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pzjOoCcJb/hrK9zpMu6wWhX+9LX407fAn16JP/1z8KevxJ9egT89xd9sJeJKCtj18dUUei+Fb2tF6IwZ/P0UfFUkwrc78DceyVPwRRsjBKHUntieB4oWLPgT1n7GnufZOd11/JJwYmI2Qo9fxNsRHqtQaMAl7i6xMlceKTMLuuBLz6tBG/MVDhpXJuXKuL7FyrSwOQrGrskfKylyejq3K3wy3uMqryw5EcQVC9JQTpesOOlTIR94+x6GpxNeFkVonnKqfJ5Q5SZDcqstlTQwUrmVQucFaIeuyubIyfRUKkorsPL0zs0GQb5ZoN+UBHopRKrxCGleOD0fL1K/khg3WC348i3KYu+mQuxJLZN0S1ufVTT7uM2OFVf/yFB5Wvq2gphApuCrUUtuhs9QivizmRTgOeTHSQzG3/NBjxLlCwJwmVEIv1cYGiuMjRUGR2ZFrduNM4p036gKHOfYIIW4n9vgxVB0ZYKr8nxsXArrfmfHKMFhrMXgcJMErt2e5HErtfEphwUaKcyPxoN/H4sGmFGyvyZrIgLld1Hu6M1NPNdPrtc/RSgpL+GT8yBb781WhWIKh2G23PGp8HgivaKsO/qXyaNII6YqFOoJpxU2DqJu+qD8cAXPIQ1ALzkfkZ/t1Mp6yJ1mgi/Sk+htpOUjVsKYTd94bKWvbNIRGcIKBrygtSc8qpWzfVPvckGgeE4RBthm1KG8AOjPIVByVC/SCFTNQ9xJIYlXL7jIqyii4TqeIML1R3NcA99J9fz0VeBCFDGvp5mRmhqTTXQabiJO3BU3YMCTYoj0z8Cn3wppLcKvIWvnzh+sONJDZ0oXqxy2LwtqenmFLQTSA+oTva/3u3pvMOz29nrawUDrH6DoeaDdJ7JEPVnWpFWfsj1tT9eGe/u6vj8c9PcG3T6v+PQpVWy3cxWTCUl2TMV7Lh/JO8OIN4mlxIDDop1qW+wjvrv5ESA/Fgk3CeFYIRLux9KDaFEoJ2khHa/oidMVXR1vgJVL5xVF2/TnCQ6lMASSAtDNjoDZZZWHAwSfE2shNKzXE3wrdY34th4CpQ1aGMe8JLsgY5SUlsQZ4+y9ud+4uweUDX2xvi6Mn4qNNgl9SzwIfVaOkCPPtKGhNp06AQiVmN0GQdd2PGB47XLFGlYtL1/F3BN5HW0688Mlvj6s3LDNL2KI5GZXWnfSSq5ZxUSSATRtmyW/bzdud/AjlJtZP4H4uM2CPeR1iVZt4FQUPzzkYz/UGBcZGObTu/vE7kJQdPfRMtkRd4+rag37UqVhX9TBm5VVel2pCr5DTlXwZmUVfSgPbZiMbLimijyZZCrrJiLPI5nGulnIk0jmsHYKCM+HLUN9TN7aB2tyFwRGPoyRvuPIY1y7AL6DVAJUqJek/xJE+MciL2D7yzzVp40tobFlsbFyjOGx8U19/c6XU5X/5wqP2GESoK+S/2eg9frF/D99Xavz/9T5f6rz/9ABUJH9R+/qIvlPtzPIJ/7Rh6W0P3pXyvnT1f9w+X66+r8z1w/gcqtUP0Ntm0Q/3UE+c8fvmfIHFO5WCX/07pbZfjbPvM70U2f6+cNl+mkP9zV9AK71fk8f9Pb6w4N+f7vEP7q+N/zclDz/J9MAFcVQnRCoTghUJwSqEwLVCYHqhEB1QqA6IVCdEKhOCFQnBKoTAtUJgeqEQHVCoDohUJ0QqE4I9B+VECgpezOmE4H9Qa97sDfQpdMVb8Y6Pent7WkH/fyTLj7pHfS1/f3uwUB+0qMnPQxNasN9+Un/kqKI+weD/oHWl58MLilGvH9w0Bsc7MlPhvSkuz/UNb2ryU/28Mm+PtR6MAj5wT4Ner+7tzc8yD04oP6Hw56u9w9y0yQM6L3hwV5fy3WvEwaGg+E+DC73gBDQPejt9fTc9PUeH3G3Pxzkm6Lp9/eG2n5u7jqf/B6sXR6epj7Y73V7ozppU520qU7aVCdtqpM21Umb6qRNddKmP3bSpqrcRUf83cMnVXZmIccRcDZmM8K3ELESzQ7N/Qo7tLpmN6vZpZoVdmp1zV5Ws8drlu3Y6pr9rGafalbYudU1B1nNAdWssIOraw6zmkNes2wnV9fcy2ruUc2yHV1dcT+ruM8XpWRnV1c8yCoecPyU7PAVZKBJdKDxaZYM9RVVZRLiNFSy5FfUlEhI5zRUMvVX1JRISO+JVSn4AitqSiSkcxoqOgsrKkoUpAsSKngTKypKBKRzCiq4G3W2tTrbWp1trc62Vmdbq7Ot1dnW6mxrdba1OttanW2tzrZWZ1urs63V2dbqbGt1trU621qdba3OtlZnW6uzrdXZ1upsa3W2tTrbWp1trc62Vmdbq7Ot1dnW6mxrdba1Ottaff3B87+dn568fHfaiR/i37mP9fnfNB0MlkL+N73X1ev8b193/bNMOIV8bFk6N1DtF/lEcEy5s43X5y32HpxO8Ej8ILZd+6MVsq6m5Wx5tdMIrakfmtgQY8Eynvtej7Vfs6z4KU8Jd2Xa4TOe84cN8HHnJgK1ysDNFQMJFx6bODY6u7aBvzrP8deIhf59xOIWT8/x0Fq2ZpPWzGihk9yKzFZgq41ry+NDkEYhyp56hms9Y0/RvbqaBBHceuRqwc2t+6uxiH244+nkrqZGAD8w19QzPIeN6RJxiLPQd6XMa1jcid3AURtzI/SsKKLOg2Vo3WEuvMAxlofszvLudie2t8sHxCTQqe/CA8vkOEiRgT1yrCieZZmRaLHFrHjeNiZ2Cwqm4TKIfRM8fTVv//jOFMx6QF3bBmemnSyadNsGnEa4hlAGzbbvrDCiPSpcZihLRtWmMcBtC/7HSpgYif0dZ92YdAVO6Ly7H0ClKGauPQ39iQVWjmuEt0xJcuuFobHE03IwbbFZTJRFh45gshkdNSryVB6yJPvfA2axS/xjkRsMUwG2GKwXw81neIyL1qnVW0H+8/X6F/SxUf4PBwX5D+DDWv7/kfN/pkkmn3eF6Zm6PxM99Ww8KX9Wte+DLxuUg4TrUvsAf8umcuYulaOEGjmMmCeG3MXsNC/7M4oJPFXTG6UGb34S3a85CVGu72VHeH+/Kb7hP/YuV02192+Yai6rUqHCnvovnXzt2FTLf24EfX353x1oe0X5v1fL/69z/elPf/re8jDPgMUMOWMw30WKGdmzlHPPAisWwDqNRWRcW4dfZjzLVvNTYTY/a3ygA6pkrOHJP27lN6ND9r2xiCIYR4tFNuqhg06f23kGpVVqYcLKwAHp4HtiC5IMvxgs0SmlVzJiMP9u3SyJEngh6dvOJlNu3SMcWa5bALYdpS+65LstqtoBVDVsl96kjJaY5cKI5w2cNNiWE/B1pmBwumRntsjCbKGRjlsty6hjhNd3Y/1w/7IxFafCjViZTtQRVOOBdMWbwi80UvlPuFMpnbDYFFfwVm1cvP4eBV8H+OSgPxjxVMvTCWW7aPcbCIwg9GLArSuq4kg7fFId2sxRVYxT4fMj1sT5N8URVAC+ddUGviQ8ploUn9bAuH4CuMJNKZU9eYJRMBTGt+