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
Answers "are any config requirements actively fighting the engine". One is.
§39.1 NEGATIVE (recorded): no room spec in any corpus programme is internally
contradictory. Using shapecurve.leaf_constraints' validated FAIL_THRESHOLD
inversions, every code admits an (area, aspect) satisfying size, width,
proportion and crinkliness at once, and none needs more than one exposed side.
The "estimated targets are mutually unsatisfiable" hypothesis is falsified.
§39.2 SEVERE: Urb's type system is prefix-based (c = circulation, o/s =
outside) and programme codes share that namespace. A code starting with those
letters is silently reinterpreted, with three unannounced consequences:
check_space_counts SKIPS it outright (never required, no missing or too-many
fail); get_space_params returns generic *_circulation/*_outside params before
consulting self.spaces; and is_circulation/is_outside flip, changing value
rate, crinkliness exemption, and whether it supplies daylight to neighbours.
harbor-house is affected (maple-court, health-centre, programme-house are
clean): cr1 "Common Room with Fireplace" has all three declared targets
overridden (size 80.0 -> 0.0/14.0) and is valued at 50/m2 not 300; of x2 and
st1/st2 lose width/proportion and are treated as outside space. 5 of 37 room
instances (14%) are silently optional.
Measured: the two cr1 leaves converged to 32.9 and 17.1 m2 against a declared
80, with no too-many-spaces fail despite count:1; of/st1/st2 are absent from
the result with zero fails. Compounds with §38.2 -- the largest room in the
programme sits on the wrong side of the x6 circulation value gap, so the
objective is paid to shrink it.
Benchmark validity: every harbor-house fail count in this document was
measured against a 32-instance effective programme, not the 37 its config
declares.
Adds experiments/audit_programme_config.py (namespace + satisfiability
reports). Filed homemaker-py-ju3 (P0). No src changes; 336 passed, 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
Diagnostic investigation of why search stalls in local minima. Adds
experiments/diag_exposure_frontage.py (frontage/exposure/value reports,
no search run required) and records the findings as §38.
Core mechanism: quality_uncrinkliness returns a hard 0.0 for any leaf with
no daylit wall, and since leaf quality is a product feeding
value += quality * rate * area, every buried room contributes exactly zero
value while still costing. 45-56% of interior leaves are in this state under
the default construction stack.
Consequences measured, not inferred:
- Deleting a buried O leaf improves the score x85, a buried C leaf x62.
Nothing pins circulation or outside space, so the search is rewarded by two
orders of magnitude for deleting the circulation spine. This retro-explains
§18, §21/§22 and the level-not-connected fails surviving >1M evals.
- Closed-form frontage bound: every interior leaf needs exposed wall
L >= A/(1.6202*h). harbor-house supplies 54m against 148m needed (2.7x
short), maple-court 56 vs 162; health-centre and programme-house are
feasible. The corpus plateau is predicted by frontage deficit alone.
- Crinkliness is tiered SOFT but 60-100% of its fails are zero-exposure,
which is topological, so §37.1's tiered comparator is mis-informed about
the largest fail category.
- The missing-space cascade emits one extra fail per declared size/width/
proportion key, so under 0.5^n a missing room is weighted 4x differently
depending on patterns.config verbosity.
Filed as homemaker-py-ssz, hxi, tdp, gvb, 1i8 (plus bdf for the pre-existing
fresh-clone test failures found en route).
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01MJ84Feep79Hhm3E4zZJmnB
homemaker-py-7ua is fixed but only covers run_staged_search.py's
LEAFSHARE/MULTIUSE-gated rescore path. Baseline (LEAFSHARE=0/MULTIUSE=0)
runs still MISMATCH for the same underlying reason -- driver.search_staged
has no collapse_insearch param and always runs with it on -- tracked as
the new homemaker-py-4ok.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_014uCyZJCh5mBuA2yEFxgQpo
Research spike (DESIGN.md §37.8): classical rectangular dualization assumes
one-vertex-one-rectangle, which breaks on harbor's circulation hub (an
emergent-shape multi-leaf region, not a fixed single module) and is overkill
on the room-only adjacency graph (a trivial 3-edge matching already fully
satisfied by §11.7's seeding). No literature precedent for the multi-storey
stacking constraint either. Not prototyping; no code changes.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_014uCyZJCh5mBuA2yEFxgQpo
Full harbor+maple/3-seed/20k-budget run superseded the earlier inconclusive
pilot table; section now records the closed verdict (no clear win, both
flags stay default off) instead of "driver-level INCONCLUSIVE at pilot
scale".
