bruno/homemaker-layout
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

bd: sync issues.jsonl export after 8oq/57z creation

Browse source 
Created homemaker-py-8oq (review the 2g7.7 LLM-repair plan with a more
capable model) and homemaker-py-57z (live acceptance-benchmark follow-up,
blocked on ANTHROPIC_API_KEY availability), linked as blockers/dependents
of homemaker-py-2g7.7 per the planning session on 2026-08-05.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_014uCyZJCh5mBuA2yEFxgQpo
This commit is contained in:
Bruno Postle 2026-08-06 08:08:07 +01:00
parent 91f7626a77
commit 25e0503ddc
1 changed files with 19 additions and 17 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

36
.beads/issues.jsonl
View file

@ -27,11 +27,13 @@
{"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-57z","title":"Run homemaker-py-2g7.7 acceptance benchmark against evolved-3M-nols-3 plateau seed (needs live ANTHROPIC_API_KEY)","description":"homemaker-py-2g7.7's acceptance criteria requires an end-to-end run of the LLM repair loop against the evolved-3M-nols-3 15-hard-fail plateau seed (level 0 not connected + me1 on wrong level, which survived \u003e1M blind evals / ~2.4 days in the original evolve-3M-nols-3.log run): repair loop reduces hard-fail count within \u003c=20 LLM calls, edit-DSL rejects malformed proposals (should already be covered by unit tests), and an A/B at equal native-eval budget shows strictly better final fails on \u003e=2/3 seeds vs a no-repair control. This requires a live Claude API call (client.messages.create against claude-opus-5) which needs ANTHROPIC_API_KEY set or 'ant auth login' completed -- neither was available in the sandbox that scaffolded the feature (no ANTHROPIC_API_KEY env var, no ant CLI installed). Run this once credentials are available, then close out homemaker-py-2g7.7's acceptance criteria referencing the results.","status":"open","priority":2,"issue_type":"task","owner":"bruno@postle.net","created_at":"2026-08-06T07:05:45Z","created_by":"Bruno Postle","updated_at":"2026-08-06T07:05:45Z","dependencies":[{"issue_id":"homemaker-py-57z","depends_on_id":"homemaker-py-2g7.7","type":"blocks","created_at":"2026-08-06T08:06:12Z","created_by":"Bruno Postle","metadata":"{}"}],"dependency_count":1,"dependent_count":0,"comment_count":0}
{"id":"homemaker-py-8oq","title":"Review homemaker-py-2g7.7 LLM-repair-operator plan with a more expensive/capable model","description":"A detailed implementation plan for the LLM repair operator (homemaker-py-2g7.7) was drafted in a planning session on 2026-08-05: new src/homemaker_layout/llm_repair.py module (edit DSL over swap/divide/retype/rotate/undivide with explicit (level,path) targeting, apply_script/validate_script interpreter, prompt builder serializing dom+fails+programme summary, propose_repairs() with an injectable call_fn and a (genome.signature, sorted fails) cache), driver.py stagnation-trigger wiring modeled on the existing restart_patience/last_improve clock, evolve.py CLI flags (--llm-repair, --llm-repair-patience, --llm-repair-max-calls, --llm-repair-model default claude-opus-5), an anthropic SDK dependency addition, and a DESIGN.md writeup. Plan text is saved at /home/bruno/.claude/plans/glowing-snuggling-flute.md from that session. Before implementing, the user wants a second, more capable/expensive model to review this plan for soundness (DSL completeness vs the 19 existing mutate_* primitives, correctness of the path-addressing scheme, the stagnation-trigger placement in driver.py's search() loop, cache-key choice, and whether the phasing that defers the live acceptance-criteria benchmark to a separate follow-up issue is the right call) before a new implementation session begins.","status":"open","priority":2,"issue_type":"task","owner":"bruno@postle.net","created_at":"2026-08-06T07:04:54Z","created_by":"Bruno Postle","updated_at":"2026-08-06T07:04:54Z","dependency_count":0,"dependent_count":1,"comment_count":0}
{"id":"homemaker-py-koo","title":"Multi-storey (below-link) support for the shape-curve DP","description":"homemaker-py-6xh item 3 (DESIGN.md §37.2/§37.4). src/homemaker_layout/shapecurve.py's solve()/realise() writes division on every divided node under a single level_root unconditionally -- it has no notion of upper-storey below-inherited (wall-stacked) fixed splits (see solver.free_branches: a branch is free only when b.below is None or not b.below.divided). shapecurve.eligible() currently guards this by requiring len(dom.levels(root)) == 1, so the DP warm-start (homemaker-py-6xh) never fires on multi-storey topologies -- which is most real programmes (e.g. examples/programme-house has storey_minimum=2, examples/harbor-house is multi-storey; only the purpose-built examples/harbor-house-l0 de-risk variant is single-storey). Needs: generalise the DP to run bottom-up per storey, treating below-inherited-and-divided branches as FIXED (their (w,h) contribution comes from the level below's already-realised geometry, not chosen by this level's DP) while still composing correctly through them to size the level's own free branches. Validate against the full (multi-storey) examples/harbor-house, the DP's original but not-yet-attempted target.","notes":"DONE, PASS. Generalised shapecurve.py's DP to handle below-inherited (wall-stacked) multi-storey trees: dom.levels(root) processed bottom-up per storey; a divided node's split is free only per solver.free_branches' own criterion (below is None or undivided there), since geometry.coordinate always mirrors a below-linked node's corners from the storey below regardless of whether that storey's counterpart is divided. New _region_roots walks each storey descending through below.divided spines (nothing to solve there) to find below-fixed leaves (checked directly via the new _leaf_feasible, gridless/exact) and below-fixed-box/free-split fringe nodes -- each solved with the EXACT pre-existing single-region _check/realise, unmodified. _solve_all_levels realises each storey before checking the one above (fixed boxes read off already-realised geometry) and snapshots+restores on any infeasibility, preserving solve()'s all-or-nothing and is_feasible()'s never-writes contracts across the whole multi-storey tree. eligible() now allows any storey count (only leaf_sharing/superpose/max_share/multi_use -- tym's scope -- remain excluded). Validated: experiments/validate_shapecurve_multistorey.py, 200 random 2-storey topologies on the REAL examples/harbor-house (not the l0 de-risk variant), same DP-vs-NM protocol as 2g7.4/wkh -- 99.5% agreement, 0 false negatives, 117.7x speedup (DESIGN.md §37.6). Manual smoke: driver.search with shapecurve_warmstart=True and shapecurve_prune=True both run to completion on examples/harbor-house/init.dom. 4 new/1 renamed tests in test_shapecurve.py, 1 renamed+inverted in test_driver.py. Full suite 397 passed. Follow-up filed: homemaker-py-v4s (driver.search A/B on real multi-storey, blocked on tym).","status":"closed","priority":2,"issue_type":"feature","assignee":"Bruno Postle","owner":"bruno@postle.net","created_at":"2026-08-03T17:29:43Z","created_by":"Bruno Postle","updated_at":"2026-08-03T22:25:26Z","started_at":"2026-08-03T20:40:51Z","closed_at":"2026-08-03T22:25:26Z","close_reason":"Multi-storey DP generalisation validated PASS on real harbor-house (99.5% agreement, 0 false negatives, 117.7x speedup); see notes and DESIGN.md §37.6","dependencies":[{"issue_id":"homemaker-py-koo","depends_on_id":"homemaker-py-2g7","type":"parent-child","created_at":"2026-08-03T18:31:48Z","created_by":"Bruno Postle","metadata":"{}"}],"dependency_count":0,"dependent_count":0,"comment_count":0}
{"id":"homemaker-py-wkh","title":"DP-exact hard pre-filter: replace/augment predicted_shape_fails with shapecurve's boolean infeasibility","description":"homemaker-py-6xh item 1 (DESIGN.md §37.2/§37.4). The shapecurve DP (src/homemaker_layout/shapecurve.py, promoted from experiments/shapecurve_spike.py) gives an EXACT feasible/infeasible verdict for the size/width/proportion family, currently wired only as an NM warm-start (safe: never prunes). operators.predicted_shape_fails' threshold-based prune (driver._evaluate, feasibility_max_shape_fails/best_n_fails) still uses the older heuristic-count proxy. Using shapecurve.solve's infeasible verdict as an ADDITIONAL/replacement hard-prune signal would be stronger (exact, not a graduated heuristic) but riskier: unlike a bad warm-start, a wrong prune permanently discards a topology that could have beaten the incumbent. DESIGN.md §37.2 measured 0/200 false negatives (DP infeasible, NM reaches 0 anyway) on harbor-house-l0, but that is not a proven bound (the rectangle-vs-skew-quad approximation is a known ~7-12% error source, §37.2). Needs: (a) a design for how the DP's boolean signal composes with the existing pred\u003ethreshold\u0026\u0026pred\u003e=best_n_fails guard, (b) a false-negative-risk validation before enabling by default (a larger/less-rectangular topology sweep than the 200-topology harbor-house-l0 one), (c) a driver.search A/B (evals-to-N-hard-fails) against today's predicted_shape_fails-only filter.","notes":"2026-08-03: Shipped the DP-exact hard pre-filter, DESIGN.md §37.5. Full\ndetails there; summary:\n\n- shapecurve.is_feasible() (new, non-mutating refactor of solve()'s check\n phase) + shapecurve_prune flag in driver._evaluate/search, threaded\n through to `homemaker-evolve --shapecurve-prune` (default off, mirrors\n --shapecurve-warmstart). Composition: DP-feasible vetoes a heuristic\n prune outright (skips predicted_shape_fails entirely); DP-infeasible only\n hard-prunes when the incumbent already has 0 total fails (exact, since\n infeasible proves the shape-fail floor \u003e=1); otherwise defers unchanged\n to the existing predicted_shape_fails threshold. Conservative by design\n per the bead's own risk framing (a wrong prune is unrecoverable, unlike a\n bad warm-start).\n- Validation (bead item b): pointed experiments/validate_shapecurve.py at\n the promoted product module (was still validating the frozen spike) and\n gave it a programme_dir CLI arg; ran the same 200-topology protocol\n against examples/programme-house (a genuinely skewed, non-axis-aligned\n plot, not just a rotated harbor-house-l0): 200/200 agreement, 0 false\n positives, 0 false negatives, 87.4x speedup. Combined with §37.2's\n original 200 on harbor-house-l0: 0/400 false negatives across two\n structurally distinct plots.\n- A/B (bead item c): experiments/ab_shapecurve_prune.py, same protocol as\n 6xh's warm-start A/B (harbor-house-l0, budget=2000, seeds 0-4). Result:\n byte-identical off/on across all 5 seeds -- NULL, not a regression.\n Instrumented root cause: on this benchmark predicted_shape_fails itself\n (pre-existing 9gp.1, not this bead's code) rarely reaches best_n_fails\n organically -- tests/test_driver.py's own test_feasibility_filter_\n prunes_cheaply already had to force it to 999 to observe any real prune\n -- so neither the veto nor the exact-prune branch had an opening to fire\n (spied: 17/17 DP checks infeasible, incumbent total fails never reached\n 0). Not a wkh defect; 9gp.1 is documented as a \"scaling lever\", expected\n to matter at larger programmes/leaf counts than this benchmark, not here.\n\nTests: tests/test_shapecurve.py (+1), tests/test_driver.py (+4). Full\nsuite: 393 passed.\n\nFollow-up (not blocking this close, noted in DESIGN.md §37.5): re-run the\nA/B at a scale where predicted_shape_fails organically prunes to see wkh's\nmarginal value -- the more direct route there is homemaker-py-koo\n(multi-storey) and homemaker-py-tym (leaf-sharing), since today's DP\neligibility already excludes the real \u003e=2-storey, leaf-sharing-default\nprogrammes (programme-house, harbor-house) this would need to be measured\non.","status":"closed","priority":2,"issue_type":"feature","assignee":"Bruno Postle","owner":"bruno@postle.net","created_at":"2026-08-03T17:29:13Z","created_by":"Bruno Postle","updated_at":"2026-08-03T20:08:37Z","started_at":"2026-08-03T18:16:35Z","closed_at":"2026-08-03T20:08:37Z","close_reason":"DP-exact hard prune shipped + validated (0/400 false negatives across 2 plots); A/B measured NULL on harbor-house-l0 for a root-caused, pre-existing reason (9gp.1 rarely engages organically at this scale)","dependencies":[{"issue_id":"homemaker-py-wkh","depends_on_id":"homemaker-py-2g7","type":"parent-child","created_at":"2026-08-03T18:31:46Z","created_by":"Bruno Postle","metadata":"{}"}],"dependency_count":0,"dependent_count":0,"comment_count":0}
