.beads/issues.jsonl

@@ -1,8 +1,3 @@
-{"id":"homemaker-py-erc.4","title":"Experiment: plot-filling / slack-aware constructive seeding","description":"Attacks the #2 factor (size/undersize 242) via the §12.3 paradox: rooms are undersize while 56% of the plot is empty. The shape floor is computed at TARGET dims, so construction never spends the slack. Scale leaves up to consume available plot area (proportionally, preserving target aspect) so rooms reach/exceed target — bigger leaves are also easier to keep compact, so this may help crinkliness/width too.\n\nBuilds on leu.2 (proportion-aware splits sized FROM target dims) by adding a fill step that scales the whole layout (or per-region) to the plot envelope instead of leaving slack as empty plot. Implementation in operators construction / _size_divisions_from_targets.\n\nNOTE: exact fix-site (construction vs inner loop) is decided by Diagnostic B — if B shows leaves park at target with unused plot, this construction lever is correct; if B shows the inner loop simply lacks an expansion gradient, prefer the inner-loop slack-expansion sibling instead. A/B vs §12.2 baseline, seeds 0/1/2, 20000 evals, staged, default-OFF. Record DESIGN.md §13.4.","status":"open","priority":1,"issue_type":"feature","owner":"bruno@postle.net","created_at":"2026-06-22T23:16:19Z","created_by":"Bruno Postle","updated_at":"2026-06-22T23:16:19Z","dependencies":[{"issue_id":"homemaker-py-erc.4","depends_on_id":"homemaker-py-erc","type":"parent-child","created_at":"2026-06-23T00:16:19Z","created_by":"Bruno Postle","metadata":"{}"},{"issue_id":"homemaker-py-erc.4","depends_on_id":"homemaker-py-erc.2","type":"blocks","created_at":"2026-06-23T00:16:45Z","created_by":"Bruno Postle","metadata":"{}"}],"dependency_count":1,"dependent_count":0,"comment_count":0}
-{"id":"homemaker-py-erc.3","title":"Experiment: leaf-sharing / multi-room leaves in construction","description":"Strongest untried construction lever. §12.3 named 'merge or share leaves across same-class rooms' but c3g never tested it — c3g only coarsened the circulation spine (circ_divisor), trading shape gains for equal access/adjacency damage (null). Leaf-sharing is DIFFERENT: it reduces leaf count by collapsing same-class rooms (e.g. several O/storage, or same-type repeated rooms) into a shared leaf, attacking crinkliness(346)+size(242) directly WITHOUT coarsening circulation — so it should dodge the access penalty that sank c3g.\n\nImplementation sketch: in operators.constructive_topology (+ lift path), allow rooms of the same class/type (and compatible adjacency) to be instantiated as one larger leaf rather than one-leaf-per-room, lowering leaves-per-room from ~1.4 toward 1.0 or below. Honour storey_minimum and required-room presence (a shared leaf must still satisfy each merged room's presence/area in the fitness check, or the merge must be limited to rooms the fitness treats as fungible).\n\nTests the deepest open question: whether 52 rooms simply cannot be well-shaped as 52 leaves at this density. A/B vs §12.2 baseline (maple 136.0, harbor 74.0), seeds 0/1/2, 20000 evals, staged; default-OFF toggle so controls reproduce. Record DESIGN.md §13.3.","status":"open","priority":1,"issue_type":"feature","owner":"bruno@postle.net","created_at":"2026-06-22T23:16:15Z","created_by":"Bruno Postle","updated_at":"2026-06-22T23:16:15Z","dependencies":[{"issue_id":"homemaker-py-erc.3","depends_on_id":"homemaker-py-erc","type":"parent-child","created_at":"2026-06-23T00:16:15Z","created_by":"Bruno Postle","metadata":"{}"},{"issue_id":"homemaker-py-erc.3","depends_on_id":"homemaker-py-erc.1","type":"blocks","created_at":"2026-06-23T00:16:42Z","created_by":"Bruno Postle","metadata":"{}"}],"dependency_count":1,"dependent_count":0,"comment_count":0}
-{"id":"homemaker-py-erc.2","title":"Diagnostic B: undersize-despite-slack localization (construction-target vs inner-loop-fill)","description":"GATES the plot-fill-construction vs inner-loop-expansion decision. The paradox from §12.3: plot utilisation is 0.44 (56% empty) yet size fails are 242 (rooms UNDERSIZE). Where is the slack stranded, and at which stage should it be spent?\n\nMeasure, on constructive seeds for maple-court + harbor (seeds 0/1/2):\n1. After CONSTRUCTION (before inner loop): per-leaf achieved area vs target area, and total occupied vs plot area. Are leaves parked at target with the slack left as unused plot, or is the slack distributed but mis-shaped?\n2. After the INNER LOOP optimises ratios: did size fails drop — i.e. does the ratio solve already expand leaves into slack, or does it have no gradient/incentive to exceed target? Compare predicted_shape_fails (target geometry) vs achieved size fails (post-optimise).\n\nThe §12.3 calibration (floor at TARGET dims ≈ achieved) already hints the inner loop is NOT filling slack — confirm and quantify, and identify whether the gap is (a) construction targets too-small dims given the plot, or (b) the objective gives no reward for exceeding target area. Output: DESIGN.md §13.2.\n\nDECISION RULE: if rooms are parked at target with unused plot → fix in CONSTRUCTION (plot-fill, erc child). If the inner loop has the room to expand but no objective gradient → fix in the INNER LOOP (slack-expansion term, erc child). Reads only; no behaviour change.","status":"open","priority":1,"issue_type":"task","owner":"bruno@postle.net","created_at":"2026-06-22T23:15:42Z","created_by":"Bruno Postle","updated_at":"2026-06-22T23:15:42Z","dependencies":[{"issue_id":"homemaker-py-erc.2","depends_on_id":"homemaker-py-erc","type":"parent-child","created_at":"2026-06-23T00:15:42Z","created_by":"Bruno Postle","metadata":"{}"}],"dependency_count":0,"dependent_count":2,"comment_count":0}
-{"id":"homemaker-py-erc.1","title":"Diagnostic A: per-leaf shape-fail vs density/granularity profile","description":"GATES the leaf-sharing vs compactness-cuts decision. The open question from §12.3: is the shape floor intrinsic to slicing at this leaf density (→ fewer leaves is the only lever), or fixable by better-shaped cuts at the same leaf count?\n\nMeasure: per-leaf shape-fail rate (crinkliness/size/proportion/width, broken out) as a function of leaves-per-room and plot utilisation, across the existing programmes spanning density — harbor (16 rooms) vs maple-court (52 rooms) — and, if cheap, a synthetic sweep that holds the programme fixed while varying leaf count (e.g. reuse the circ_divisor / construction granularity knob already in place to generate coarser vs finer constructive seeds and score predicted_shape_fails per leaf).\n\nReads, does not change behaviour: use operators.predicted_shape_fails + the per-leaf factor breakdown already in fitness.py (the §12.3 residual table was produced this way). Output: a table of per-leaf shape-fail vs density, written into DESIGN.md §13.1.\n\nDECISION RULE (write it into the verdict): if per-leaf shape-fail is FLAT across densities → floor is intrinsic to slicing density → prioritise leaf-sharing (erc child), deprioritise/close compactness-cuts. If it RISES with density → better cuts can pay → keep compactness-cuts. This is a measurement, not an experiment; no A/B, no baseline reproduction needed.","status":"open","priority":1,"issue_type":"task","owner":"bruno@postle.net","created_at":"2026-06-22T23:15:40Z","created_by":"Bruno Postle","updated_at":"2026-06-22T23:15:40Z","dependencies":[{"issue_id":"homemaker-py-erc.1","depends_on_id":"homemaker-py-erc","type":"parent-child","created_at":"2026-06-23T00:15:39Z","created_by":"Bruno Postle","metadata":"{}"}],"dependency_count":0,"dependent_count":2,"comment_count":0}
-{"id":"homemaker-py-erc","title":"Phase 8: lower the geometry/shape floor — construction \u0026 inner-loop levers","description":"Continuation of Phase 7 (leu, closed). Phase 7's decisive finding (§12.3 calibration): predicted_shape_fails at the best achievable geometry ≈ the achieved total fail count (maple floor 121-163 vs achieved 126-148). Therefore SEARCH MACHINERY CANNOT HELP — there is no lower-fail basin for the constructed topologies to reach; the floor IS the result. Scoreboard: 4/4 wins from construction/seed quality (§11.2, §11.6, §11.7, §12.2), 0/3 from search machinery (§11.4, §11.5, §12.3). The only way to lower fails is to lower the geometry FLOOR.\n\nResidual decomposition (maple-court, 6 constructive seeds, §12.3): crinkliness 346 + size 242 (undersize) + proportion 121 + width 102, with plot utilisation only 0.44 (56% of plot empty) yet rooms UNDERSIZE. Diagnosed mechanism: over-granular construction — 73 leaves for 52 rooms — every leaf high perimeter/area (crinkliness) and below target area (size). c3g tested ONE granularity lever (circulation-spine coarsening via circ_divisor) → null (shape gain cancelled by equal access/adjacency damage). The other named levers were never tested.\n\nThis epic runs DIAGNOSTICS FIRST to decide which floor-lowering lever to invest in, then the construction/inner-loop experiments in dependency order. Tier-3 search-machinery bets (island model psk, tournament pressure 6zy) are tracked but LOW prior — do not invest there until something moves the floor.\n\nShared protocol (every experiment): A/B on maple-court + harbor, seeds 0/1/2, 20000 evals, staged; controls MUST reproduce the §12.2 baseline (maple 136.0, harbor 74.0); record verdict in DESIGN.md (new §13.x). Same discipline as every lever in §11-§12.","status":"open","priority":1,"issue_type":"epic","owner":"bruno@postle.net","created_at":"2026-06-22T23:14:56Z","created_by":"Bruno Postle","updated_at":"2026-06-22T23:14:56Z","dependency_count":0,"dependent_count":0,"comment_count":0}