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01LSwQwpEaHFBkeVSDDWd75S
Adds src/homemaker_layout/cpsat.py (OR-Tools CP-SAT) as an exact alternative
to operators._assign_adjacency_aware's greedy/beam room-code placement,
wired in as assign_solver="greedy"|"cpsat" (EXPERIMENTAL, default "greedy",
byte-identical to before) through constructive_topology/lift_base_to_storeys/
driver.search, plus a new operators.mutate_reassign in-search repair
operator (driver.search's enable_reassign=False default, mirrors
enable_ruin_recreate). Both found and fixed a resize-fragility bug (a
second CP-SAT pass against settled geometry, operators._cpsat_relabel_settled)
and a CP-SAT symmetry-blowup stall (explicit interchangeable-code grouping).
Seeder-level A/B on harbor-house is a solid, low-noise positive (~13% fewer
real fitness-scored secondary-adjacency fails, 10 seeds). Full driver.search
A/B is only pilot-scale (budget=3000 vs the bead's own 20k target) and
inconclusive -- both flags stay default-off pending a larger-N confirmation.
Full writeup: DESIGN.md §37.7. Bead left in_progress (own acceptance
criteria not fully met); homemaker-py-5bv tracks the deferred post-collapse
repair item.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01LSwQwpEaHFBkeVSDDWd75S
Generalise shapecurve.py's DP to process dom.levels(root) bottom-up per
storey instead of assuming a single free tree. A divided node's split is
free only per solver.free_branches' own criterion (below is None or
undivided there) -- geometry.coordinate always mirrors a below-linked
node's corners from the storey below regardless of whether that storey's
counterpart is divided, so every free region at any storey reduces to the
exact same single-region problem the pre-existing _check/realise already
solved. New _region_roots finds below-fixed leaves (checked directly,
gridless) and below-fixed-box/free-split fringe nodes per storey;
_solve_all_levels realises each storey before checking the one above and
snapshots+restores on any infeasibility, preserving solve()'s all-or-nothing
and is_feasible()'s never-writes contracts across the whole tree.
eligible() now allows any storey count.
Validated on the real (non-de-risked) examples/harbor-house: 200 random
2-storey topologies, DP-vs-NM agreement 99.5%, 0 false negatives, 117.7x
speedup (DESIGN.md §37.6). Full suite 397 passed.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01LSwQwpEaHFBkeVSDDWd75S
Adds shapecurve.is_feasible() (a non-mutating refactor of solve()'s check
phase) and a shapecurve_prune flag composing the DP's exact feasible/
infeasible verdict with operators.predicted_shape_fails' existing heuristic
prune: DP-feasible vetoes a heuristic prune outright; DP-infeasible only
hard-prunes when the incumbent already has zero total fails (exact, since
infeasible proves the shape-fail floor is >=1); otherwise defers unchanged
to today's heuristic threshold. Conservative by design since a wrong prune
is unrecoverable.
Validated 0/400 false negatives across two structurally distinct plots
(harbor-house-l0 + a newly-added programme-house sweep, the first genuinely
non-rectangular plot this DP has been checked against). The real
driver.search A/B on harbor-house-l0 measured NULL (byte-identical off/on)
for a root-caused, pre-existing reason: predicted_shape_fails rarely
triggers organically at this scale, so neither new branch had an opening to
fire -- not a defect in this change. Full writeup: DESIGN.md §37.5.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01LSwQwpEaHFBkeVSDDWd75S
Promotes the validated shape-curve DP (experiments/shapecurve_spike.py,
2g7.4, DESIGN.md §37.2) from a reference-only spike into
src/homemaker_layout/shapecurve.py, and wires it into driver._evaluate as a
warm-start for innerloop.optimise: when eligible (single storey, no
leaf_sharing/superpose/max_share/multi_use) and no caller-supplied x0, the
DP's exact shape-feasible ratio point is written onto the tree before NM
runs, off by default (shapecurve_warmstart=/--shapecurve-warmstart).