{"id":"homemaker-py-6xh","title":"Wire shapecurve DP prototype into driver.py as a real pre-filter + NM warm-start","description":"homemaker-py-2g7.4's prototype (experiments/shapecurve_spike.py, DESIGN.md\n§37.2) validated PASS on harbor-house-l0 (99% agreement vs shape-fail-only NM\nover 200 random topologies, 93.6x speedup at grid_n=150, 0 false negatives).\nIt is not yet wired into the product — it's a reference spike only, same\nstatus as experiments/autodiff_spike.py (§34).\n\nTo productionise per the original plan (DESIGN.md §37 point 2):\n- Replace/augment operators.predicted_shape_fails with the DP as driver.py's\n real per-child pre-filter (a single-sample heuristic today; the DP gives an\n exact yes/no plus a realizing ratio point).\n- Warm-start innerloop.optimise's NM from the DP's realised ratios instead of\n (or in addition to) the current proportion-aware target-geometry seed.\n- Multi-storey support: the DP only walks one level's leaves currently;\n below-linked nodes (wall-stacking across storeys) aren't modelled.\n- leaf_sharing/co_type target-adjustment: not modelled in leaf_constraints,\n needed for any programme that uses either (harbor-house-l0 doesn't).\n- Consider replacing the bounding-box leaf approximation with true skew-quad\n polygon algebra to remove the ~7-12% area approximation error identified\n as the root cause of both measured false positives (§37.2) -- or at least\n characterise it on a LESS rectangular plot than harbor-house-l0's\n near-rectangular trapezoid, where the error is likely worse.\n- A/B against the real driver.search: does DP-pre-filter + warm-start beat\n today's predicted_shape_fails + cold/proportion-aware start on wall-clock\n to N hard fails, on harbor-house (full) and/or a less-rectangular plot?","notes":"2026-08-03: Shipped NM warm-start (item 2) + a scoped A/B (item 5), left\nin_progress -- 3 of 5 description items deliberately deferred to new\ntracked beads (see below). Full details + measured numbers: DESIGN.md\n§37.4.\n\nWhat shipped: promoted experiments/shapecurve_spike.py into\nsrc/homemaker_layout/shapecurve.py (fixed a latent numpy.float64-in-division\nbug caught by round-tripping through dom.dumps in the new tests -- the spike\nnever round-tripped and so never caught it). Added shapecurve.eligible()\n(single storey, no leaf_sharing/superpose/max_share/multi_use). Wired into\ndriver._evaluate as an NM warm-start only (never a prune) behind\nshapecurve_warmstart=False default, threaded through driver.search and\nexposed as `homemaker-evolve --shapecurve-warmstart`. A/B\n(experiments/ab_shapecurve_warmstart.py) on harbor-house-l0, budget=2000,\n5 seeds: mean total-fails 16.6 (on) vs 19.6 (off), ~3.5x mean fitness\nimprovement; mean hard-fail count alone was a noise-level wash (4.6 vs 4.4\nat n=5). Tests: tests/test_shapecurve.py (4), tests/test_driver.py (+3).\nFull suite 388 passed.\n\nDeferred to new tracked beads (children of 2g7, per the epic's own\ndependency ordering):\n- homemaker-py-wkh: DP-exact hard pre-filter (item 1) -- replacing\n predicted_shape_fails' heuristic threshold with the DP's exact\n infeasibility verdict. Needed to actually chase \"evals to N hard fails\"\n rather than just improve soft-fail/fitness convergence.\n- homemaker-py-koo: multi-storey (below-link) DP support (item 3) --\n without this, the warm-start never fires on programme-house\n (storey_minimum=2) or full harbor-house, only the purpose-built\n single-storey harbor-house-l0.\n- homemaker-py-tym: leaf_sharing/co_type modelling (item 4) -- without this,\n the warm-start never fires when leaf_sharing=True, which is\n driver.search's own default.\n- homemaker-py-ekc: true skew-quad polygon algebra (the §37.2-quantified\n ~7-12% rectangle-approximation error) -- not a new bead-description item,\n but the explicit \"consider replacing the bounding-box leaf approximation\"\n bullet.\n\nNet: 6xh's own acceptance (a real evals-to-N-hard-fails win over\npredicted_shape_fails + cold/proportion-aware start) is NOT yet met --\ntoday's result is a safe, positive-but-partial step (soft-fail/fitness\nconvergence, not hard-fail convergence, and only on the single-storey\nno-sharing envelope). Keeping 6xh in_progress rather than closing it.","status":"in_progress","priority":2,"issue_type":"feature","assignee":"Bruno Postle","owner":"bruno@postle.net","created_at":"2026-08-02T22:39:46Z","created_by":"Bruno Postle","updated_at":"2026-08-03T17:40:52Z","started_at":"2026-08-03T15:51:29Z","dependency_count":0,"dependent_count":0,"comment_count":0}
