4 arms x 7 runs vs real DrussGT. mix alternates the two guns 476 times/7 runs
(liveness OK) but our bullets are no more varied (power sd / aim-offset sd flat)
and DrussGT's dodge quality is unchanged (miss/tick mix-pat +0.03, p=0.66; MDE
3.8%). mix wins 24/49 = the 49% baseline; the user's 6/10 has P=0.353 at 49%.
New tools/ab/ab_dodge_analyze.py splits the validated per-shot dodge instrument
by arm and adds gun-switch/power/bearing liveness; fixtures committed.
- docs/bitbrain_gun_verdict.md: control vs bb_decay (decay SBC memory) vs a
provably-zero placebo, 30 runs/arm vs real DrussGT. Nothing separates
(bb_decay +3.3 dmg/run, p=0.71; round wins 97/210 vs 97/210, p=1.00); the
7-run shape does not replicate. TR_BITBRAIN_RANGE=0 is clamped to 1.0 deg
(bitbrain_gun.nim:207) so it is NOT a zero-shift placebo; TR_BITBRAIN_MIN_OBS
unreachable is used instead.
- tools/ab/ab_analyze.py: keep exact enumeration for C(n,na)<=20e6 (7v7), add
a seeded Monte-Carlo permutation test (1e6 draws, 0x5eed5eed) with its
standard error, a tie-corrected Mann-Whitney U cross-check, a minimum
detectable effect line, all-pairs comparisons, and a [bb] shift check.
- tools/ab/README.md: document the new analyzer output.
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 = eb74f9b2, sha256 cb66d66b..., real DrussGT,
every arm forced alone with TR_RACK_<GUN>=both and all 14 others off, liveness
confirmed per run):
arm shots real % dmg/run round wins
onlyPattern 4610 10.74% 285 25/49
onlyTMPATTERN (radial) 3374 3.50% 71 0/49
onlyLinear 3218 3.23% 61 0/49
Pattern vs TM: +7.22 pp / +213.7 dmg, exact p=0.0006
TM vs Linear: +0.30 pp, p=0.659 (dmg p=0.438)
**The TM is statistically INDISTINGUISHABLE from its own Linear base live.** So it
is not "the TM works and we are aiming it wrong".
DIRECT ANSWER: **(c) It loses live AND sits at/below majority - the target carries
no learnable signal beyond the base rate, and that is the reason.** The reason is
not the machine, not the knobs, and not the application alone: the thing it was
asked to predict is dominated by the modal answer.
This closes the TM-as-gun thread. If a TM is wanted in the bot, a firing gate or a
movement decision is a better fit for a boolean-rule classifier than an aim point -
that is untested and is a different project.
A LIVE GF-MODE ARM WAS NOT RUN (stated as unmeasured): the task pinned one frozen
HEAD binary and HEAD registers the TM gun as radial only; Task 1 already makes GF
the unpromising candidate.
HARNESS FIX WORTH KEEPING: `tools/ab/which_gun_arm_env.sh` left the TARGET gun
unset, so with the now-Pattern-only default it silently fell back to the FULL rack
- an arm could appear to test a single gun while actually running the whole rack.
It now emits `TR_RACK_<GUN>=both` for the target and `=off` for all 14 others.
(Earlier which-gun results are unaffected: they ran before the Pattern-only default,
or - as in the melee/1v1 campaign - set the explicit `=both` themselves.)
tm_pattern.nim gains a per-class confusion matrix (warm samples only) to support the
majority baseline; no behaviour change. Adds Round 4 to
tm_pattern_sweep_results.md with both tasks and the interpretation rule.
Follow-up to e0666a5, which showed Pattern alone (10.78%) beats the full rack
(6.93%). That left two open questions: is a SMALL rack of good guns better than
Pattern alone, and does the selector add value on a good rack (rather than only
on the bloated one)? Both are now answered: NO and NO.
6 arms x 7 runs x 7 rounds, one frozen binary from CLEAN HEAD e0666a5 (built via
`git archive`, source verified byte-identical to the clean tree), rack knobs
only, 8 concurrent battles, real server-side hit rate vs the real DrussGT, exact
two-sided permutation test on per-run rates.
arm guns (selector active?) real % dmg/run p vs onlyPattern
onlyPattern Pattern, NO selection 10.36 264 --
lean8 HeadOn,Linear,Circular,Accel,Pattern,GF,KNN,WallBounce 6.31 146 0.0169
lean6 lean8 - HeadOn 8.83 212 0.0262
pairPC Pattern + Circular 8.23 185 0.0460
pairPK Pattern + KNN 9.80 264 0.3998
pairPL Pattern + Linear 8.23 200 0.0035
The control replicates the prior run (10.36% vs 10.78% before; same binary tree,
different build path).
THE MECHANISM, from the per-arm selected-gun mix - the virtual signal keeps
ranking the WRONG guns first, even on a two-gun rack:
lean8: HeadOn 46.2% of ticks at 2.0% REAL; Pattern only 14.4% (12.6% real)
lean6: Pattern 29.2% (10.4% real) vs KNN 25.3% (8.1%) and Linear 15.2% (8.0%)
pairPC: Circular 66.8% (6.9% real) vs Pattern 33.2% (11.2% real) - over-picks Circular
pairPL: Linear 57.7% (6.0% real) vs Pattern 42.3% (11.1% real) - over-picks Linear
pairPK: Pattern 86.4% - ties ONLY because the selector happens to pick Pattern
most of the time; it is numerically lower with identical dmg/run
So the failure is NOT rack size. Pruning does not fix it; the ranking is wrong.
VERDICT: ship `onlyPattern` - Pattern alone with selection bypassed - at 10.36%
real and 264 dmg/run, vs lean8 6.31%/146 and the prior full rack 6.93%/159.
This DIRECTLY CONTRADICTS the standing user directive to keep virtual-fitness
selection, so it is recorded here plainly rather than quietly acted on: disable
the selector (`TR_RACK_<every gun but PATTERN>=off`) pending a better fitness
signal. The mechanism itself is left intact and functional so it can be re-enabled
with one env var, and so it can be fixed rather than discarded.
REMAINING CAVEAT: ONE ADVERSARY. All of this is vs DrussGT. Pattern as the default
must be re-checked against other bots first - that is the next job.
Extends the reusable harness (tools/ab/which_gun_arm_env.sh now has lean8/lean6/
pairPC/pairPK/pairPL; which_gun_analyze.py is parameterised by WHICHGUN_OUT and
compares against both `full` and `onlyPattern`).
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).
SirStone