Caught and fixed a latent bug promoting the spike: realise() could leave
numpy.float64 in `division`, which yaml.safe_dump can't serialise — the
original spike never round-tripped through dom.dumps so this was never hit.
A/B on harbor-house-l0 (experiments/ab_shapecurve_warmstart.py, budget=2000,
5 seeds): mean total fails 16.6 (on) vs 19.6 (off), ~3.5x mean fitness
improvement; mean hard-fail count alone was a noise-level wash at this
sample size. Full writeup in DESIGN.md §37.4.
Deliberately deferred to new tracked beads (children of 2g7): DP-exact hard
pre-filter (wkh), multi-storey below-link support (koo), leaf_sharing/
co_type modelling (tym), true skew-quad polygon algebra (ekc) — 6xh stays
in_progress pending those.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01LSwQwpEaHFBkeVSDDWd75S
Composer half of the ground-truth bead: examples/harbor-house/drawings/
harbor-house 1.svg turned out to be a Bonsai/Blender render of 3m.dom's own
IFC (32 IfcSpace paths == 3m.dom's upper-storey leaf count), not a human
trace, so no usable reference exists yet -- this builds and tests the
pipeline that will consume one once traced. compose.py parses storey-N
Inkscape layers of cut-lines + labels against a boundary-stub .dom (plot/
height/elevation only, no room shapes to keep aligned across storeys) and
recursively detects guillotine cuts, mirroring geometry.py's own
division-line algebra; non-slicible regions and label mismatches are
reported by location rather than guessed at. homemaker-compose CLI added.
Renamed dom._link to public dom.link since compose.py needs to re-link from
outside dom.py. Full design writeup in DESIGN.md sec 37.3; actual human
tracing of harbor-house/programme-house is tracked as follow-up under
2g7.1, still open.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01LSwQwpEaHFBkeVSDDWd75S
User review caught a real gap: the DP approximated each quad's (w,h)
via its axis-aligned bounding box in global x/y, correct only because
harbor-house-l0's plot happens to be near-parallel to its own axes
(~7.5% area error). A real building's orthogonal walls need not align
to the survey/CRS axes at all -- confirmed by rotating the plot 45deg,
where the old bbox error jumped to 102% (up to 2x for a rotated square).
Fixed in two steps: (1) measure (w,h) from edge lengths
((edge0+edge2)/2, (edge1+edge3)/2, the geometry.aspect() pairing)
instead of global bbox -- rotation-invariant by construction. (2) this
alone regressed accuracy (99.0% -> 95.5%) because a child's own
rotation parity determines whether its local edge0/edge2 pair aligns
with its parent's edge0/edge2 or edge1/edge3 -- not a matter of degree
to measure empirically (as attempted first) but an exact algebraic
identity (verified float-exact: left.w + right.h == parent.w whenever
left.rotation is even and right.rotation is odd). _child_contrib now
applies this directly, replacing the empirical _orientation/
annotate_orientations machinery entirely -- simpler and correct.
Re-validated: 99.0% agreement on harbor-house-l0 unrotated (back to
matching the original result, same 2 residual mismatches, 0 false
negatives), 100% agreement at 97x speedup on the same plot rotated
45deg (new, via validate_shapecurve.py's rotated_plot_dir helper).
DESIGN.md §37.2 updated with the full correction history.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01LSwQwpEaHFBkeVSDDWd75S
Prototype + validation for an exact size/width/proportion feasibility DP
over a frozen slicing topology, replacing the ~80-200 eval Nelder-Mead
inner loop's approximate answer to the same question with one bottom-up
pass (experiments/shapecurve_spike.py). Leaf feasible regions are exact
FAIL_THRESHOLD-inversions of fitness.py's quality_size/width/proportion;
internal-node composition runs on a shared discretised grid.
