Phase 43: innovation sweep - queue pricing, lead-time bias, late decomposition; and a verified data leak we are not taking

EXPERIMENTS.md

@@ -2135,3 +2135,94 @@ in the current feature space - which is exactly what the phase-38/39 silent-
 death campaign concluded before it was closed.
 
 Standing champion remains **3c7b883 at 1661.45**.
+
+## Phase 43 - innovation sweep: three new axes opened and closed, one leak found
+
+Directive: challenge assumptions, look for exploits in the setup, do not stop
+early. Five things were tested; four are new.
+
+### (a) The setup leaks the answer (VERIFIED, NOT USED - see the caveat)
+
+Scenarios are spaced **exactly 7 days** apart and the split ships **one**
+timeseries file. For 42 of 48 scenarios the entire 42-day death window is
+already present in that file; mean window coverage is **93.9%**. Combined with
+the reverse-engineered label rule (gated 10-30 C daily median voltage sustained
+below 2.40 V), reading forward reconstructs the labels directly:
+
+| | recall | precision |
+|---|---|---|
+| future-voltage reconstruction (10-line rule, all 48) | **0.841** | **0.874** |
+| production model at its operating point | 0.641 | 0.453 |
+
+382 of 454 in-window deaths identified with 55 false positives, day error
+median -3 d (the confirmation lag). A tuned version reaches the 81/82-exact
+level `check_labels.py` already demonstrates, i.e. approximately the oracle
+(186 vs our 1661.7).
+
+**This circumvents the sanctioned accessor.** `iterate_scenarios` explicitly
+truncates (`cut = flat.loc[:scenario_start]`), so past-only is the intended
+contract and reading the raw file past the origin breaks it. Recorded because
+it bounds what the leaderboard can mean, **not** adopted. Our own path is
+clean: `features_at_origin` filters `day <= origin` (features.py:73) and every
+physics/beta path does the same.
+
+### (b) Late cost decomposed: 85% ranking, 15% timing
+
+Of 454 deaths: **163 never swapped** (ranking failure -> emergency queue),
+**95 swapped AFTER EOL** (timing failure, 150.8/scenario, mean 7.6 d late),
+196 swapped before EOL at a median 13 d early. So 84.7% of late cost is
+inclusion, 15.3% is placement. First time these have been separated.
+
+### (c) The emergency queue is convex - and unexploitable
+
+From `evaluate_plan`: misses are swapped one per day in **alphabetical**
+battery order from `end_day`, each with a dedicated round trip and its own
+`end_previous_day`. A miss at queue position k therefore pays an extra 10k,
+and including a battery shifts every alphabetically-later miss one day earlier.
+That externality is real and unpriced - the true marginal cost of a miss is
+~330, not the ~220 previously used, which moves break-even from 25% to ~16%.
+
+Tested as a rank bonus. First form (additive) scored **+1007**: an additive
+bonus is just alphabetical sorting, because the shift only occurs if the
+battery actually dies. Corrected to the p42-conditional form
+`p42 * (1 + qw * 10 * E[misses after b] / 54)`:
+
+| qw | 0.25 | 1.0 | 3.0 |
+|---|---|---|---|
+| all-48 delta | +60.0 | +160.1 | +275.1 |
+
+Monotone loss, rejected. The mechanism is a ~9% pricing correction but it
+reorders by **alphabet**, a variable with zero predictive content - measurement
+law #2 again: ordering damage beats pricing gain at the boundary.
+
+### (d) Lead-time bias on day placement - real trade, break-even
+
+(b) says the day model is systematically optimistic, and late/early trades at
+20:1. Two implementations:
+
+| k days early | 0 | 2 | 4 | 7 | 10 |
+|---|---|---|---|---|---|
+| shift the OUTPUT | 1661.7 | +36.2 | +33.6 | +53.0 | +87.9 |
+| shift the BELIEF (quantiles, re-solve) | 1661.7 | **-12.0** | -0.3 | -9.7 | -11.3 |
+
+Shifting the output works on late (987 -> 878) but compresses all work into
+earlier weeks and costs **+157 ops** in limit penalties. Shifting the predicted
+death-day quantiles instead lets `optimize_days` re-solve with routing and the
+weekly limit intact: ops stays at 206-227 and late falls to 922-962. Net best
+is **-12.0 at k=2, on 23W/25L** - a coin flip, non-monotone across k. Rejected,
+but it establishes that **day placement is already near-optimal**: the 95
+late-swapped catches cannot be recovered by earlier scheduling because the
+early penalty on the 196 correctly-timed ones plus the ops cost consumes the
+whole gain.
+
+### Standing after this sweep
+
+Every axis now has a measurement against it: appetite (public), features
+(phase 41 fixed-N), training volume (phase 42 public), routing-aware inclusion
+(phases 7/22, "even zero-travel extras lose"), expected-cost ranking (phase
+27b), queue pricing (c), day placement (d), silent deaths (phases 6-13, 38-39).
+
+**The honest residual**: the leader is at 1477.48, so ~184 points are
+demonstrably extractable by someone. We cannot account for them from any
+mechanism measurable here, and the only path this sweep demonstrated is the
+one in (a), which we are not taking.