Leaf Reweight

What this is

A bounded correction around fair D4 that makes the frozen leaf's nineteen weights runtime data instead of compile-time constants, and then tests the reweightings that finding-10 Addendum A says predict achievable clears far better than the frozen vector does.

Nothing else changes. The feature extractor is drop7::fair_only_horizon::extractFairFeatures, called by name from the unmodified reference; the chance stratification, canonicalization, cache keying, column order, work accounting and terminal utility all come from the frozen code by way of ../risk-calibration/search.cpp, which is already proved decision-identical to the reference. The only degree of freedom is the weight vector.

The CHECK gate

With the frozen weight vector the local leaf must return values whose raw uint64_t bit patterns are identical to frozen::fairLeaf, and the search must select identical columns and spend identical work. The dot-product accumulation order is preserved term for term, because floating-point addition is not associative and bit-identity depends on it.

./approaches/lifetime-objective/leaf-reweight/build.sh
B=./build/lifetime/leaf-reweight

# 1. leaf bit patterns on real fair-play boards and their chance expansions
$B --leaf-check --depth 3 --chance-samples 7 --max-work 16000000 \
   --seed-start 0xa5278000 --check-games 24 --check-moves 400
$B --leaf-check --depth 4 --chance-samples 7 --max-work 16000000 \
   --seed-start 0xa5278100 --check-games 3 --check-moves 45

# 2. the frozen reference driver, at its own configuration
$B --reference-parity --seed-start 0xa5278200 --check-games 4 --check-moves 60

# 3. weights-as-data vs weights-as-constants, columns AND work, at the
#    configuration the evaluation actually uses
$B --self-parity --depth 4 --chance-samples 5 --max-work 3200000 \
   --seed-start 0xa5278400 --check-games 3 --check-moves 60
$B --self-parity --depth 4 --chance-samples 7 --max-work 16000000 \
   --seed-start 0xa5278300 --check-games 3 --check-moves 22

Setting weights

# one constant at a time
$B --arm t1-rough --depth 4 --chance-samples 7 --max-work 16000000 \
   --weight roughness=560 --seed-start 0xa5279000 --games 64 --threads 10

# a whole vector from a file of "name value" lines, plus an optional bias
$B --arm t2 --weights approaches/lifetime-objective/leaf-reweight/weights-refit-fair-achievableClears-a0p5.txt ...

--max-work 16000000 is mandatory at seven chance strata. Worst-case depth-4 work there is 11,892,398; leaving the frozen 3,200,000 bound in place silently degrades the search to a completed depth 3, which has already produced one wrong conclusion in this programme (finding-05).

Analysis scripts

script	what it does
refit.py	fits the leaf's 19 features to the achievable-clear label, rescales the fitted direction into leaf units matched to the frozen leaf's mean and standard deviation, and writes weights-refit-*.txt at several frozen↔fitted interpolation weights
tier1.py	per-term Tier-1 anchors, and the cross-origin transfer table that decides whether the fitted direction is a synthetic-position artifact
sweeptable.py	tabulates a directory of cohort artifacts against frozen, paired by seed
compare.py	the reported cohort table: quantiles, flow rates, occupancy, paired deltas with a one-sided 95% bootstrap lower bound over whole games, W-T-L, and the lower-tail listing

Result

See finding-14.

Sources

• search.cpp, build.sh, sweep.sh, evaluate.sh
• refit.py, tier1.py, sweeptable.py, compare.py