Validated on harbor-house-l0 (experiments/validate_shapecurve.py, 200
random topologies vs NM minimising shape-fail-count directly): 99.0%
agreement (0 false negatives), 93.6x speedup at grid_n=150, plot-level
bbox approximation error quantified at +7.5% (root-causing both observed
false positives). All three acceptance criteria cleared -- see DESIGN.md
§37.2 for full results and the caveats/scope not covered (multi-storey,
leaf_sharing/co_type, true skew-quad regions). Kept as a reference spike,
same status as experiments/autodiff_spike.py (§34); production wiring
into driver.py filed as homemaker-py-6xh.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01LSwQwpEaHFBkeVSDDWd75S
DESIGN.md §37.1: hard/soft tiering A/B (harbor-house + maple-court, 3 seeds,
20k evals/run) shows hard-fail mean strictly better under the tiered
comparator on both programmes (harbor 11.67->5.33, maple 19.33->14.00) at
the cost of higher soft/total fails — the intended trade. ACCEPTANCE: PASS.
Filed homemaker-py-p6t as a non-blocking follow-up: race tiered vs flat to
0 hard fails (convergence speed) rather than composition at a fixed budget.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01LSwQwpEaHFBkeVSDDWd75S
Review of fitness.py/solver.py/collapse_cmd.py/innerloop.py/driver.py for
silent score-corrupting bugs (the iio class). Filed with verified repros:
r5a (stale-share resurrection via collapse commit), cvw (parallel staged
stale id()-keyed geometry cache), sd3 (collapse_best keep-better guard
vacuous under baked-in collapse_insearch), pek (shadowed process_storey).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01NjyStTdLWFMtdScrpbgQur
Adds a full writeup of the root cause (already fixed in 929be5b) plus a
same-codebase fix-vs-no-fix re-verification: harbor-house qpk-protocol
seeds 1-3 show collapse_insearch=OFF unaffected, but ON diverges by 5-8
fails on 2/3 seeds, non-directionally. Confirms the bug was not merely
theoretical for historical leaf_sharing+collapse_insearch runs, though the
noise is unlikely to have flipped 1ph's aggregate N=20 verdict. Adds a
caveat postscript to §20 (qpk) and files homemaker-py-d86 for the rigorous
historical-commit re-verification this session didn't do.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01R8agJBT2ZpmF3ErW7wi2wY
Re-ran the §13.1/§13.2-style per-leaf fail-breakdown diagnostic on real
driver.search_staged runs (budget 20000, seeds 0-2, harbor-house and
maple-court) under the current full default stack (leaf-sharing x3,
depth-balanced, interior-O, share-aware edge cap) -- never decomposed by
category since those defaults were flipped on.
Finding: crinkliness (48%) and size (20.6%) now dominate the residual on
both programmes (~69% combined); construction-completeness fails
(missing space, adjacency, level, connectivity) are down to a small
tail (<=6% each). This revises erc.1's old recommendation to deprioritise
compactness-cuts in favour of leaf-sharing -- leaf-sharing is now fully
deployed and crinkliness is proportionally more dominant than ever, so
DESIGN.md §13.11 recommends reopening a compactness/crinkliness-targeted
construction lever as the next concrete step.
Also files two bugs found while validating the methodology: dumping and
reloading a .dom under leaf_sharing+collapse_insearch does not reproduce
the search's own in-process fail count (homemaker-py-iio), and
run_staged_search.py's own sanity rescore omits the collapse_insearch
override (homemaker-py-7ua). experiments/run_and_capture_91f.py sidesteps
this by capturing the true in-process fails list instead of rescoring
from disk; experiments/diag_residual_91f.py tallies fail categories from
those sidecars.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01R8agJBT2ZpmF3ErW7wi2wY
Build a torch-differentiable local proxy for the ratio-to-fitness path (exact
port of geometry.py's coordinate recursion + the 5 continuous per-leaf quality
factors, with discrete/structural facts frozen from a real fitness.py
snapshot and the 0.5^n cliff relaxed to a sigmoid) and compare Adam ascent
against nm_search on frozen topologies from programme-house and harbor-house.