 {"id":"homemaker-py-leu.1","title":"Larger-than-house benchmark programme (\u003e16 rooms) + baseline","description":"PREREQUISITE for the whole epic. Harbor (16 rooms) is the biggest real programme in examples/; 9gp's scaling claim ('\u003e16 rooms') and acceptance criterion ('larger-than-house programme') cannot be measured without a bigger one.\n\nBuild a reproducible benchmark programme larger than harbor (target ~24-32 rooms, multi-storey, with a realistic per-level required-room partition and adjacency-to-c load like harbor's). Provide its patterns.config / costs.config (reuse config inheritance, homemaker-py-n5k) and an init.dom, mirroring the examples/harbor-house layout. Wire it into the existing experiment harnesses (run_search_scaled.py / run_staged_search.py) and record a BASELINE total-fail count at a fixed budget for the current default search (adjacency-aware seeding + staged), exactly as §11.6/§11.7 reported harbor. This baseline is the yardstick proportion-seeding and 9gp are measured against.\n\nDeliverable: examples/\u003cnew\u003e/ with configs+init.dom, a documented baseline (seeds 0-2, total fails at budget), recorded in DESIGN.md §12.1 + bead notes.","acceptance_criteria":"A \u003e16-room multi-storey benchmark exists under examples/, runs through the current harness, and has a documented baseline fail count (\u003e=3 seeds) recorded in DESIGN.md.","notes":"Benchmark delivered: examples/maple-court/ (26 entries / 52 rooms / 3 storeys, ~1015 m2 internal, ~790 m2/floor plot). Mirrors harbor's adjacency-to-c load + secondary adjacencies; room codes avoid generic c/o/s leading letters. Baseline (staged adjacency-aware, URB_NO_OCCLUSION=1, 20000 evals): seed0=145, seed1=158, seed2=152, mean=151.7 fails. All native re-score OK. Best (145, seed0) saved as generated.dom. Recorded in DESIGN.md §12.1.","status":"closed","priority":1,"issue_type":"task","assignee":"Bruno Postle","owner":"bruno@postle.net","created_at":"2026-06-19T11:13:59Z","created_by":"Bruno Postle","updated_at":"2026-06-19T12:33:31Z","started_at":"2026-06-19T11:17:25Z","closed_at":"2026-06-19T12:33:31Z","close_reason":"Closed","dependencies":[{"issue_id":"homemaker-py-leu.1","depends_on_id":"homemaker-py-leu","type":"parent-child","created_at":"2026-06-19T12:13:59Z","created_by":"Bruno Postle","metadata":"{}"}],"dependency_count":0,"dependent_count":2,"comment_count":0}
 {"id":"homemaker-py-leu","title":"Phase 7: scaling validation \u0026 residual reduction (post-c4c)","description":"Continuation of the closed c4c epic (Phase 6). Phase 6 evidence is decisive about WHERE leverage lives: the two search-machinery experiments (§11.4 graded high-fail objective, §11.5 niching+restarts) BOTH landed negative and BOTH concluded the high-fail plateau is a REACHABILITY problem (operators+encoding cannot reach low-fail basins), not population-management or objective-shaping. The two wins (§11.6 adjacency-aware seeding, §11.7 adjacency-aware lift) came from CONSTRUCTION/SEED quality. Harbor is now ~85 fails (best 78), down from the 95/105 plateaus; the residual is geometry/shape-bound (size/proportion/crinkliness).\n\nThree gaps block further scaling progress and must be done in order:\n1. There is NO larger-than-house benchmark. Harbor (16 rooms) is the biggest real programme in examples/. 9gp's headline claim is scaling \u003e16 rooms and its acceptance criterion demands 'a larger-than-house programme' to measure on — so a bigger benchmark is a PREREQUISITE, not optional.\n2. Proportion-aware seeding: §11.6 noted the seed uses 0.5 splits -\u003e 'more, smaller leaves' -\u003e geometry fails. Sizing splits from target dims attacks the §11.7 geometry residual directly, in the proven construction direction; cheaper than an encoding rewrite.\n3. 9gp (canonical Polish encoding) must be RE-SCOPED: its 'topology signature for niching' justification is dead (§11.5 falsified niching; genome.signature already exists as the cheap stand-in). The surviving, evidence-supported parts are M1/M2/M3 Wong-Liu moves (reachability) and shape-feasibility pruning (residual + inner-loop budget = scaling).\n\nOrdering rationale: benchmark first (makes scaling measurable for everything downstream), then the cheap proven-direction seeding win (sets the strongest baseline), then the re-scoped canonical-encoding capstone (lands on the best seed, with a benchmark to prove its scaling claim).","design":"Do NOT build search/selection machinery on unmeasured premises — that is exactly what §11.4/§11.5 did and both regressed. Every child lands an experiment with results recorded in DESIGN.md §12.x + bead notes, same discipline as Phase 6. The benchmark child is the root dependency; proportion-seeding depends on it (so the win is measured at scale too); re-scoped 9gp depends on both (best baseline + scaling measurement).","acceptance_criteria":"(1) A reproducible \u003e16-room benchmark exists with a documented baseline fail count; (2) proportion-aware seeding shows a measured fail reduction on harbor AND the new benchmark; (3) re-scoped 9gp lands M1/M2/M3 + shape feasibility and shows measured search improvement on the larger-than-house benchmark.","notes":"EPIC COMPLETE. leu.1 established the \u003e16-room maple-court benchmark (baseline 151.7→ leu.2 136.0). leu.2 proportion-aware seeding: measured win on both larger programmes (harbor -13%, maple -10%), default-on. 9gp (re-scoped): M3 reassociate + shape-feasibility filter landed + measured NEGATIVE — the residual is the geometry/shape floor of the constructed layouts, not reachability/feasibility-bound. Net: Phase 7 reduced the benchmark residual via construction (leu.2) and validated that further search-machinery gains are unavailable (9gp), a 3rd search-machinery negative vs 4 construction wins. See DESIGN.md §12.","status":"closed","priority":1,"issue_type":"epic","owner":"bruno@postle.net","created_at":"2026-06-19T11:13:29Z","created_by":"Bruno Postle","updated_at":"2026-06-21T06:21:30Z","closed_at":"2026-06-21T06:21:01Z","close_reason":"all steps complete","dependency_count":0,"dependent_count":0,"comment_count":0}
 {"id":"homemaker-py-c4c.3","title":"Staged per-floor search (curriculum: credible base floor, then upper floors as deltas)","description":"Search the genome in its causal dependency order. The base-floor tree is the master; upper storeys are deltas (Below-inheritance). The programme partitions rooms by required level (harbor: 10 L0, 4 L1, 2 free), so each floor's target room set is known up front. Today the search discovers both floors simultaneously via random typing + the rare/drastic level_add (weighted 0.2) — an uncontrolled, degenerate version of staging.\nStage 1 — base floor: search the single-storey tree over the level-0 room set, dimensionality reduced (one tree, no deltas).\nStage 2 — upper floors as deltas: seed each upper storey with ITS required room set (via the construction op, homemaker-py-c4c.2), search the deltas; keep the base MUTABLE at low probability so it can adapt to upper-floor pressure.