{"id":"homemaker-py-2g7.9","title":"Parallel best-of-N + racing harness (use all cores, kill stragglers early)","description":"§14 measured islands \u003c= best-of-N, and the 3M runs used workers=1-2 on a 4-core box — independent seeds are the proven shape and we are not even using the local machine. Build a harness: launch N independent search_staged seeds across all cores (processes, not threads — mind the cvw id()-keyed cache bug), checkpoint fail-counts periodically, successively halve (hyperband-style: kill runs above median hard-fail count at each rung, reallocate budget to survivors). Fix/respect homemaker-py-b8g (parallel non-determinism) and homemaker-py-cvw first or work around with process isolation. This multiplies whatever eval cost the shape-curve DP issue achieves; on its own it is a free 4x locally and scales to any box. Report best + variance across seeds (the seed-variance in §12-§13 tables is huge — 78 vs 97 same config — so best-of-N is worth several levers combined).","acceptance_criteria":"harness runs N=16 seeds on 4 cores with racing; at equal total native-eval budget beats the single-seed mean on harbor by at least the observed seed spread; deterministic per-seed replay","status":"open","priority":2,"issue_type":"task","owner":"bruno@postle.net","created_at":"2026-08-02T09:15:58Z","created_by":"Bruno Postle","updated_at":"2026-08-02T09:15:58Z","dependencies":[{"issue_id":"homemaker-py-2g7.9","depends_on_id":"homemaker-py-2g7","type":"parent-child","created_at":"2026-08-02T10:15:58Z","created_by":"Bruno Postle","metadata":"{}"},{"issue_id":"homemaker-py-2g7.9","depends_on_id":"homemaker-py-b8g","type":"blocks","created_at":"2026-08-02T10:16:16Z","created_by":"Bruno Postle","metadata":"{}"},{"issue_id":"homemaker-py-2g7.9","depends_on_id":"homemaker-py-cvw","type":"blocks","created_at":"2026-08-02T10:16:15Z","created_by":"Bruno Postle","metadata":"{}"}],"dependency_count":2,"dependent_count":0,"comment_count":0}
{"id":"homemaker-py-2g7.7","title":"LLM repair operator at stagnation (dom+fails -\u003e targeted compound edits)","description":"Generalize the §4.10 lesson: deceptive valleys are crossed by COMPOUND edits (move room + re-home displaced room + fix ratios atomically), which we currently hand-code one per valley (mutate_level_compound_fix). Our fail messages are semantically rich and localized ('me1 on wrong level', 'level 1 not connected', '0/rlrlr proportion') and the .dom is readable — ideal LLM input. Loop: on stagnation (no fail-tier improvement for N evals), serialize best individual + .fails + programme summary -\u003e LLM proposes 3-5 multi-step repairs as structured edit scripts (a small DSL over existing operator primitives: swap/divide/retype/rotate with explicit paths — NOT freeform dom text, so proposals are always well-formed) -\u003e apply, inner-loop, lex-accept as usual. Native fitness disposes; a bad proposal costs one child budget. Cost discipline: one LLM call ~ thousands of native evals, so plateau-only, cache by (signature, fails) key. Benchmark: the 3M-run best sat on 'level 0 not connected' + 'me1 on wrong level' for \u003e1M evals — moves a plan-reader fixes in one edit. Use claude via API (see claude-api skill); temperature\u003e0 for diverse proposals. Later extension (separate issue): AlphaEvolve-style operator-code synthesis using our existing A/B harness as the evaluator.","acceptance_criteria":"on the evolved-3M-nols-3 15-fail plateau seed: repair loop reduces hard-fail count where 1M+ blind evals did not, within \u003c=20 LLM calls; edit-DSL rejects malformed proposals; A/B at equal native-eval budget shows strictly better final fails on \u003e=2/3 seeds","status":"open","priority":2,"issue_type":"feature","assignee":"Bruno Postle","owner":"bruno@postle.net","created_at":"2026-08-02T09:15:54Z","created_by":"Bruno Postle","updated_at":"2026-08-04T23:40:49Z","started_at":"2026-08-04T23:31:21Z","dependencies":[{"issue_id":"homemaker-py-2g7.7","depends_on_id":"homemaker-py-2g7","type":"parent-child","created_at":"2026-08-02T10:15:53Z","created_by":"Bruno Postle","metadata":"{}"}],"dependency_count":0,"dependent_count":1,"comment_count":0}
{"id":"homemaker-py-2g7.7","title":"LLM repair operator at stagnation (dom+fails -\u003e targeted compound edits)","description":"Generalize the §4.10 lesson: deceptive valleys are crossed by COMPOUND edits (move room + re-home displaced room + fix ratios atomically), which we currently hand-code one per valley (mutate_level_compound_fix). Our fail messages are semantically rich and localized ('me1 on wrong level', 'level 1 not connected', '0/rlrlr proportion') and the .dom is readable — ideal LLM input. Loop: on stagnation (no fail-tier improvement for N evals), serialize best individual + .fails + programme summary -\u003e LLM proposes 3-5 multi-step repairs as structured edit scripts (a small DSL over existing operator primitives: swap/divide/retype/rotate with explicit paths — NOT freeform dom text, so proposals are always well-formed) -\u003e apply, inner-loop, lex-accept as usual. Native fitness disposes; a bad proposal costs one child budget. Cost discipline: one LLM call ~ thousands of native evals, so plateau-only, cache by (signature, fails) key. Benchmark: the 3M-run best sat on 'level 0 not connected' + 'me1 on wrong level' for \u003e1M evals — moves a plan-reader fixes in one edit. Use claude via API (see claude-api skill); temperature\u003e0 for diverse proposals. Later extension (separate issue): AlphaEvolve-style operator-code synthesis using our existing A/B harness as the evaluator.","acceptance_criteria":"on the evolved-3M-nols-3 15-fail plateau seed: repair loop reduces hard-fail count where 1M+ blind evals did not, within \u003c=20 LLM calls; edit-DSL rejects malformed proposals; A/B at equal native-eval budget shows strictly better final fails on \u003e=2/3 seeds","notes":"Planning session 2026-08-05 produced a full implementation plan (edit DSL, driver stagnation hook, CLI flags, phasing) saved at /home/bruno/.claude/plans/glowing-snuggling-flute.md. Blocked on homemaker-py-8oq (review with a more capable model) before implementation starts in a new session. No code was written yet.","status":"open","priority":2,"issue_type":"feature","assignee":"Bruno Postle","owner":"bruno@postle.net","created_at":"2026-08-02T09:15:54Z","created_by":"Bruno Postle","updated_at":"2026-08-06T07:06:45Z","started_at":"2026-08-04T23:31:21Z","dependencies":[{"issue_id":"homemaker-py-2g7.7","depends_on_id":"homemaker-py-2g7","type":"parent-child","created_at":"2026-08-02T10:15:53Z","created_by":"Bruno Postle","metadata":"{}"},{"issue_id":"homemaker-py-2g7.7","depends_on_id":"homemaker-py-8oq","type":"blocks","created_at":"2026-08-06T08:06:10Z","created_by":"Bruno Postle","metadata":"{}"}],"dependency_count":1,"dependent_count":2,"comment_count":0}