Result: ~30-35x slower per unit of search progress than nm_search at both
6 DOF and 36 DOF (per-op torch tensor dispatch overhead with no batching
opportunity, plus snapshot/resnapshot cost on par with a full oracle eval),
and no better quality at matched budget. A step-size sensitivity check
confirmed the flagged 0.5^n cliff risk is real, but autodiff doesn't make the
gradient direction any cheaper to obtain here. Not recommended; kept as
reference only, not wired into innerloop.py. Full writeup in DESIGN.md §34.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01R8agJBT2ZpmF3ErW7wi2wY
The N=3 A/B (previous commits) found the precision-weighted shape
combination improved both example programmes (harbor-house -1.4%,
health-centre -13.9%), but N=3 is a thin sample by this project's own
standard (xyu/9yx use N=15). Two confirmations:
- N=15, plain search, budget=3000 (mirrors xyu/9yx's own protocol exactly):
both programmes trend NEGATIVE (harbor +6.1%, health-centre +6.6%,
Wilcoxon p=0.044)
- N=15, staged search, budget=20000 (true same-conditions replication --
identical to the original A/B except seed count): both programmes AGAIN
trend negative (harbor +6.6% p=0.15, health-centre +4.7% p=0.48)
The same-conditions replication disagrees with the original result's
direction on both programmes. Conclusion: the N=3 positive signal was
sampling noise, not a real effect -- health-centre's -13.9% was driven
substantially by one seed (71->43 fails) that didn't hold up.
multi_use stays default OFF and is not recommended even as a promising
lever -- this is a clean NULL, closing out both halves of §26's original
multi-use-leaves question (path a was NULL/NEGATIVE, path b is NULL after
replication). Mechanism itself is unchanged, complete, and fully tested.
DESIGN.md §33 rewritten with all three measurements and the honest verdict.
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
run_9gp_ab.sh never threaded a worker count through run_staged_search.py, so
every §12.3 arm ran at n_workers=1 (serial) — the one mode §12.4 already
proved byte-for-byte reproducible even before the completion-order
determinism fix (that bug is ProcessPoolExecutor as_completed-only).
Spot-checked empirically: same config run twice gave identical fail counts
at every checkpoint. Closes homemaker-py-h10 as confirmed-null without
re-spending the ~8 core-hours a full sweep re-run would cost.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01R8agJBT2ZpmF3ErW7wi2wY
N=15 seeds, xyu's own ruin_recreate ON/OFF protocol, against a real 20-room
diverse programme instead of programme-house's duplicated-code sweep: 2.2%
mean-fails delta, p=0.40 two-sided — much weaker than xyu's own inconclusive
6.4%/p=0.059 reading at the same room count, and converging with
harbor-house's null-to-negative result instead. Closes the diversity-axis
gap xyu's larger-N pass could not reach; enable_ruin_recreate stays OFF on a
now-broader evidence base. Issue closed.
Extends y51's n=18 synthetic sweep (strongest of four sizes at N=10) to
N=15 seeds, matching f1d's own confirmation sample size. Effect shrank
(9.3%->6.4%, two-sided Wilcoxon p 0.098->0.059) but didn't evaporate or
reverse — an ambiguous middle case, not a clean confirm or null. Refiled
option (b) (non-synthetic third example programme) as homemaker-py-9yx
since extending N alone doesn't address the interchangeable-room-code
confound §24 already flagged. enable_ruin_recreate stays default OFF.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01R8agJBT2ZpmF3ErW7wi2wY
N=15 driver.search sweep of construction_beam_width 1 vs 4 (protocol
identical to c94's original 5-seed run, DESIGN.md §29): 6W/4L/5T, mean
fails 57.0->56.6, Wilcoxon p=0.84. Excluding seed 2's outlier the mean
flips slightly negative (56.3->56.9), confirming the §29 5-seed
"improvement" was that one outlier. construction_beam_width stays
default 1 on confirmed rather than precautionary grounds. DESIGN.md §30.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01R8agJBT2ZpmF3ErW7wi2wY
The raw-seed diagnostic (single constructed tree, byte-identical across
beam widths) was wrongly taken as proof a full driver.search run would
also be byte-identical. Actually running it (5 seeds/programme, budget
1500, n_workers=1) shows harbor-house diverges: 2 wins/1 loss/2 ties vs
greedy — a full bootstrap population hits beam-vs-greedy tie-breaks a
lone raw seed sample missed. programme-house stayed tied 5/5. Verdict
corrected from a confident null to inconclusive/mixed on harbor-house;
practical disposition unchanged (construction_beam_width stays default 1).