\nCRITICAL non-goal: do NOT hard-freeze the base. A base optimised purely as ground floor is a §4.2-style partial objective and can be a bad SUBSTRATE. Stage 1 objective must include (a) a reserved, vertically-alignable circulation core and (b) a substrate-readiness term: enough divisible area/cut structure to host the level-1 room set later.","design":"Premise gated by homemaker-py-c4c.1: only high-value if single-storey construction already reaches low fails. Substrate-readiness proxy candidates: count of base leaves large enough to subdivide for L1 rooms; presence of a core node with vertical continuity. Stage transition: when stage-1 base hits a fails/score threshold or budget fraction, freeze-soft and open the delta genome. Composes with canonical encoding (homemaker-py-9gp) — deltas are where redundancy/coarse moves hurt most.","acceptance_criteria":"Staged search beats single-stage on harbor-house (best fails/score), measured at equal native-fitness budget and recorded in DESIGN.md §11.x + bead notes. Reserved-core + substrate-readiness shown to prevent the bungalow trap (stage-2 does not have to carve a core from scratch — track core-carving moves). No regression on programme-house.","notes":"DONE. Implemented driver.search_staged (Stage 1 single-storey base over level-0 set with substrate-readiness ranking bonus; Stage 2 upper floors lifted as constructed deltas, base mutable at 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}, driver.search rank_bonus_fn/seed_factory/base_p hooks, 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-\u003e18 + edge 12-\u003e8, small access cost +5. Anti-bungalow CONFIRMED: all Stage-2 core_divide/undivide in winning lineage are noops (core inherited, not carved). Regression PASS: programme-house warmstart-2f4 still reaches whole-pop 1-fail. DESIGN.md §11.3 filled. Remaining high-fail plateau is §11.4 (graded objective) territory.","status":"closed","priority":1,"issue_type":"feature","assignee":"Bruno Postle","owner":"bruno@postle.net","created_at":"2026-06-17T19:01:01Z","created_by":"Bruno Postle","updated_at":"2026-06-18T05:04:48Z","started_at":"2026-06-18T04:25:07Z","closed_at":"2026-06-18T05:04:48Z","close_reason":"Closed","dependencies":[{"issue_id":"homemaker-py-c4c.3","depends_on_id":"homemaker-py-c4c","type":"parent-child","created_at":"2026-06-17T20:01:00Z","created_by":"Bruno Postle","metadata":"{}"},{"issue_id":"homemaker-py-c4c.3","depends_on_id":"homemaker-py-c4c.1","type":"blocks","created_at":"2026-06-17T20:01:00Z","created_by":"Bruno Postle","metadata":"{}"},{"issue_id":"homemaker-py-c4c.3","depends_on_id":"homemaker-py-c4c.2","type":"blocks","created_at":"2026-06-17T20:01:01Z","created_by":"Bruno Postle","metadata":"{}"}],"dependency_count":2,"dependent_count":0,"comment_count":0}
@@ -21,8 +16,6 @@
 {"id":"homemaker-py-1p0","title":"Geometry inner loop: full-objective equal-offset ratio optimiser","description":"DESIGN.md §5.1, §7 Phase 1. Productionise experiments/optimize_fullfitness.py into homemaker: optimise(topology, x0=None) -\u003e (geometry, fitness). DOF = equal-offset division ratios of free branches (solver.free_branches, lowest-storey cut ownership), clipped to [eps, 1-eps]. Objective = full oracle fitness (never a proxy — §4.2 falsified). Must support warm-start x0 (§5.6) and a population/batch evaluation mode so each iteration scores via one batched oracle call (§4.6).","acceptance_criteria":"Reproduces or exceeds §4.5 gains (x1.24–x1.67, no new failures) on 2f45907, candidate-002, c964435; works as a library call on any corpus .dom","status":"closed","priority":1,"issue_type":"feature","assignee":"Bruno Postle","owner":"bruno@postle.net","created_at":"2026-06-11T23:36:58Z","created_by":"Bruno Postle","updated_at":"2026-06-12T08:46:31Z","started_at":"2026-06-12T00:14:19Z","closed_at":"2026-06-12T08:46:31Z","close_reason":"innerloop.optimise() lands: batched CMA-ES sigma ladder (0.05/0.15, IPOP popsize doubling, deterministic seeding) over equal-offset free-branch ratios vs full oracle fitness; warm-start x0 supported. Acceptance vs unprojected originals: x1.65/x1.66/x1.58 against bars x1.24/x1.67/x1.59, no new failures, 46 oracle calls vs NM's 200. Two near-bar results accepted as reproduced-within-noise (1% tol) — draw spread brackets the single-NM-draw bars; approved by Bruno 2026-06-12. Gotchas: equal-offset projection of legacy unequal cuts loses fitness/adds failures (midpoint projection used); pycma seed=0 means clock-seeded.","dependencies":[{"issue_id":"homemaker-py-1p0","depends_on_id":"homemaker-py-av5","type":"blocks","created_at":"2026-06-12T00:39:33Z","created_by":"Bruno Postle","metadata":"{}"}],"dependency_count":1,"dependent_count":3,"comment_count":0}
 {"id":"homemaker-py-8cs","title":"Experiment: warm-vs-cold start of inner loop (Lamarckian inheritance)","description":"DESIGN.md §5.6, §4.6. Warm-starting a child topology's inner loop from the parent's optimised ratios is the main lever for cutting per-topology cost (~3 min/topology cold). Apply single topology mutations to optimised corpus designs, re-optimise warm (surviving cuts keep values, new cuts get heuristic defaults) vs cold, compare oracle-call counts to convergence at equal final fitness.","acceptance_criteria":"Speedup factor measured across \u003e=10 mutated topologies; decision recorded (expect order-of-magnitude; if \u003c2x, revisit §4.6 Phase-2 scoping)","notes":"Experiment script committed (experiments/warm_vs_cold.py, 1cc86c8) and machinery validated oracle-free; one mutated child scored through the oracle OK. Waiting on homemaker-py-gp2 reference run to finish, then execute under URB_NO_OCCLUSION=1 (3 parents x 400 evals + 12 children x 2 x 200 evals, ~1.5-2 h oracle time). Default budgets: parent 400, child 200; target = evals to 95% of best final.","status":"closed","priority":1,"issue_type":"task","owner":"bruno@postle.net","created_at":"2026-06-11T23:36:58Z","created_by":"Bruno Postle","updated_at":"2026-06-12T11:44:45Z","closed_at":"2026-06-12T11:44:45Z","close_reason":"Measured (URB_NO_OCCLUSION=1, parent budget 400, child 200, 12 single mutations across 3 designs): cold start reached 95% of warm final in 0/12 cases within budget — speedup unbounded at practical budgets; warm finals beat cold finals x1.2-x4 in 12/12; 6/12 warm starts were within 95% at 1 eval (near-neutral mutations). Decision: Lamarckian warm-starting is MANDATORY in the memetic driver (homemaker-py-b39), not an optimisation; cold starts produce strictly worse geometry at equal budget. Note: 2 undivides were exactly fitness-neutral (same-type merge == Merge_Divided equivalence) — locality datum for homemaker-py-nyb.","dependencies":[{"issue_id":"homemaker-py-8cs","depends_on_id":"homemaker-py-1p0","type":"blocks","created_at":"2026-06-12T00:39:34Z","created_by":"Bruno Postle","metadata":"{}"}],"dependency_count":1,"dependent_count":0,"comment_count":0}
 {"id":"homemaker-py-av5","title":"Batched oracle: score many .dom files per invocation","description":"oracle.py currently scores one .dom per urb-fitness.pl call (~1.65 s/dom). DESIGN.md §4.6: batching amortises Perl startup to ~0.99 s/dom and is required so population/batch optimisers can score a whole generation in one oracle call. Extend oracle.py with a batch API: write N .dom files, one perl invocation, parse N .score/.fails pairs. Keep the single-file path for compatibility.","acceptance_criteria":"Batch of 35 corpus files scores in one perl invocation; per-file results identical to single-file calls; measured s/dom reported","status":"closed","priority":1,"issue_type":"task","assignee":"Bruno Postle","owner":"bruno@postle.net","created_at":"2026-06-11T23:36:56Z","created_by":"Bruno Postle","updated_at":"2026-06-12T00:14:06Z","started_at":"2026-06-11T23:50:40Z","closed_at":"2026-06-12T00:14:06Z","close_reason":"score_batch() lands in oracle.py; 35-file corpus parity verified single-vs-batch (1e-12 rel fitness, exact fail sets); 0.98 s/dom batched vs 1.27 single, x1.30","dependency_count":0,"dependent_count":1,"comment_count":0}
-{"id":"homemaker-py-erc.6","title":"Experiment: inner-loop slack-expansion objective term","description":"Inner-loop counterpart to plot-fill construction. If Diagnostic B shows the inner loop has room to expand leaves into slack but no objective gradient to do so (the scalar rewards hitting target area but not exceeding it where slack exists), add a term/incentive so the ratio optimiser pushes leaf boundaries out to consume neighbouring slack and satisfy size, rather than parking at target.\n\nCONDITIONAL on Diagnostic B: build this only if B localizes the gap to the inner loop (room to expand, no gradient); if B shows construction targets too-small dims, prefer the plot-fill construction sibling. Must preserve the §5.4 inner-loop cliff / §4.9 lexicographic protection — the term sits where it cannot displace the fail-count ordering. A/B vs §12.2 baseline, seeds 0/1/2, 20000 evals, staged, default-OFF. Record DESIGN.md §13.6.","status":"open","priority":2,"issue_type":"feature","owner":"bruno@postle.net","created_at":"2026-06-22T23:16:24Z","created_by":"Bruno Postle","updated_at":"2026-06-22T23:16:24Z","dependencies":[{"issue_id":"homemaker-py-erc.6","depends_on_id":"homemaker-py-erc","type":"parent-child","created_at":"2026-06-23T00:16:23Z","created_by":"Bruno Postle","metadata":"{}"},{"issue_id":"homemaker-py-erc.6","depends_on_id":"homemaker-py-erc.2","type":"blocks","created_at":"2026-06-23T00:16:47Z","created_by":"Bruno Postle","metadata":"{}"}],"dependency_count":1,"dependent_count":0,"comment_count":0}
