[{"data":1,"prerenderedAt":117},["ShallowReactive",2],{"run:lego-phase-f1-emit-simd-5k":3,"run-findings:lego-phase-f1-emit-simd-5k":93},{"run":4,"config":20,"metrics":54,"curve":60,"assets":92},{"id":5,"title":5,"subtitle":6,"eyebrow":7,"date":8,"workspace":9,"commit":10,"tags":11,"verdict":19},"lego-phase-f1-emit-simd-5k","Phase F.1 emit_pairs_simd gate flip 5k smoke (clean baseline 比較用)","Run summary · P1 Phase F.1 Tier 1 gate flip","2026-05-25","splat","077d726",[12,13,14,15,16,17,18],"p1-profile","axis-1","phase-f","emit-simd","lego-5k","premultiplied","tier-1","partial",[21,24,27,30,33,36,39,42,45,48,51],{"key":22,"value":23},"dataset","\u002FUsers\u002Fotkrickey\u002Fdev\u002F3dgs-workspace\u002Fdatasets\u002Fnerf_synthetic\u002Flego",{"key":25,"value":26},"gt_convention","Premultiplied",{"key":28,"value":29},"iterations","5,000",{"key":31,"value":32},"seed","42",{"key":34,"value":35},"capacity","1,000,000 splats",{"key":37,"value":38},"sh_degree","3",{"key":40,"value":41},"loss","L1Ssim",{"key":43,"value":44},"lambda","0.200",{"key":46,"value":47},"ssim","window=7 sigma=1",{"key":49,"value":50},"backend.backward","Simd",{"key":52,"value":53},"backend.loss_path","Gpu",{"psnr":55,"wallclock":56,"wallclock_regress":57,"splats":58,"final_loss":59},31.588451385498047,"1m 57s",false,84208,"1.436054e-2",{"loss":61,"splats":86},{"iters":62,"values":74},[63,64,65,66,67,68,69,70,71,72,73],1,500,1000,1500,2000,2500,3000,3500,4000,4500,5000,[75,76,77,78,79,80,81,82,83,84,85],0.5938447713851929,0.08621859550476074,0.05430267006158829,0.032195527106523514,0.019590672105550766,0.017106300219893456,0.015984008088707924,0.01537264883518219,0.01484949141740799,0.014587678015232086,0.014360540546476841,{"iters":87,"values":88},[63,64,65,66,67,68,69,70,71,72,73],[89,90,91,58,58,58,58,58,58,58,58],5207,842,11358,{"has_renders":57,"has_splat":57},[94],{"id":95,"title":96,"date":8,"status":97,"polarity":98,"category":99,"axes":100,"tags":101,"task_code":107,"related_runs":108,"delta_psnr":111,"delta_wallclock":112,"rank":113,"verdict":114,"impact_summary":115,"detail_path":116},"p1-axis1-phase-f1-emit-simd-falsified","Phase F.1 emit_pairs_simd + f16 forward gate flip — audit Tier 1 仮説 falsified、現規模で net regression \u002F no improvement","stable","negative","audit",[63],[102,14,15,103,18,104,105,106,16],"p1-axis1","f16-forward","falsified","negative-finding","ab-test","P1 Phase F.1 \u002F F.2",[5,109,110],"lego-phase-f1-baseline-5k","lego-phase-f2-f16-fwd-5k","±0.13 dB (両者とも許容範囲、atomic\u002Ffp 順序由来)","+4.7% (emit_simd net regression) \u002F +2.5% (f16 fwd noise 圏内)","medium","audit-falsified-tier-1","audit (p1-axis1-metal-opt-audit) で Tier 1「即 actionable gate flip、-0.7-1.0% wallclock、zero risk」と分類した 2 候補を Lego 5k smoke A\u002FB で実証検証。\u003Cstrong>emit_pairs_simd は total wallclock +4.7% の net regression\u003C\u002Fstrong> (112.11s → 117.38s、~10 kernel 平均なので noise floor 小、real regression 確定)、ただし per-kernel emit_pairs 単体は +8.5% で baseline 2 sample 変動 (4.814 \u002F 5.129、6.5%) と近い hedge 必要。\u003Cstrong>f16 forward は ~+2.5% wallclock\u003C\u002Fstrong> (114.97s)、run-to-run variance 圏内で improvement \u002F regression いずれも明確に検出できず。\u003Cstrong>PSNR は両者で許容範囲\u003C\u002Fstrong> (emit_simd -0.132 dB、f16 +0.075 dB、atomic order \u002F fp 順序由来想定)。**audit の予測 calibration data**: Tier 1 SIMD-reduction 系の効果は theory より小さく overhead が打ち消し、Tier 2 別 mechanism (CPU-GPU sync 除去) は別途検証必要、Tier 2 同 family (backward TBDR) は falsification 拡大適用で skip 判断強化。卒論 narrative 価値: 「audit theoretical predictions vs empirical measurements」の方法論 paragraph を §5.4 negative findings 章 (chapter-5-4-negative-findings.md) に追加候補。","\u002Ffindings\u002Fp1-axis1-phase-f1-emit-simd-falsified\u002F",1782449788227]