{"id":"homemaker-py-2g7.6","title":"Spike: graph-first construction — adjacency-realizing slicing trees / rectangular dualization","description":"Research spike, timeboxed. Literature: rectangular dualization (planar triangulated graph -\u003e rectangular floorplan) and characterizations of slicible adjacency graphs. Our programme already IS an adjacency graph (every room wants c, plus secondary pairs); instead of mutating trees hoping adjacency emerges, construct trees that realize the required adjacency by construction — the direction §11.6/§11.7 crawled toward greedily. Deliverable is a WRITTEN assessment (DESIGN.md section): can harbor's programme graph (16 rooms + spine, 2 storeys with stacking constraint) be dualized into slicing trees, how many, and is enumeration of realizing trees tractable? Prototype only if the answer is clearly yes. Watch for: multi-storey Below-inheritance constrains both floors' trees jointly; circulation spine is a connected dominating set requirement, not a simple adjacency.","acceptance_criteria":"DESIGN.md section with go/no-go verdict, the relevant algorithms named, and complexity estimate for harbor-scale programmes","status":"closed","priority":2,"issue_type":"task","assignee":"Bruno Postle","owner":"bruno@postle.net","created_at":"2026-08-02T09:15:07Z","created_by":"Bruno Postle","updated_at":"2026-08-04T23:21:32Z","started_at":"2026-08-04T23:08:59Z","closed_at":"2026-08-04T23:21:32Z","close_reason":"NO-GO verdict in DESIGN.md §37.8: rectangular dualization assumes one-vertex-one-rectangle; harbor's circulation hub is an emergent-shape multi-leaf region (§11.6 CDS seeding), breaking that assumption where it matters. Room-only graph is a trivial 3-edge matching, already fully satisfied by §11.7 seeding (0 secondary-adjacency fails on the 2g7.7 plateau benchmark). No multi-storey dualization precedent in the literature. Not prototyping.","dependencies":[{"issue_id":"homemaker-py-2g7.6","depends_on_id":"homemaker-py-2g7","type":"parent-child","created_at":"2026-08-02T10:15:07Z","created_by":"Bruno Postle","metadata":"{}"}],"dependency_count":0,"dependent_count":0,"comment_count":0}
{"id":"homemaker-py-2g7.5","title":"CP-SAT type assignment for a fixed tree (replace swap/retype random walk)","description":"For a FIXED topology, assigning room codes to leaves subject to counts, required levels, adjacency-to-circulation-spine, secondary adjacencies (k1-da1, da1-o...), and share grouping is a small discrete problem (~30-70 leaves, ~16-26 codes) — well within OR-Tools CP-SAT range, solvable optimally in milliseconds. Today swap/retype/level_retype random-walk this space; §11.6/§11.7's greedy constructive assignment was the single biggest fail-count win of Phase 6, and CP-SAT is its exact big brother. Plan: model leaf-graph adjacency (geometry.leaf_graph) as fixed at seed geometry; objective = weighted satisfied adjacencies + level compliance; use as (a) seeder replacing the greedy _assign_adjacency_aware, (b) periodic 'reassign' operator inside search (the assignment analogue of ruin_recreate §23), (c) post-collapse repair. Note the §11.2 lesson: assignment quality at SEED geometry can shift after the inner loop moves ratios — re-run assignment after geometry settles (alternating minimization).","acceptance_criteria":"A/B vs greedy seeder (harbor+maple, 3 seeds, 20k evals): adjacency+access seed fails strictly lower; end-to-end mean fails no worse; reassign operator fires and is accepted at least once per run","notes":"2026-08-04: Ran the bead's own full acceptance-criteria A/B (harbor+maple,\n3 seeds, budget=20000, experiments/ab_cpsat_assign.py) to completion --\n~12h wall clock, 18 driver.search runs total. Raw log:\nexperiments/results/ab_cpsat_assign_20k_harbor_maple.log.\n\nResults (mean hard/soft fails over 3 seeds):\n harbor-house: greedy 9.3/35.3 | cpsat 13.0/32.0 | reassign 8.3/35.3\n maple-court: greedy 22.7/72.3 | cpsat 22.3/68.3 | reassign 35.3/77.7\n reassign_fired (mean/3 seeds): harbor 0.0, maple 0.3 (fired in just\n 1 of 18 runs total, i.e. 1 of 6 reassign-arm runs).\n\nVerdict: acceptance criteria NOT met.\n - \"adjacency+access seed fails strictly lower\" -- cpsat is WORSE than\n greedy on harbor-house hard fails (13.0 vs 9.3); roughly a wash on\n maple-court (22.3 vs 22.7). No consistent win.\n - \"end-to-end mean fails no worse\" -- reassign arm is much worse on\n maple-court (35.3 vs 22.7 hard).\n - \"reassign fires and is accepted at least once per run\" -- fired in\n only 1/6 reassign-arm runs, 0/3 on harbor-house entirely. Confirms\n the earlier pilot's diagnosis: even at 20k budget the uniform-weight\n operator draw rate is too low for it to matter in a single run.\n\nDecision: assign_solver stays default \"greedy\", enable_reassign stays\ndefault False. The seeder-level win documented in\ntest_assign_cpsat_matches_or_beats_greedy_secondary_adjacency (isolated,\nseed-geometry-only, secondary-adjacency-only metric) does not survive\ncontact with a full driver.search run across two programmes -- likely\nbecause CP-SAT's exact optimum at seed geometry doesn't stay optimal once\nthe inner loop moves ratios (the bead's own §11.2 lesson), and any gain\ngets swamped by search noise at this budget. Not pursuing a higher budget\nor more seeds -- the direction (no clear win, one programme regresses)\nis consistent enough with the pilot to close this out rather than keep\nspending compute chasing it.\n\nThis bead's acceptance criteria are now fully evaluated (both shipped\nsub-items (a)/(b) AND this final A/B). Closing homemaker-py-2g7.5.\nhomemaker-py-5bv (item (c), post-collapse repair) remains open as a\nseparate child bead.","status":"closed","priority":2,"issue_type":"feature","assignee":"Bruno Postle","owner":"bruno@postle.net","created_at":"2026-08-02T09:15:06Z","created_by":"Bruno Postle","updated_at":"2026-08-04T22:05:06Z","started_at":"2026-08-03T22:44:01Z","closed_at":"2026-08-04T22:05:06Z","close_reason":"Closed","dependencies":[{"issue_id":"homemaker-py-2g7.5","depends_on_id":"homemaker-py-2g7","type":"parent-child","created_at":"2026-08-02T10:15:05Z","created_by":"Bruno Postle","metadata":"{}"}],"dependency_count":0,"dependent_count":0,"comment_count":0}