-{"id":"homemaker-py-erc.5","title":"Experiment: compactness-aware cuts (minimize leaf perimeter/area)","description":"Attacks the #1 factor, crinkliness (346) — a per-leaf perimeter/area property DISTINCT from proportion (aspect ratio). Proportion-aware seeding (leu.2) sizes splits but does not bias toward balanced, square-ish subdivision. Add a KD-tree-style 'keep both children compact' cut rule (prefer the cut orientation/position that minimises summed child perimeter/area) in construction.\n\nCONDITIONAL on Diagnostic A: if A shows per-leaf shape-fail is FLAT across densities (floor intrinsic to slicing density), better cuts at the same leaf count will not pay → this should be closed wont-fix in favour of leaf-sharing. Only build if A shows shape-fail RISES with density. A/B vs §12.2 baseline, seeds 0/1/2, 20000 evals, staged, default-OFF. Record DESIGN.md §13.5.","status":"open","priority":2,"issue_type":"feature","owner":"bruno@postle.net","created_at":"2026-06-22T23:16:21Z","created_by":"Bruno Postle","updated_at":"2026-06-22T23:16:21Z","dependencies":[{"issue_id":"homemaker-py-erc.5","depends_on_id":"homemaker-py-erc","type":"parent-child","created_at":"2026-06-23T00:16:21Z","created_by":"Bruno Postle","metadata":"{}"},{"issue_id":"homemaker-py-erc.5","depends_on_id":"homemaker-py-erc.1","type":"blocks","created_at":"2026-06-23T00:16:43Z","created_by":"Bruno Postle","metadata":"{}"}],"dependency_count":1,"dependent_count":0,"comment_count":0}
 {"id":"homemaker-py-xcy","title":"Constructive seeder is nondeterministic across processes (id-based set iteration)","description":"BUG / reproducibility. operators._assign_adjacency_aware builds Python sets of dom.Node objects (circ/dominated/frontier) and iterates them; set iteration order for objects is id()-based, which varies across processes. Result: constructive_topology(seed=0, adjacency_aware=True) yields DIFFERENT topology signatures in separate processes (verified: sig hashes 4480 vs 16064 for maple-court seed 0), so the whole staged search trajectory is non-reproducible run-to-run. Measured single-run noise ~±3 fails (c3g div=3 control 129 vs §12.3 126 for the same maple seed 0). IMPACT: per-seed numbers in the §11/§12 ledger are not reproducible; only multi-seed MEANS are stable, and small effects (±3-4, e.g. the §12.3 negatives) are near the noise floor. FIX: make the dominating-set/assignment iteration deterministic — sort candidate nodes by the existing idx (leaf index) instead of relying on set iteration order, or drive all tie-breaks through idx. Re-establishing determinism will shift baselines slightly; note in DESIGN.md. Files: operators._assign_adjacency_aware (circ set, dominated union, frontier, the for s in circ loops).","notes":"RESOLVED with a corrected diagnosis (operator: investigated 2026-06-22).\n\nMISDIAGNOSIS: the constructive seeder is NOT nondeterministic. _assign_adjacency_aware ends every max/min with a unique idx tiebreak (-idx[L]); its set unions (circ/dominated/frontier) are used only for membership, so iteration order never leaks. Proven: constructive_topology(seed=0, adjacency_aware AND not) gives BYTE-IDENTICAL signatures across processes for all four example programmes (stable sha1, e.g. maple-court aa=e688f744326b in 3 separate processes). The cited '4480 vs 16064' was a MEASUREMENT ARTIFACT: Python's builtin hash() of a str is salted per-process (PYTHONHASHSEED), so hashing an IDENTICAL signature string in two processes yields different ints (reproduced: 51920/5342/59970 for one identical string). Serial search (workers=1) is byte-for-byte reproducible (identical .dom across runs).\n\nREAL BUG (fixed): parallel-only nondeterminism in driver._run_batch. It admitted futures via concurrent.futures.as_completed -\u003e completion order varies run-to-run, 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) — the real source of the +-3..6 'noise'. FIX: iterate the futures list in SUBMISSION order (block on each f.result() in turn; all still run concurrently), reproducing the serial admission sequence. After fix: two workers=4 runs are byte-identical (162 fails, identical .dom). 211 tests pass.\n\nIMPLICATION FOR LEDGER: per-seed numbers are reproducible ONLY for a fixed worker count. Serial != parallel is EXPECTED (children-per-iteration = 1 vs n_workers changes batch granularity, hence the search), not nondeterminism. Any ledger A/B comparing runs at DIFFERENT worker counts (or pre-fix parallel) conflated this with a real effect — re-run sub-+-3 effects at a fixed worker count.","status":"closed","priority":2,"issue_type":"bug","owner":"bruno@postle.net","created_at":"2026-06-21T20:39:09Z","created_by":"Bruno Postle","updated_at":"2026-06-22T22:13:17Z","closed_at":"2026-06-22T22:13:17Z","close_reason":"Closed","dependency_count":0,"dependent_count":0,"comment_count":0}
 {"id":"homemaker-py-9gp.2","title":"M3 Wong-Liu re-association reachability move","description":"9gp.2: add mutate_reassociate, the associativity move (a|b)|c \u003c-\u003e a|(b|c) (same-axis tree rotation on owned/live cuts) missing from the swap(M1)/rotate(M2) set. Targets the §11.4/§11.5 reachability bottleneck. Round-trip/invariant tests. MEASURE value on maple-court vs leu.2 baseline — either result is a valid verdict per the re-scoped bead. DESIGN.md §12.3.","notes":"MEASURED — NEGATIVE (DESIGN.md §12.3). M3 reassociate landed + A/B'd: maple 136.0→139.3, harbor 74.0→78.0 (neutral-to-worse, never a win) across seeds 0/1/2. Reaches new tree shapes but they are not better — third independent negative on search machinery (§11.4/§11.5/§12.3). Kept default-OFF. Valid verdict per re-scope.","status":"closed","priority":2,"issue_type":"feature","assignee":"Bruno Postle","owner":"bruno@postle.net","created_at":"2026-06-20T16:54:07Z","created_by":"Bruno Postle","updated_at":"2026-06-21T06:20:43Z","started_at":"2026-06-20T17:54:15Z","closed_at":"2026-06-21T06:20:43Z","close_reason":"Closed","dependencies":[{"issue_id":"homemaker-py-9gp.2","depends_on_id":"homemaker-py-9gp","type":"parent-child","created_at":"2026-06-20T17:54:07Z","created_by":"Bruno Postle","metadata":"{}"}],"dependency_count":0,"dependent_count":0,"comment_count":0}
 {"id":"homemaker-py-9gp.1","title":"Shape-feasibility pre-filter before inner loop","description":"9gp.1: predict per-leaf shape fails (size/width/proportion/crinkliness) at the proportion-aware target geometry, prune clearly-infeasible topologies before the inner loop so budget flows to feasible ones. Reuse operators._size_divisions_from_targets + fitness quality methods. Default OFF; threshold is a measured parameter. Hook in driver._evaluate. Measure on maple-court + harbor vs leu.2 baseline. DESIGN.md §12.3.","notes":"MEASURED — NEGATIVE (DESIGN.md §12.3). Shape-feasibility filter landed + A/B'd: maple 136.0→140.0, harbor 74.0→77.0. Filter DID prune/explore more topologies in several runs, but extra topologies didn't lower fails. Calibration: shape floor ≈ achieved total (geometry-bound residual, confirms §11.7), so no lower-fail basin for saved budget to find. Kept default-OFF.","status":"closed","priority":2,"issue_type":"feature","assignee":"Bruno Postle","owner":"bruno@postle.net","created_at":"2026-06-20T16:53:48Z","created_by":"Bruno Postle","updated_at":"2026-06-21T06:20:41Z","started_at":"2026-06-20T16:54:15Z","closed_at":"2026-06-21T06:20:41Z","close_reason":"Closed","dependencies":[{"issue_id":"homemaker-py-9gp.1","depends_on_id":"homemaker-py-9gp","type":"parent-child","created_at":"2026-06-20T17:53:48Z","created_by":"Bruno Postle","metadata":"{}"}],"dependency_count":0,"dependent_count":0,"comment_count":0}
@@ -42,7 +35,6 @@
 {"id":"homemaker-py-nyb","title":"High-locality topology operators (mutation + subtree crossover)","description":"DESIGN.md §5, §7 Phase 2, §8.4. Mutation moves: divide/undivide leaf, swap children, rotate cut, retype leaf, per-floor delta edits, storey add/delete (cf. Urb Mutate.pm — but geometry sliding belongs to the inner loop, not the operator set). Crossover: area-matched subtree exchange (a subtree = a contiguous region, so crossover is meaningful — Crossover.pm). Operators must be high-locality: small genome change =\u003e small phenotype change, so warm-started inner loops stay cheap.","acceptance_criteria":"Each operator produces valid genomes (oracle scores them without error); locality measured (mean fitness/geometry perturbation per operator)","status":"closed","priority":2,"issue_type":"feature","assignee":"Bruno Postle","owner":"bruno@postle.net","created_at":"2026-06-11T23:37:27Z","created_by":"Bruno Postle","updated_at":"2026-06-12T13:07:37Z","started_at":"2026-06-12T12:54:23Z","closed_at":"2026-06-12T13:07:37Z","close_reason":"operators.py lands: 7 mutations + area-matched crossover, valid-by-construction via genome.encode repair. 115/115 oracle-valid children; locality measured: geom-pert 0.07-0.33 per op, fitness-pert 0.68-0.99 (0.5^n cliff flags raw moves — warm restart + penalty reshaping confirmed load-bearing). Also fixed dom._link stale below-links on structural mutation.","dependencies":[{"issue_id":"homemaker-py-nyb","depends_on_id":"homemaker-py-k2g","type":"blocks","created_at":"2026-06-12T00:39:36Z","created_by":"Bruno Postle","metadata":"{}"}],"dependency_count":1,"dependent_count":1,"comment_count":0}
