flag by construction (in-process fitness reads
ENV at call time). Old flag-off numbers kept in DESIGN as historical.
Closes homemaker-py-gp2 (Urb-side patch lives in /home/bruno/src/urb,
uncommitted there pending review).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
experiments/warm_vs_cold.py (homemaker-py-8cs): top-storey divide/undivide
mutations on corpus designs, path-keyed inheritance of the parent's
optimised ratios (surviving cuts keep values, new cuts 0.5), per-evaluation
convergence traces; reports oracle evals to 95% of best final, warm vs
cold. Machinery validated oracle-free (cut survival counts) and one
mutated child scored through the oracle.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
innerloop.py: optimise(root, programme_dir, x0=None, budget, method) ->
Result, optimising equal-offset free-branch ratios (midpoint projection of
legacy unequal cuts) against full oracle fitness. OracleEvaluator scores
each population in one batched perl call. Methods: cma (default) — multi-
start sigma ladder (0.05 local, 0.15 exploratory) with IPOP-style popsize
doubling and deterministic seeding (pycma treats seed 0 as clock!) — and
compass with Hooke-Jeeves pattern moves, kept for the d0s bake-off.
Acceptance (experiments/accept_innerloop.py, §4.5 bars vs unprojected
originals, within-noise tolerance 1%): x1.65 / x1.66 / x1.58 against bars
x1.24 / x1.67 / x1.59, no new failures, 46 oracle calls vs Nelder-Mead's
200. The two near-bar results are statistically indistinguishable from the
single-NM-draw bars (measured draw spread brackets them); decision approved
by Bruno 2026-06-12.
Also: tests/ scaffold (12 oracle-free unit tests, pytest pythonpath=src),
rebaseline_no_occlusion.py for homemaker-py-gp2, cma>=3.0 dependency
(installed via dnf), dead-variable cleanup in solver.py.
Closes homemaker-py-1p0.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
oracle.score_batch() writes/cleans N outputs and runs urb-fitness.pl once
with all file names; oracle.score() is now a thin wrapper. Adds
Score.fail_lines (sorted) because Perl hash-order randomisation shuffles
.fails line order between runs, and documents Urb's ~1-ULP score
nondeterminism (compare with rel tolerance, never ==).
experiments/bench_batch_oracle.py validates batch-vs-single parity on the
35-file corpus and benchmarks: 0.98 s/dom batched vs 1.27 s/dom single
(x1.30), all files identical (fitness to 1e-12 rel, exact failure sets).
Closes homemaker-py-av5.
Driving equal-offset cut ratios with Nelder-Mead against the REAL oracle
fitness (full objective, no proxy) improves all three test candidates with
zero new failures:
2f45907 (best evolved) 0.012617 -> 0.015684 x1.24 (2->2 fails)
candidate-002 0.007375 -> 0.012319 x1.67 (2->2 fails)
c964435 (baseline) 0.003667 -> 0.005836 x1.59 (3->3 fails)
Headroom widens on weaker designs. The EA under-optimises geometry by
24-67% even on its best result. This validates a full-fitness geometry
inner loop (NOT the earlier area-proxy solver) and motivates a memetic
architecture: topology search outside, full-objective geometry optimise
inside, gated on a native Python fitness (oracle at ~3s/call is too slow).
Adds the bottom-up ratio solver, programme parser, Perl-oracle bridge,
and two experiments. Headline finding: the "isolated size solver on a
frozen topology" hypothesis is NOT validated.
- resolve_ratios.py: re-solving candidate-002 from programme targets
recovers areas accurately but scores below the original (introduces
width/perpendicular/crinkliness failures the area objective ignores).
- refine_sweep.py: warm-start refine of all 34 evolved candidates
regresses 34/34 (fails 124->297 perpendicular-tied; 124->626 area-only
with free skew). Moving cuts to fix room area breaks the coupled
adjacency/access/shape constraints those designs balanced.
Conclusion: sizing is not separable from the rest of Urb's fitness;
a geometry inner loop must optimise the full objective, not an area proxy.
Geometry port remains validated byte-identical to Urb.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Clean-room Python successor to Urb for programme-driven layout search.
This initial commit establishes the .dom bridge format and a faithful
port of Urb's top-down quad geometry, validated byte-identical against
Urb across all 35 programme-house example files (including the wall
inset and multi-storey wall-stacking inheritance).
- dom.py: .dom YAML <-> Node tree, parent/below/position linkage,
wall_outer inset on load, raw-corner stash for round-tripping
- geometry.py: Coordinate/Coordinate_a/_b/Area/Length + Coordinate_Offset
- experiments/dump_areas.{py,pl}: geometry regression harness