{"id":"homemaker-py-cvw","title":"Parallel staged runs: substrate_readiness reads stale id()-keyed geometry cache in the parent process","description":"Found by the homemaker-py-zrx expert review. geometry._cache is keyed by (id(node), idx) and relies on every reader being preceded by clear_cache(). In driver.search_staged stage 1 with n_workers\u003e1 that contract breaks: the PARENT process never runs score_with_fails (children are scored in the pool workers), so its cache is never cleared, yet _rank_fitness -\u003e rank_bonus_fn -\u003e graph.substrate_readiness(ind.root) reads geometry.area()/coordinate() in the parent on every tournament/admit comparison. Evicted individuals are eventually gc'd (Node trees are parent\u003c-\u003echild reference cycles, freed by the cycle collector) while their cache entries linger; freshly unpickled worker results reuse those addresses, and substrate_readiness then serves another (dead) tree's coordinates.\n\nVerified with a probe simulating the parent's allocation pattern (unpickle jittered harbor-house trees, pop-16 eviction churn, periodic gc.collect): 24/300 readiness computations returned a corrupted value, worst absolute error 0.999 on the [0,1] readiness scale (i.e. completely wrong), and the parent cache grew without bound (38k entries after 300 children — it is never cleared for the whole run). Serial staged runs are safe (every in-process score_with_fails clears the cache between children, and live/dead id coexistence prevents collisions).\n\nImpact: silently biases stage-1 substrate selection in every parallel staged run (run_staged_search.py with WORKERS\u003e1 — the default experimental harness), and makes the bias address-dependent, i.e. NON-DETERMINISTIC across byte-identical re-runs. This is a concrete, static-read-visible candidate mechanism for part of homemaker-py-b8g's irreproducibility (it is not BLAS): it perturbs the stage-1 trajectory, not a single fixed-genome score. Reported fitness numbers are unaffected (the bonus only reorders the comparator).\n\nRecommended fix: geometry.clear_cache() at substrate_readiness entry (cheap: the readiness read is a handful of areas on the base level; serial-mode behaviour is unchanged because the cache there is already cold at that point). The durable fix for the whole bug class — also covering the (unobserved but real) gc-timing hazard in collapse_finish's cand deepcopy, probed 0/6 today only because cyclic trees outlive the deepcopy window — is to cache on the Node object itself (as Urb does, per geometry.py's own comment) or key by a per-tree epoch, so a recycled address can never alias. Also add a defensive geometry.clear_cache() at collapse_global entry (one line, zero practical cost: finish-time it is one-shot, in-search the cache was just cleared by _evaluate_full).","status":"closed","priority":2,"issue_type":"bug","assignee":"Bruno Postle","owner":"bruno@postle.net","created_at":"2026-08-02T08:19:18Z","created_by":"Bruno Postle","updated_at":"2026-08-02T09:52:49Z","started_at":"2026-08-02T09:52:09Z","closed_at":"2026-08-02T09:52:49Z","close_reason":"Fixed: geometry.clear_cache() added at substrate_readiness (graph.py) and collapse_global (fitness.py) entry points; commit 2f26f46. Full test suite (338 tests) passes.","dependency_count":0,"dependent_count":1,"comment_count":0}
@ -130,27 +132,27 @@
{"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.","notes":"DEPRIORITISED by Diagnostic B (§13.2). B shows the inner loop CANNOT repair undersize: the slack is depth-driven maldistribution baked into the frozen topology, and the equal-offset ratio DOF cannot shrink a 14x leaf to feed a starved one without trading into shape fails (0.5^n cliff). Wrong DOF and wrong direction — the blocker is slicing POSITION, not a missing expansion reward. Fix belongs upstream in construction/topology (erc.4 re-scoped, erc.3). Keep as a low-priority follow-up only if a depth-balanced construction still leaves a residual size gradient the inner loop could pick up.","status":"closed","priority":4,"issue_type":"feature","owner":"bruno@postle.net","created_at":"2026-06-22T23:16:24Z","created_by":"Bruno Postle","updated_at":"2026-06-28T13:22:22Z","closed_at":"2026-06-28T13:22:22Z","close_reason":"wont-fix (DESIGN §13.7): Diag B (§13.2) showed the inner loop cannot repair undersize (wrong DOF — slicing position, frozen-topology ratios). Superseded by depth-balanced construction (erc.4). Condition unmet.","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.","notes":"DEPRIORITISED by erc.1 verdict (§13.1): per-leaf shape-fail flat vs slicing density and cuts already squarest (_size_divisions_from_targets picks squarest rotation) yet still ~1.8 fails/leaf =\u003e little compactness headroom at fixed leaf count. Floor is intrinsic to leaf COUNT, not cut quality. Revisit only if leaf-sharing (erc.3) underdelivers.","status":"closed","priority":4,"issue_type":"feature","owner":"bruno@postle.net","created_at":"2026-06-22T23:16:21Z","created_by":"Bruno Postle","updated_at":"2026-06-28T13:22:17Z","closed_at":"2026-06-28T13:22:17Z","close_reason":"wont-fix (DESIGN §13.7): Diag A (§13.1) showed the floor is intrinsic to leaf COUNT not cut quality; revisit condition was 'only if leaf-sharing underdelivers' but leaf-sharing OVER-delivered (−32…−39%, §13.3). Condition unmet.","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-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":"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":"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":"ld2-13-6-interior-o-seed-diagnostic-all","value":"ld2/§13.6 interior-O seed diagnostic: ALL crinkliness fails in the constructed bal+share seed are UNDER-exposed (crink\u003c0.62, landlocked rooms with no facade + no uncovered-O neighbour) — zero over-exposed sliver fails. So the erc crinkliness residual is genuine under-daylighting, validating the interior light-well premise. Default outside_divisor=6 was too sparse (null: harbor 147-\u003e142, crinkliness even rose). odiv=3 is the seed-optimal joint setting: harbor seed fails 147-\u003e129 (-18), maple 219-\u003e206 (-14), landlocked fails drop, at cost of more leaves (harbor +4, maple +8). Because it ADDS leaves it carries the §13.4 wash-out risk; A/B to convergence pending."}