 {"id":"homemaker-py-k2g","title":"Topology genome: base-floor tree + per-floor deltas + type assignment","description":"DESIGN.md §5.2, §7 Phase 2. Genome = base-floor slicing topology (primary) + per-leaf type assignment + per-floor divide/undivide deltas (Below-inheritance as regulariser; cut owned by lowest storey where its path is divided — §10). Must round-trip to/from dom.py Node trees so the oracle and inner loop consume it directly. Includes storey count and per-floor type overrides.","acceptance_criteria":"Genome \u003c-\u003e .dom round-trip on all 35 corpus files preserves fitness; multi-storey wall stacking preserved","status":"closed","priority":2,"issue_type":"feature","assignee":"Bruno Postle","owner":"bruno@postle.net","created_at":"2026-06-11T23:37:26Z","created_by":"Bruno Postle","updated_at":"2026-06-12T12:52:34Z","started_at":"2026-06-12T10:55:21Z","closed_at":"2026-06-12T12:52:34Z","close_reason":"genome.py encode/decode lands. 35/35 oracle fitness parity after round-trip (flag-on); genome fixed-point + owned-projection tests. Dead-field discovery: corpus upper storeys carry drifted dead divisions (97) and rotations (187) — canonicalised by decode, validated fitness-neutral.","dependency_count":0,"dependent_count":1,"comment_count":0}
 {"id":"homemaker-py-d0s","title":"Experiment: inner-loop optimiser bake-off at equal oracle budgets","description":"DESIGN.md §7 Phase 1, §8.3. DOF is only ~rooms-1 (6–7 on corpus). Compare Nelder-Mead vs CMA-ES vs batched multi-start pattern search at equal oracle-call budgets, measuring fitness gained per oracle call and wall-clock (batch-friendliness matters — §4.6). Measure, don't commit blind.","acceptance_criteria":"Table of fitness-per-budget across \u003e=3 candidates; one optimiser chosen and recorded in DESIGN.md","status":"closed","priority":2,"issue_type":"task","assignee":"Bruno Postle","owner":"bruno@postle.net","created_at":"2026-06-11T23:36:59Z","created_by":"Bruno Postle","updated_at":"2026-06-13T08:48:13Z","started_at":"2026-06-12T21:22:15Z","closed_at":"2026-06-13T08:48:13Z","close_reason":"Bake-off complete: CMA-ES confirmed as Phase 1/2 optimiser. NM wins quality per eval but sequential architecture incompatible with batching (§4.6). Compass stalls on narrow valleys. Results in DESIGN.md §8.3 and experiments/bakeoff_innerloop.*","dependencies":[{"issue_id":"homemaker-py-d0s","depends_on_id":"homemaker-py-1p0","type":"blocks","created_at":"2026-06-12T00:39:35Z","created_by":"Bruno Postle","metadata":"{}"}],"dependency_count":1,"dependent_count":0,"comment_count":0}
-{"id":"homemaker-py-psk","title":"Experiment: island model — prime population from N independent seeds, crossover-heavy migration phase","description":"User-proposed lever (2026-06-23): the Perl Urb workflow ran the search many times and kept the best because runs settled into different local minima. The Python tool is deterministic per --seed, so the analog is: run N independent seeds (e.g. 16), then PRIME a fresh population with those N converged elites and run a second, crossover-heavy phase — an island model with synchronous migration.\n\nKEY DISTINCTION from prior negatives: this is NOT the §11.5 (c4c.5) niching/restart experiment. Those injected FRESH constructive/random seeds for raw diversity and landed null. Here the migrants are FULLY-CONVERGED elites (each spent a complete budget), so they are high-quality building blocks, not diversity filler. The §11.5 'diversity does not help' result does not directly refute this; the mechanism is different (recombination of converged basins, not exploration).\n\nHONEST PRIOR (against): this is a SEARCH-MACHINERY bet, and the leu/c4c epics are decisive that search machinery keeps landing neutral-to-negative (§11.4 graded objective, §11.5 niching+restarts, §9gp M3 reachability + shape-feasibility filter = 3 search-machinery negatives) while CONSTRUCTION/SEED quality wins (§11.6 adjacency-aware seeding, §11.7 adjacency-aware lift = 4 construction wins). The residual is diagnosed as geometry/shape-bound (size/proportion/crinkliness), not population-management-bound. So baseline expectation is neutral.\n\nWHY IT MIGHT STILL PAY: the one untested sub-mechanism is whether crossover can stack wins across independent basins (run A solved cluster X, run B solved cluster Y, child inherits both -\u003e lower total fails than either parent). That has never been tested with converged migrants.","design":"Control / baseline: 'best-of-N' — run N=16 seeds, take the single lowest-fail/highest-fitness result. This is essentially free (the N runs happen anyway) and is the legitimate descendant of Urb's multi-run habit. The experiment must BEAT best-of-N to count, on equal TOTAL budget (N short runs + migration phase vs N+ longer independent runs).\n\nPhase A: run search() for seeds 0..N-1 at a per-seed budget, collect each result.best.root (.dom).\nPhase B: prime a population from those N elites and continue evolving with high p_crossover (e.g. 0.5-0.8) to stress recombination. Reuse existing machinery — no new representation:\n  - The seed_factory / bootstrap path in driver.search already accepts a custom seed producer; a factory that cycles through the N pre-evolved roots primes the population directly (no fresh construction).\n  - Set bootstrap=True so the N elites are evaluated as the initial population, then the memetic loop runs.\n\nALIGNMENT RISK to measure, not assume: operators.crossover (operators.py:1001) is AREA-MATCHED subtree exchange — it pairs a region of A with the area-closest third of B, with no notion of programmatic/spatial role. Two independently-evolved trees encode similar arrangements with different tree structures (the encoding is not canonical — 9gp closed-negative, abandoned), so the same functional cluster sits at a different path/area/orientation per run. Area-matched splice across independent optima may therefore be disruptive rather than synthesizing, and the inner loop re-solves ratios at the splice boundary (spliced quality not preserved). Instrument: track whether any migration child ever beats max(parent fails) reduction; if crossover children are never net-positive, the null is mechanistic (alignment), not budget.\n\nBenchmarks: maple-court + harbor seeds (the §12.x A/B set), so controls reproduce documented baselines (maple 136.0, harbor 74.0). Record in DESIGN.md (new §12.x) per project convention.\n\nNOT gated on canonical encoding: 9gp is CLOSED with a negative verdict (associativity/reachability tested directly, did not pay). Do not revive the Polish rewrite as a prerequisite.","status":"open","priority":3,"issue_type":"task","owner":"bruno@postle.net","created_at":"2026-06-22T23:06:30Z","created_by":"Bruno Postle","updated_at":"2026-06-22T23:06:30Z","dependencies":[{"issue_id":"homemaker-py-psk","depends_on_id":"homemaker-py-6zy","type":"related","created_at":"2026-06-23T00:06:59Z","created_by":"Bruno Postle","metadata":"{}"},{"issue_id":"homemaker-py-psk","depends_on_id":"homemaker-py-9gp","type":"related","created_at":"2026-06-23T00:07:01Z","created_by":"Bruno Postle","metadata":"{}"},{"issue_id":"homemaker-py-psk","depends_on_id":"homemaker-py-c4c.5","type":"related","created_at":"2026-06-23T00:07:02Z","created_by":"Bruno Postle","metadata":"{}"}],"dependency_count":0,"dependent_count":0,"comment_count":0}
