7516ca47c0c81edb8a80ba2a66b4bd0b674f65cb
2 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
 SirStone
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b0654d18eb |
TM verdict, settled: it loses LIVE and sits at/below its majority class - (c)
The user pushed back on "the TM can't be your best 1v1 gun", correctly, because two
decisive tests had never been run. Both are now run and they agree.
TASK 1 - THE GF HEAD vs ITS MAJORITY-CLASS BASELINE (offline, n=1,751,067):
label histogram [254286, 284578, 678879, 297055, 236269]
majority class = 2 (the CENTRE bucket) = 38.77%
RAW head accuracy = 36.69% -> margin **-2.08 pp, BELOW majority**
GATED head accuracy = 40.37% vs 38.75% majority -> +1.62 pp, BUT it predicts the
majority class on 62.4% of ticks and its minority recall is 13.6% / 12.9% - a
base-rate predictor wearing a classifier's clothes.
Shuffled control sits at its own majority (20.04% vs 20.12%), confirming chance.
**THE OLD "46% vs 20% CHANCE" FIGURE I QUOTED WAS WRONG ON TWO COUNTS:** the
baseline is 38.8%, not 20%, and the 46% predated the deferred-label fix. Against
the correct baseline the head is BELOW it.
TASK 2 - THE FIRST-EVER LIVE A/B OF THE TM GUN (7 runs x 7 rounds per arm, one
frozen binary from git archive HEAD =
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SirStone
|
e0666a562d |
The gun selector is NEGATIVE value: Pattern alone beats the full rack (p=0.0012)
MEASURED against the real DrussGT, one frozen binary built from clean HEAD, rack knobs only (no source edits), 5 arms x 7 runs x 7 rounds, 8 concurrent battles, judged ONLY on server-side real hit rate from the events sidecar, exact two-sided permutation test on per-run rates. arm runs shots hits real % dmg/run p vs full full (shipped) 7 3898 270 6.93 159 -- onlyPattern 7 4582 494 10.78 287 0.0012 <- BETTER onlyKNN 7 4033 207 5.13 119 0.1340 onlyLinear 7 3215 105 3.27 65 0.0082 onlyGF 7 3193 72 2.25 45 0.0012 Firing Pattern ALONE gives +3.85pp pooled hit rate and +80% damage per run, and it fires MORE shots (4582 vs 3898) - it dominates on rate and volume. This is not "any single gun wins" (full beats Linear, GF and KNN); it is specifically "Pattern alone beats the rack". WHY - the virtual fitness signal mis-ranks guns against real outcomes: - HeadOn is massively over-selected: 31.4% of ticks, the most real shots (1070), but only 4.5% REAL. It alone drags the rack down. - Pattern has the best virtual rank and near-best real rate (11.9%, rank 2), yet is selected only 22.6% of the time. - Linear's apparent strength was SELECTION BIAS: conditional on being selected it looked like 15.2% (n=33), but its UNCONDITIONAL rate (onlyLinear) is 3.27%. Every earlier per-gun "real rate" in this repo is conditional on selection and is therefore confounded. This experiment is the clean measurement. NOT YET SETTLED (do not overclaim): - ONE ADVERSARY. All of this is vs DrussGT. Pattern must be re-checked against other bots before it becomes the default on this evidence alone. - Whether a SMALL rack of good guns beats Pattern alone. The selector is negative value on the CURRENT bloated rack; that does not prove it is negative value on a rack of only good guns. That is the next experiment and it decides whether the selection apparatus is fixed or disabled. - The user's standing directive is to KEEP virtual-fitness selection. This measurement conflicts with it, so the next step tests the selector on a small good rack rather than assuming either answer. Context - three prior selection-side attempts all failed: hysteresis (7.02% -> 5.10%, p=0.002), commitment (7.17% -> 4.44%, p=0.0012), arrival-accuracy tie-break (7.08%, p=0.88 null). The per-tick random draw is load-bearing on three independent measurements. This experiment locates the real problem one level up: which guns are in the rack, and that the virtual signal ranks them wrongly. Preserves the reusable harness (tools/ab/which_gun_run_one.sh, which_gun_arm_env.sh, which_gun_analyze.py) and the full writeup (docs/selector_negative_value.md). |