{"_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":"experiment-seeding-pitfall-run-search-scaled-py-s","value":"Experiment seeding pitfall: run_search_scaled.py's default PH_SEED (c964…dom) is a FINISHED programme-house design — passing it warm-starts and floors at ~3 fails, NOT a blank-slate topology search. For blank-slate runs comparable to §11.5/§11.6 baselines, seed from examples/programme-house/init.dom (a bare undivided plot; driver bootstrap auto-triggers only on bare plots). Bit the 6zy sweep — first pass used c964 and falsely showed 3-fail floor across the whole grid."}
{"_type":"memory","key":"ld2-13-6-interior-o-seed-diagnostic-all","value":"ld2/§13.6 interior-O seed diagnostic: ALL crinkliness fails in the constructed bal+share seed are UNDER-exposed (crink\u003c0.62, landlocked rooms with no facade + no uncovered-O neighbour) — zero over-exposed sliver fails. So the erc crinkliness residual is genuine under-daylighting, validating the interior light-well premise. Default outside_divisor=6 was too sparse (null: harbor 147-\u003e142, crinkliness even rose). odiv=3 is the seed-optimal joint setting: harbor seed fails 147-\u003e129 (-18), maple 219-\u003e206 (-14), landlocked fails drop, at cost of more leaves (harbor +4, maple +8). Because it ADDS leaves it carries the §13.4 wash-out risk; A/B to convergence pending."}
{"_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":"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":"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":"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":"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":"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":"collapse-global-94g-and-any-label-usage-optimisation","value":"collapse_global (94g) and any label/usage optimisation CANNOT fix geometry-intrinsic fails. The harbor-house 15-fail best layout contains long-thin cells that are useless whatever room usage is assigned — their width/proportion/crinkliness fails are shape-bound, not label slack. Two consequences: (1) do not over-claim collapse gains — only ~2-3 of that layout's fails are reclaimable relabel slack, the rest are geometry- or building-level bound; (2) the threshold objective must not be tuned to 'pass' a degenerate cell via a permissive room type — a metric-pass on a physically useless space is gaming, not a fix. Real remedies for these are geometry/topology search (cell shape) and circulation placement, filed separately, not the collapse."}
{"_type":"memory","key":"experiment-harness-gotcha-the-leaf-sharing-relaxed-objective","value":"Experiment harness gotcha: the leaf-sharing RELAXED objective (§13.3) is injected ONLY by monkeypatching fitness.load_config in the parent process (run_staged_search.py / probe scripts). This is parent-process-only and does NOT propagate into ProcessPoolExecutor workers (n_workers\u003e1), which re-import fitness fresh and score under the STRICT on-disk patterns.config -\u003e r.n_fails MISMATCH (worker strict vs parent relaxed re-score). ALL §13.x floor runs were therefore SERIAL. Any future PARALLEL leaf-sharing experiment will silently mis-score until leaf_sharing lives on disk/CLI (tracked: homemaker-py-x3b). The parallel driver itself is correct; both paths score via load_config(programme_dir)."}
{"_type":"memory","key":"island-model-psk-14-is-a-null-priming","value":"Island model (psk, §14) is a NULL: priming a population from N converged independent elites + crossover-heavy migration does not beat best-of-N at equal total budget (maple island 124 vs control 116). The child_probe instrument shows WHY: area-matched crossover across independently-converged elites almost never synthesizes (1-3 of ~64 children beat the better parent, max drop 2-5) because the slicing encoding is non-canonical (9gp), so splices are disruptive not combinatorial. Search-machinery null #3 after graded-objective and niching/restarts; residual stays geometry/shape-bound."}
{"_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":"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":"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":"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":"9o5-multi-use-leaves-is-path-a-superposition","value":"9o5 multi-use leaves is path (a) — superposition as SEARCH RELAXATION that COLLAPSES to specific usage at the end, NOT path (b) loose-fit/no-collapse. Bruno's intent: codes with SIMILAR leaf requirements form an interchangeable equivalence class; during evolution the solver doesn't commit which leaf serves which specific usage (smoother landscape, no fighting over exact leaf usage); at the end the layout is CONDENSED to specific usages by brute-forcing the in-class assignment (3 interchangeable usages over 3 leaves = 3! = 6 combinations to check, pick best). 'Derive automatically' compatibility = requirement-similarity grouping. This reverses the issue's stated 'path b preferred' note."}
{"_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-3l6-fix-leaf-sharing-evolve-runs","value":"homemaker-py-3l6 fix: leaf-sharing evolve runs now auto-finish before write via driver.polish_finish — unfold_shared_leaves() then a warm-started leaf_sharing=False polish search (--polish-budget, default budget//2). Makes the written .dom honest under canonical homemaker-fitness (internal==canonical when leaf_sharing off). Interrupt path forces polish_budget=0 (unfold+rescore only). This is yaa's unfold-then-polish, made automatic; Schedule B annealing is still kpu."}