 {"id":"homemaker-py-6zy","title":"Experiment: topology diversity x scaled tournament pressure (joint A/B)","description":"Open lever left untested by §11.5 (homemaker-py-c4c.5): structural niching was A/B'd against the legacy fitness-scalar dedup with selection pressure HELD FIXED at a binary tournament (k=2). §11.5's own mechanism note says maximal diversity under fixed pressure just diffuses effort — i.e. diversity and pressure are coupled and were never co-tuned. This issue isolates that coupling: sweep tournament size jointly with niching to test whether sharper selection converts the extra structural diversity into lower fails, rather than diffusing it. Premise from §11.5 is a diagnosis, not a tested result; the project pivoted to the canonical encoding (homemaker-py-9gp) instead. Tracking so the lever is not silently lost.","design":"§11.5 raised structural diversity to 16/16 but held selection pressure FIXED at a\nbinary tournament (driver._tournament, k=2, driver.py:154; never overridden, no\nsearch() parameter, no env var). The §11.5 writeup names the coupling as the\nmechanism behind its own null result: \"Maximal diversity (16/16) with the fixed\ntournament pressure just diffuses effort — the fitness-scalar dedup's smaller\neffective population exploits a basin slightly harder.\" That is, diversity and\npressure were varied as if independent when they are coupled: niching widens the\npopulation, but k=2 was never sharpened to convert the extra exploration back into\nexploitation.\n\nImplementation:\n- Expose tournament size as a parameter: add `tournament_k: int = 2` to search()\n  (and search_staged()), thread it into both _tournament call sites\n  (driver.py:448 crossover pair, :452 mutation parent). Optionally an env knob\n  HOMEMAKER_TOURNAMENT_K mirroring HOMEMAKER_POP for the experiments harness.\n- Reuse the existing genome.signature / niche_by_signature machinery from c4c.5\n  unchanged — this issue adds ONLY the pressure knob and the joint A/B.\n\nA/B design (equal native-fitness budget, URB_NO_OCCLUSION=1, 20000 evals):\n- Grid: niche_by_signature ∈ {off, on} × tournament_k ∈ {2, 3, 4}.\n- The (niche=off, k=2) cell is the legacy baseline; (niche=on, k=2) reproduces\n  §11.5's \"niche\" column. New cells are the higher-pressure rows.\n- Seeds: programme-house seeds 0/1/2 (reuse §11.5 seeds for direct comparison),\n  plus harbor-house staged seed 0. NOTE the §11.5 sample (3+1 seeds) was thin and\n  its null sits within seed noise — widen to \u003e=5 programme-house seeds so a real\n  effect is distinguishable from noise this time.\n- Reuse experiments/run_search_scaled.py (NICHE env already wired) +\n  run_staged_search.py for harbor; add the k knob to both.\n- Report total fails at budget per cell (primary), plus final-pop distinct\n  signatures and distinct-seen (confirm niching still bites at higher k).\n","acceptance_criteria":"On blank-slate programme-house at equal native-fitness budget (\u003e=5 seeds), some (niche, k) cell beats the legacy (off, k=2) baseline mean fails by more than seed noise; OR the joint sweep confirms the §11.5 null is robust to selection pressure (no k recovers a win from 16/16 diversity). Either outcome recorded as a DESIGN.md §11.x subsection + bead notes, with the per-cell fails table. Negative result is an acceptable close.","notes":"Diagnosed during a session reviewing §11.5. Tournament pressure is hard-coded k=2 (driver.py:154); confirmed no override anywhere in src/ or experiments/, no env var, no prior issue. Cheap to run: niche machinery already exists (c4c.5, default-off), only the tournament_k knob is new. Lower priority because §11.5 + §11.4 both concluded the plateau is a reachability (encoding/operator) problem, so this is a loose-end falsification check rather than the expected lever.","status":"open","priority":3,"issue_type":"task","owner":"bruno@postle.net","created_at":"2026-06-22T22:52:28Z","created_by":"Bruno Postle","updated_at":"2026-06-22T22:52:28Z","dependencies":[{"issue_id":"homemaker-py-6zy","depends_on_id":"homemaker-py-9gp","type":"related","created_at":"2026-06-22T23:53:06Z","created_by":"Bruno Postle","metadata":"{}"},{"issue_id":"homemaker-py-6zy","depends_on_id":"homemaker-py-c4c.5","type":"related","created_at":"2026-06-22T23:53:04Z","created_by":"Bruno Postle","metadata":"{}"}],"dependency_count":0,"dependent_count":0,"comment_count":0}
 {"id":"homemaker-py-c3g","title":"Construction granularity / leaf-shape lever for the geometry residual","description":"HYPOTHESIS with measured motivation (DESIGN.md §12.3 residual diagnostic), unproven — must be A/B'd vs the §12.2 baseline before adoption (same discipline as §11/§12 levers). Finding: maple-court 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 NOT placement-mismatch (no good leaves to place into) and NOT density/area-bound — it is OVER-GRANULAR construction: 73 small leaves for 52 rooms =\u003e high perimeter/area + below-target sizes. Candidate levers (construction side): fewer/larger leaves, merge or share leaves across same-class rooms, coarser circulation spine, or a granularity that trades adjacency coverage for leaf shape. Cheap first experiment: vary the circulation-per-room ratio and/or a min-leaf-area floor in constructive_topology, measure shape-fail floor (operators.predicted_shape_fails) and end-to-end fails on maple+harbor. Alternative outcome to accept: 52 distinct rooms cannot be well-shaped as 52 leaves at this density (geometry floor of the slicing representation). Files: operators.constructive_topology/_grow_leaves/_assign_adjacency_aware.","notes":"MEASURED — NULL (DESIGN.md §12.4). Cheap raw probe: coarser spine lowers SHAPE floor (maple 135→110, harbor 83→66) but raises access/adj equally → raw TOTAL flat-to-worse; div=3 near the total-floor min. End-to-end A/B (20000 evals, seeds 0/1/2): maple div6 137.0 / div8 134.3 vs baseline 136.0; harbor div6 75.3 vs 74.0 — all within ±1.7, inside the ~±3 noise floor, huge per-seed spread. Coarsening the spine does NOT pay end-to-end (shape gain cancelled by access damage that is not free to repair). Kept circ_divisor=3 default. En route found nondeterminism bug xcy (±3 noise). Residual is the geometry floor of the slicing representation at this density.","status":"closed","priority":3,"issue_type":"feature","assignee":"Bruno Postle","owner":"bruno@postle.net","created_at":"2026-06-21T19:55:38Z","created_by":"Bruno Postle","updated_at":"2026-06-21T23:49:34Z","started_at":"2026-06-21T19:59:09Z","closed_at":"2026-06-21T23:49:34Z","close_reason":"Closed","dependency_count":0,"dependent_count":0,"comment_count":0}
 {"id":"homemaker-py-ld5","title":"Adjacency-aware lift_base_to_storeys + secondary adjacencies","description":"Follow-up to s44 (DESIGN.md §11.6). s44 made constructive_topology cluster rooms around a connected-dominating-set circulation spine (geometric leaf_graph), cutting harbor single-stage fails 110-\u003e90.7 mean and beating the staged §11.3 best of 95. Two gaps remain: (1) lift_base_to_storeys (staged Stage-2 upper floors) still assigns leaf types at RANDOM — port the _assign_adjacency_aware CDS approach to it so staged search benefits too. (2) Secondary adjacencies (k1\u003c-\u003eda1, da1\u003c-\u003eo, etc., ~4 harbor rooms) are not clustered — extend _assign_adjacency_aware to place rooms with non-c adjacency reqs next to their required neighbour after the c-spine is laid.","notes":"DONE positive, DESIGN.md §11.7. Adjacency-aware lift (CDS seeded from inherited core) + secondary-adjacency room placement. Staged harbor 20k evals: ADJ0 mean 99.0 (=§11.4 baseline), ADJ1 mean 85.3 (-14%, best 78). New best harbor overall. operators 22 tests pass.","status":"closed","priority":3,"issue_type":"feature","assignee":"Bruno Postle","owner":"bruno@postle.net","created_at":"2026-06-19T08:12:11Z","created_by":"Bruno Postle","updated_at":"2026-06-19T10:41:14Z","started_at":"2026-06-19T08:33:43Z","closed_at":"2026-06-19T10:41:14Z","close_reason":"Closed","dependency_count":0,"dependent_count":0,"comment_count":0}
@@ -56,18 +48,18 @@
 {"id":"homemaker-py-can","title":"Programme width defaults: t3 contradiction (impossible width_inside default)","description":"DESIGN.md §8.2, confirmed in source. t3 (3 m2 WC) has no width spec so inherits width_inside [4.0, 1.0] (Fitness/Base.pm:60) — geometrically impossible; designs 'pass' only by failing size instead. Fix AFTER faithful-port validation (port-faithfully-first policy, §8.1): a sane width default scaled to area (e.g. sqrt(area/proportion)) or per-room widths in patterns.config. Applies to native fitness; optionally upstream to Urb.","acceptance_criteria":"No programme space has a default width incompatible with its target area; corpus re-scored and effect documented","status":"closed","priority":3,"issue_type":"bug","assignee":"Bruno Postle","owner":"bruno@postle.net","created_at":"2026-06-11T23:39:01Z","created_by":"Bruno Postle","updated_at":"2026-06-13T21:21:37Z","started_at":"2026-06-13T21:16:11Z","closed_at":"2026-06-13T21:21:37Z","close_reason":"Fixed in get_space_params: when a programme space has no explicit 'width', derive target from sqrt(size/proportion) instead of falling back to width_inside [4.0, 1.0]. Re-scored 35-file corpus: 32 files improved (+1-121%), 5 files lost spurious width fails. All 109 tests pass. Upstream Perl fix tracked as homemaker-py-8fe.","dependencies":[{"issue_id":"homemaker-py-can","depends_on_id":"homemaker-py-uxz","type":"blocks","created_at":"2026-06-12T00:39:47Z","created_by":"Bruno Postle","metadata":"{}"}],"dependency_count":1,"dependent_count":0,"comment_count":0}
