- signal/normalization/numerics.py: vendor percentile, trimmean, MAD sigma (old/new), bisquare robust fits, float32/float64 typing and sequential sums reproduced exactly (ported from gtc2vcf, MIT) - signal/normalization/fit.py: outlier removal, offset, rotation, shear, scale, nn12/rr12, single-channel model for small Infinium I bins, exact and binned nearest-neighbour sweeps; vendor quirks marked QUIRK - signal/normalization/apply.py: affine transform and theta/R - pipeline/normalize.py + CLI `simurg normalize` - oracle GTCs rebuilt with -ffp-contract=off (aarch64 gcc fuses a*b+c by default, shifting the last bits); the old FMA oracle differed by <=4 ULP Result: transforms bit-identical to the oracle for all 36 GSA samples x 48 bins, for both normalization versions (3456/3456 bins, 0 ULP). Co-Authored-By: Claude Opus 5.5 <[email protected]> Claude-Session: https://claude.ai/code/session_01814GKVzZVL3FuF3DeiZXhv