{"_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":"unfold-strategy-for-shared-leaves-homemaker-py-8iv","value":"Unfold strategy for shared leaves (homemaker-py-8iv, resolved 2026-07-16): use the BALANCED GRID (operators._grow_balanced/_size_subtree_equal), NOT circulation-aware slicing. Slicing a shared leaf perpendicular to its access edge so every child touches the corridor was implemented + A/B-tested and LOST decisively (150k-eval warm-start polish from evolved-3M: slice 41 fails/3.5e-14 vs grid 25 fails/2.4e-09, grid ahead at every milestone). Reason: k rooms all touching one wall are intrinsically thin slices; that geometric debt (proportion/long/width) is unfixable without topology change, while the grid's squarer children let local search re-route access cheaply via level_retype/place_missing/level_fix. Lesson: at the sharing-\u003eno-sharing transition, prioritise squarer children and leave access to local search; do not reintroduce slicing in Schedule B (kpu)."}
{"_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":"collapse-global-s-jacobi-adjacency-relaxation-homemaker-py","value":"collapse_global's Jacobi adjacency relaxation (homemaker-py-94g) is a synchronous per-round linear-assignment re-solve, which can 2-cycle indefinitely between two labellings that each satisfy ZERO adjacency requirements even though a permutation satisfying ALL of them exists -- proven on a minimal 4-cell chain (p1-q1-p2-q2, two disjoint adjacency pairs p1\u003c-\u003ep2/q1\u003c-\u003eq2) in test_two_opt_polish_escapes_jacobi_plateau. homemaker-py-9wi added Fitness._two_opt_adjacency_polish: a same-level pairwise-swap local search run after the Jacobi fixpoint, gated behind collapse_global(local_search=True) (default off, exposed as homemaker-collapse --local-search). Monotone by construction (a swap is kept only if it strictly increases total reward). Empirically on the 11 harbor-house evolved-*.dom/3m.dom/materialised-3M.dom layouts: 10 matched Jacobi-only exactly, 0 regressed, and evolved-anneal-3M.dom improved 21-\u003e19 fails (fixed a genuine mutual da1\u003c-\u003ek1 adjacency miss the Jacobi loop couldn't reach)."}
{"_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":"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":"9o5-multi-use-leaves-is-path-a-superposition","value":"9o5 multi-use leaves is path (a) — superposition as SEARCH RELAXATION that COLLAPSES to specific usage at the end, NOT path (b) loose-fit/no-collapse. Bruno's intent: codes with SIMILAR leaf requirements form an interchangeable equivalence class; during evolution the solver doesn't commit which leaf serves which specific usage (smoother landscape, no fighting over exact leaf usage); at the end the layout is CONDENSED to specific usages by brute-forcing the in-class assignment (3 interchangeable usages over 3 leaves = 3! = 6 combinations to check, pick best). 'Derive automatically' compatibility = requirement-similarity grouping. This reverses the issue's stated 'path b preferred' note."}
{"_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":"unfold-strategy-for-shared-leaves-homemaker-py-8iv","value":"Unfold strategy for shared leaves (homemaker-py-8iv, resolved 2026-07-16): use the BALANCED GRID (operators._grow_balanced/_size_subtree_equal), NOT circulation-aware slicing. Slicing a shared leaf perpendicular to its access edge so every child touches the corridor was implemented + A/B-tested and LOST decisively (150k-eval warm-start polish from evolved-3M: slice 41 fails/3.5e-14 vs grid 25 fails/2.4e-09, grid ahead at every milestone). Reason: k rooms all touching one wall are intrinsically thin slices; that geometric debt (proportion/long/width) is unfixable without topology change, while the grid's squarer children let local search re-route access cheaply via level_retype/place_missing/level_fix. Lesson: at the sharing-\u003eno-sharing transition, prioritise squarer children and leave access to local search; do not reintroduce slicing in Schedule B (kpu)."}
{"_type":"memory","key":"experiment-harness-gotcha-the-leaf-sharing-relaxed-objective","value":"Experiment harness gotcha: the leaf-sharing RELAXED objective (§13.3) is injected ONLY by monkeypatching fitness.load_config in the parent process (run_staged_search.py / probe scripts). This is parent-process-only and does NOT propagate into ProcessPoolExecutor workers (n_workers\u003e1), which re-import fitness fresh and score under the STRICT on-disk patterns.config -\u003e r.n_fails MISMATCH (worker strict vs parent relaxed re-score). ALL §13.x floor runs were therefore SERIAL. Any future PARALLEL leaf-sharing experiment will silently mis-score until leaf_sharing lives on disk/CLI (tracked: homemaker-py-x3b). The parallel driver itself is correct; both paths score via load_config(programme_dir)."}
{"_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":"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":"island-model-psk-14-is-a-null-priming","value":"Island model (psk, §14) is a NULL: priming a population from N converged independent elites + crossover-heavy migration does not beat best-of-N at equal total budget (maple island 124 vs control 116). The child_probe instrument shows WHY: area-matched crossover across independently-converged elites almost never synthesizes (1-3 of ~64 children beat the better parent, max drop 2-5) because the slicing encoding is non-canonical (9gp), so splices are disruptive not combinatorial. Search-machinery null #3 after graded-objective and niching/restarts; residual stays geometry/shape-bound."}
{"_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":"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."}