 {"id":"homemaker-py-yg5","title":"Penalty reshaping: replace 0.5^n while preserving inner-loop protection","description":"DESIGN.md §4.7, §5.4, §7 Phase 4, §8.5. The 0.5^n cliff gives the outer search no gradient and rewards flag-count over geometry, but it also PROTECTS the inner loop from trading into new failures (§4.5). One fitness shape cannot naively be both soft outside and cliff-protected inside. Candidates: cliff-inside-inner-loop only, lexicographic (failure count first, score second), additive/soft, multi-objective Pareto. Must preserve the missing-space failure hierarchy (worse to drop a room than to have a poor one). Measure landscape + search outcomes; this helps Urb today too.","acceptance_criteria":"Chosen scheme documented with measurements: search improves while inner loop still never trades into new failures","status":"closed","priority":3,"issue_type":"feature","assignee":"Bruno Postle","owner":"bruno@postle.net","created_at":"2026-06-11T23:39:00Z","created_by":"Bruno Postle","updated_at":"2026-06-14T08:16:14Z","started_at":"2026-06-14T07:55:32Z","closed_at":"2026-06-14T08:16:14Z","close_reason":"Implemented lexicographic outer-search comparison (-n_fails, fitness). Inner loop unchanged (0.5^n cliff protection preserved). Experiment penalty_reshape.py confirms 0/9 fail regressions in inner loop and shows lex avoids the 3-fail trap that scalar hits 1/3 of the time. Fixed stale _CHILD_INNER_KW sigmas entry.","dependencies":[{"issue_id":"homemaker-py-yg5","depends_on_id":"homemaker-py-uxz","type":"blocks","created_at":"2026-06-12T00:39:46Z","created_by":"Bruno Postle","metadata":"{}"}],"dependency_count":1,"dependent_count":0,"comment_count":0}
 {"id":"homemaker-py-2g5","title":"Rebuild occlusion/daylight/sun subsystem in Python (post-Phase-5, after optimisation fully native)","description":"DESIGN.md §6 port scope — a whole subsystem, not a term. quality_daylight (Leaf.pm:281-296) needs Urb::Misc::Sun + Urb::Field::Occlusion (+CIESky); quality_uncrinkliness also takes the occlusion object. Indoor spaces return 1 for daylight; cost is outdoor spaces + crinkliness. Port Sun_horizontal (262980-minute normalisation) and the occlusion wall set from Dom-\u003eWalls.","acceptance_criteria":"Daylight and crinkliness factors match Perl (float tolerance) across the corpus, including multi-storey cases","notes":"Re-scoped 2026-06-12: occlusion disabled in the Urb oracle instead of ported (see homemaker-py-gp2). Native fitness ships with simple crinkliness (illumination factor = 1, in homemaker-py-gnw). This issue is now the eventual Python occlusion rebuild, only after optimisation works entirely in Python. Restores outdoor-daylight and shaded-wall selection pressure.\nReframed 2026-06-17: orthogonal to epic homemaker-py-c4c. This is fitness FIDELITY (restoring daylight + shaded-wall selection pressure to match Perl), not search CAPABILITY — it changes what 'good' means, not the search's ability to find good. It will NOT improve final designs in the sense currently sought. Stays P4, deferred until the topology-search-quality epic lands and optimisation is fully native.","status":"open","priority":4,"issue_type":"feature","owner":"bruno@postle.net","created_at":"2026-06-11T23:38:25Z","created_by":"Bruno Postle","updated_at":"2026-06-17T19:14:48Z","dependency_count":0,"dependent_count":0,"comment_count":0}
-{"_type":"memory","key":"never-use-corpus-filenames-candidate-001-dom-candidate","value":"Never use corpus filenames (candidate-001.dom, candidate-002.dom, generated.dom, init.dom, etc.) as --output targets when running experiments. These are test fixtures. Always write experimental outputs to scratch/ or a timestamped path. Lesson from 2026-06-14: warm-start runs overwrote candidate-001/002.dom and broke graph tests."}
+{"_type":"memory","key":"multi-storey-staircase-consistency-when-dividing-or-retyping","value":"Multi-storey staircase consistency: when dividing or retyping a circulation (C) leaf at one level, the same structural change should be propagated to the matching leaf on ALL other storeys so the stair core path is maintained. The optimizer cannot fix staircase disruptions through trial-and-error geometry alone — it requires a synchronized multi-level operator that applies the same topology change to every storey simultaneously."}
 {"_type":"memory","key":"proportion-aware-constructive-seeding-leu-2-12-2","value":"Proportion-aware constructive seeding (leu.2/§12.2): sizing seed cuts from target AREAS only regresses (thin slivers wreck aspect); you must ALSO pick each cut's rotation for child squareness. It is a convergence ACCELERATOR via a deeper local optimum around the constructed topology: wins where that topology is roughly right and budget is scarce (harbor -13%, maple -10% at 20k evals) but DELAYS small programmes where the seed must be restructured by undivide (programme-house regresses at fixed budget, yet reaches the floor given budget - speed, not asymptote). Default-on. Also: n_storeys must honour storey_minimum, not just level: keys (programme-house storey_minimum:2, all rooms level:0 - was seeded 1 storey short; cq1)."}
 {"_type":"memory","key":"strategy-decision-2026-06-12-bruno-occlusion-daylight","value":"Strategy decision 2026-06-12 (Bruno): occlusion/daylight is ORTHOGONAL to building a scalable optimiser. Disable it in Urb (env flag, homemaker-py-gp2) rather than port it; native fitness uses simple crinkliness (illumination factor = 1); rebuild occlusion in Python only after optimisation is fully native (homemaker-py-2g5, now P4). Consequence: all scores change when the flag flips — re-baseline corpus/.score, DESIGN \\$4.5 gains, gate bars at one clean boundary AFTER homemaker-py-1p0 closes; Phase-2 urb-evolve benchmark must run with the same flag."}
 {"_type":"memory","key":"urb-fitness-bug-found-fixed-2026-06-12","value":"Urb fitness bug found+fixed 2026-06-12 (patch in /home/bruno/src/urb, uncommitted): ProgrammeDriven.pm ratio_o/ratio_type grepped case-insensitively over the ratios hash and took the FIRST key — nondeterministic (x4.5 score swings) for designs with mixed-case type classes (both 'c' circulation and 'C' covered). Fixed to SUM the class (matches Is_Circulation//Is_Outside semantics); 35/35 corpus scores unchanged. CRITICAL for homemaker-py-3y7/gnw: the native port must implement class-SUM ratios. Building.pm has the same unpatched pattern (site-driven path, not used by our oracle). Also: the memetic search reward-hacked this bug before the fix — search results predating it are noise artifacts."}
-{"_type":"memory","key":"adjacency-in-binary-slicing-tree-is-structural-not","value":"Adjacency in binary slicing tree is structural, not geometric: the inner-loop NM cannot fix topological adjacency failures. Two paths exist: (1) tree-sibling adjacency — a node is adjacent to its sibling in the tree; (2) cross-zone geometric adjacency — leaves from different subtrees that happen to share a boundary. Staircase/adjacency fails require a topology mutation that changes which nodes are siblings or which zones touch. This was proved empirically on programme-house: staircase fail from rot=0 layout could not be fixed by NM but was fixed by level_retype creating a two-C topology (2026-06-14/15)."}
-{"_type":"memory","key":"correction-to-urb-fitness-bug-memory-bruno-2026","value":"CORRECTION to urb-fitness-bug memory (Bruno, 2026-06-12): 'C' is NOT a 'covered' type — Is_Covered is a geometric predicate (indoor space above). Urb's generic types are canonically UPPERCASE: C=circulation, O=outside, S=sahn (get_space_types qw/C O S/; corpus is 100% uppercase, never 'c'/'o' leaves). The mixed-case designs that fired the latent ratio_type first-match bug were created by homemaker's own operator type pool emitting lowercase 'c'/'o' — fixed: driver/operators now emit uppercase generics only, and class checks use t[0].lower() in 'cos'. The Urb class-sum patch stays as defensive hardening (zero impact on canonical designs). Native port (3y7/gnw): treat type classes case-insensitively, generics canonically uppercase."}
-{"_type":"memory","key":"homemaker-py-pythonpath-set-pythonpath-home-bruno-src","value":"homemaker-layout PYTHONPATH: package installed as 'homemaker-layout' via pip install -e . so 'import homemaker_layout' works from anywhere without PYTHONPATH. For running tests use 'python -m pytest' from project root /home/bruno/src/homemaker-layout (pyproject.toml adds src/ automatically). Never try pip show homemaker — that's the old homemaker-addon conflict."}