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01R8agJBT2ZpmF3ErW7wi2wY
operators._assign_adjacency_aware gains beam_width (default 1 = exact
prior greedy behaviour), threaded through constructive_topology/
lift_base_to_storeys/driver.search/search_staged as
construction_beam_width. Verified functioning on an adversarial
synthetic case, but byte-identical raw-seed output to greedy at every
width tested (1/4/8/20) on both example programmes -- no headroom for
the beam to find on this repo's programmes. DESIGN.md section 29.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01R8agJBT2ZpmF3ErW7wi2wY
§25 still said the evolve.py wiring and broader sweep were pending on
homemaker-py-cdl; that closed in the previous commit, so update §25's
stale forward-references and add §28 with the 46-file sweep results and
the collapse_insearch hot-path reasoning for leaving collapse_global's
own default off.
Two closed, substantive experiments were missing their DESIGN.md write-up
despite being referenced as prior art by later sections:
- 9o5/xi7/b3v (closed 2026-06-30/07-17): multi-use-leaf type superposition,
a full feature build + real A/B validation (negative — OFF beats ON on
both programme-house and harbor-house) + a veto-hatch follow-up for the
one genuine false-positive interchange class found. §17 and §20 both cite
its verdict directly but it never got its own section.
- mi7 (closed 2026-07-25): 3D bubble-diagram / topological-hop-distance
fitness signal prototype, tested against real evolved trajectories on two
programmes, both formulations null. bubble.py was left in the tree
uncommitted "as documented reference" by the closing session -- committing
it now (with two trivial ruff fixes: unused import, ambiguous var name) so
the reference this write-up makes to it is actually resolvable, plus a
CLAUDE.md module-list entry.
Numbered §26/§27 (appended, not inserted chronologically) to avoid
renumbering every cross-reference in §14-§25.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Locates the threshold f1d's programme-house/harbor-house split implied,
using four synthetic sizes (10/14/18/22 rooms) derived from programme-house
by scaling its bedroom+ensuite module count, since no natural third example
programme sits between the two. Results are noisy and non-monotonic (n=10
mild win, n=14 clean null, n=18 strongest trend at p=0.098, n=22 near-null)
rather than a clean decay with room count -- documented in DESIGN.md #24.
enable_ruin_recreate stays default OFF; filed homemaker-py-xyu as a
low-priority follow-up (larger-N at n=18, or a non-synthetic third example).
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
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
161 (2026-07-22) answered the "remaining open question" §19 left dangling —
threading fit into driver.search so shape_rotate/deslim can fire mid-GA —
but the result only ever landed in bd notes, never here. Also negative:
full-budget harbor-house A/B (seeds 0-3) shows no improvement, confirming
the finish-time finding at in-search scale. Both halves of §19's mechanism
space are now closed negative in the doc, matching bd state.
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
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
Document the collapse as built (default-on in evolve + homemaker-collapse CLI):
label-relative vs geometry-intrinsic fails, collapse_global mechanism (c/o/s
partition, hard level, adjacency relaxation, threshold objective, public-access
pin), the two measurement corrections, keep-better wrapper + wiring, and the
6-layout sweep verification. DESIGN.md is the system-of-record for users without
beads access.
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
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
Expose tournament_k (default 2) on search()/search_staged(), threaded into
both _tournament call sites and the staged path's internal search() calls;
HOMEMAKER_TOURNAMENT_K env knob in the scaled/staged harnesses; run_6zy_ab.sh
joint niche×k grid (RESUME-able).
Result (negative, acceptable): no (niche,k) cell beats the legacy (off,k=2)
baseline. Blank-slate programme-house (5 seeds) baseline mean 4.80 fails is the
best of the 6-cell grid; every k>2 and every niche=on cell is 6.0-7.0. Niching
bites (pop_distinct 16/16 vs 4-11) but sharper pressure does not convert it to
lower fails — §11.5 'diffuses effort' null is robust to selection pressure;
plateau stays reachability-bound (confirms §11.4/§11.5).
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>