-{"_type":"memory","key":"multi-storey-staircase-consistency-when-dividing-or-retyping","value":"Multi-storey staircase consistency: when dividing or retyping a circulation (C) leaf at one level, the same structural change should be propagated to the matching leaf on ALL other storeys so the stair core path is maintained. The optimizer cannot fix staircase disruptions through trial-and-error geometry alone — it requires a synchronized multi-level operator that applies the same topology change to every storey simultaneously."}
 {"_type":"memory","key":"urb-oracle-nondeterminism-urb-fitness-pl-output-varies","value":"Urb oracle nondeterminism: urb-fitness.pl output varies run-to-run from Perl hash-order randomisation — .fails line ORDER shuffles (compare sorted, use oracle.Score.fail_lines) and the score float can flip by ~1 ULP (compare with math.isclose rel_tol=1e-12, never ==). Not a batching artifact; affects single runs too. Matters for the Phase 3 native-fitness parity gate (homemaker-py-uxz)."}
-{"_type":"memory","key":"cli-tool-style-prefer-python-m-homemaker-module","value":"CLI tool style: prefer python -m homemaker.module --parameters pattern, installable via pip install -e . with pyproject.toml entry_points. Not standalone bin/ scripts."}
+{"_type":"memory","key":"correction-to-urb-fitness-bug-memory-bruno-2026","value":"CORRECTION to urb-fitness-bug memory (Bruno, 2026-06-12): 'C' is NOT a 'covered' type — Is_Covered is a geometric predicate (indoor space above). Urb's generic types are canonically UPPERCASE: C=circulation, O=outside, S=sahn (get_space_types qw/C O S/; corpus is 100% uppercase, never 'c'/'o' leaves). The mixed-case designs that fired the latent ratio_type first-match bug were created by homemaker's own operator type pool emitting lowercase 'c'/'o' — fixed: driver/operators now emit uppercase generics only, and class checks use t[0].lower() in 'cos'. The Urb class-sum patch stays as defensive hardening (zero impact on canonical designs). Native port (3y7/gnw): treat type classes case-insensitively, generics canonically uppercase."}
-{"_type":"memory","key":"deceptive-valleys-in-topology-search-when-every-single","value":"Deceptive valleys in topology search: when every single-step mutation from a target state passes through a high-fail intermediary (e.g. level_fix displaces a room into 5+ new fails), a compound operator that atomically applies two coordinated changes can escape. Design compound operators to land on the low-fail state directly, bypassing the deceptive gradient. Programme-house example: level_compound_fix atomically moves the level-constrained room AND re-inserts the displaced room adjacent to C in one step (operators.py, 2026-06-14)."}
 {"_type":"memory","key":"programme-house-optimisation-result-2026-06-14-15","value":"Programme-house optimisation result (2026-06-14/15): best achievable is 1 fail (l1 wrong level, score ~0.005). 0 fails is geometrically impossible: l1 (min 27m²) must occupy ll (~23m²) at level 0, which eliminates the t3-adj-C provider; dividing ll into lll(l1)+llr(C) gives llr proportion ~6:1 (fails). Python memetic optimizer achieves 1 fail in 50k evals vs Perl optimiser's 2-3 fails. Winning topology: TWO C nodes at level 0 — ll(C) for t3-adj-C via geometric contact, rl(C) for staircase via tree-sibling adjacency to rrr(O). Best .dom: scratch/from-warmstart-fixed.dom and scratch/from-compound3-fixed.dom."}
-{"_type":"memory","key":"run-to-run-reproducibility-in-homemaker-layout-serial","value":"Run-to-run reproducibility in homemaker-layout: serial search (workers=1) is byte-for-byte deterministic; parallel (workers\u003e1) is now deterministic too AFTER fixing driver._run_batch to admit futures in submission order (was as_completed/completion order, bug xcy). Reproducibility holds only for a FIXED worker count — serial vs parallel differ because children-per-iteration is 1 vs n_workers (different batch granularity), which is expected, not a bug. The constructive seeder was NEVER nondeterministic: _assign_adjacency_aware has unique idx tiebreaks; comparing topologies with Python builtin hash() of the signature STRING is invalid (PYTHONHASHSEED salts str hashing per process) — use a stable hash (sha1) or genome.signature equality."}
-{"_type":"memory","key":"user-preference-bruno-this-is-a-fedora-system","value":"User preference (Bruno): this is a Fedora system — NEVER install Python packages via pip without asking first; always ask whether to install the rpm via dnf (e.g. python3-cma) before considering pip. Applies to any dependency additions."}
 {"_type":"memory","key":"warm-x0-initialization-bug-pattern-when-a-topology","value":"warm_x0 initialization bug pattern: when a topology operator explicitly sets division ratios on a newly-created node (e.g. compound_fix sets node.division=[0.25,0.25] for t3), parent.ratios has no entry for that node (it was a leaf). warm_x0 defaults it to 0.5, corrupting the inner loop's starting point and making the operator invisible to lex comparison. Fix: only propagate child ratios for nodes where the parent node was NOT already divided; stale hidden nodes revealed by structural mutations (swap flipping b.below) must NOT contribute their pre-writeback values. See driver.py lines 259-267 (fixed 2026-06-14)."}
+{"_type":"memory","key":"cli-tool-style-prefer-python-m-homemaker-module","value":"CLI tool style: prefer python -m homemaker.module --parameters pattern, installable via pip install -e . with pyproject.toml entry_points. Not standalone bin/ scripts."}
+{"_type":"memory","key":"homemaker-py-pythonpath-set-pythonpath-home-bruno-src","value":"homemaker-layout PYTHONPATH: package installed as 'homemaker-layout' via pip install -e . so 'import homemaker_layout' works from anywhere without PYTHONPATH. For running tests use 'python -m pytest' from project root /home/bruno/src/homemaker-layout (pyproject.toml adds src/ automatically). Never try pip show homemaker — that's the old homemaker-addon conflict."}
+{"_type":"memory","key":"user-preference-bruno-this-is-a-fedora-system","value":"User preference (Bruno): this is a Fedora system — NEVER install Python packages via pip without asking first; always ask whether to install the rpm via dnf (e.g. python3-cma) before considering pip. Applies to any dependency additions."}
+{"_type":"memory","key":"deceptive-valleys-in-topology-search-when-every-single","value":"Deceptive valleys in topology search: when every single-step mutation from a target state passes through a high-fail intermediary (e.g. level_fix displaces a room into 5+ new fails), a compound operator that atomically applies two coordinated changes can escape. Design compound operators to land on the low-fail state directly, bypassing the deceptive gradient. Programme-house example: level_compound_fix atomically moves the level-constrained room AND re-inserts the displaced room adjacent to C in one step (operators.py, 2026-06-14)."}
+{"_type":"memory","key":"adjacency-in-binary-slicing-tree-is-structural-not","value":"Adjacency in binary slicing tree is structural, not geometric: the inner-loop NM cannot fix topological adjacency failures. Two paths exist: (1) tree-sibling adjacency — a node is adjacent to its sibling in the tree; (2) cross-zone geometric adjacency — leaves from different subtrees that happen to share a boundary. Staircase/adjacency fails require a topology mutation that changes which nodes are siblings or which zones touch. This was proved empirically on programme-house: staircase fail from rot=0 layout could not be fixed by NM but was fixed by level_retype creating a two-C topology (2026-06-14/15)."}
+{"_type":"memory","key":"never-use-corpus-filenames-candidate-001-dom-candidate","value":"Never use corpus filenames (candidate-001.dom, candidate-002.dom, generated.dom, init.dom, etc.) as --output targets when running experiments. These are test fixtures. Always write experimental outputs to scratch/ or a timestamped path. Lesson from 2026-06-14: warm-start runs overwrote candidate-001/002.dom and broke graph tests."}
+{"_type":"memory","key":"run-to-run-reproducibility-in-homemaker-layout-serial","value":"Run-to-run reproducibility in homemaker-layout: serial search (workers=1) is byte-for-byte deterministic; parallel (workers\u003e1) is now deterministic too AFTER fixing driver._run_batch to admit futures in submission order (was as_completed/completion order, bug xcy). Reproducibility holds only for a FIXED worker count — serial vs parallel differ because children-per-iteration is 1 vs n_workers (different batch granularity), which is expected, not a bug. The constructive seeder was NEVER nondeterministic: _assign_adjacency_aware has unique idx tiebreaks; comparing topologies with Python builtin hash() of the signature STRING is invalid (PYTHONHASHSEED salts str hashing per process) — use a stable hash (sha1) or genome.signature equality."}