gRhnS+v41Tv3X5jnUj5q+fKvdqA+X7OMSjdAq+tYDZerrYRE+0cYdt3I9v3cPLy8bCC30H35tv/o/X7Nz4tqeMZ8217xF+wrwVj6MmvsK0AjZ70+nT7WP2qhP+UHkLt9hCcUp6NiG1gdSZG1fW1ZvxJ/sRt3c/3SWtgPtF87K8hUu8pbxR1UYU4rj9wPKUlCa0yw45gVNLaXJGarZYs8J/bAIFYkYwRQUKRiL51Dw+O3l3etw8ZJwI4fcL/BXFIRITFWhZAcbmu1T66sXPSTFfGCCiJ5TkWBXPn7+/SCCASFuJNm4ev3mXlAOZIOyLk/cpJFAsFuEL1UkZES0Wvjk9Pzt9i8WISSz56ez8h7dUwldd6uXty3f5ARIpOv61ousdvQuEruYG/PblRR4eoPaGKchjg1ZWrAlgr2ODMIsUFarQksC/6SogmNqgRQL2hdbvm2rnPoQaCoCpYBATs3KM37pNJBK18f9B/2dBh6+r/4f6QCvG/4a9Wv9/Lf1/TkEoEYYDnZh9kUBJQ3MqaFdgres5KN6p7wY26lzJWgC+4gGoET3G7xzQ22JCp6Y2w5pA19NcpAusgyTWJQwFum18b0Soq0USYC/i772Azr42eP5EpnT1FkYOgU2BVSMbj0TglEwjNuDWerCmlIQRJAd9qSGZLkNVi2m/pXYbytR3TMo/AsOnT1WAKEG3aIbJvvn3HBYT8oTTXI5ZbA9MBRhtXLIccCrCfiDzStQQIKd/e8eLrXh+ZUzspNzypr4JUse08G8GArMB00YA3VrTqXHbaEzpNQHop0NJgfJKR79UUaPhjtD4NlN705txM/HrI1CoaCJ08EUiXEqleZitd1O9BM3VoGNl+UpjaBYewADx3NIytqIODnRuPSjTcRPWt3kJZk0SKDxiDoyrCiyJJTZxqEAJa2bTRZCL07dXr89ef6AcvYgCJQZP9qhZzu/dSnbJm+r4sH9JNX96X6wnJXtuFSqu+ps0SF8M0NDG0fcbpjVj9zDkCJSN2LpF/cHVFDcDIzofAXNovDzFcTS1hyYQalPXm2gsgZP74uTt29Nz6VG3yx81QHiRgfip6YEC1EAlBZZxK27BAhN3kyXcfGrq4mdX/O21B+JuuCNuNPz7SFWwxU94a7nGAxZ3qFFT/hXY2a8sxtwM8QwhkANv06Oc2uKHdTW36ceYv3CI/11COfDv1WR5ZcbU62Pj3sc83ECgl6S9I8syW+ycK/BpqsCpT+SdI2QbBRtxbNeOj3raFQgB/F9NYDqRFV9NDAfPxikcpS2xUD0NiLIIAqshPadWDNPEUw8IaYLU9JccCMk968YFgQbS4goFS9oP1mylcugoJdcdwdbKuCnICO2/5BYQM9Y1NNl/bokNEbhFeqcIDqVLx5wzQGDnY6L1JjImCKp8ETA81Yjp4wXgPzSS8xWhf484BVDcOmleHmaJ7lrMhg4eWpjHfDbpYl/wj4vZuTF/ekukSj/CNrKMeiblFyKWkuaGfCRP7HNuEQlYX7HphVM+twcxx6XaEni4zDY54nB5mNvxAP0xEeu2zepMYYJASkd6gYh4xvYpvkl5Sn/o2z4Rs/K9EVOOUya4FLv/3HIUxc2WIGmYjwXEl1tLwLaYHqAcvUIfvyOzyI7WXovJhFa0cOIOkj3pPpDYWTF9a8aP2ZnvWdy9w7vswCC+sQ7/Bz6lChYLtWklSFajZTxRM5xkaeGRvkYpmcXZyXYSXk0coEgVz0ekgEzKCvWksCsVdpNClFai+OkRG/BilFyqNBSBey/FuihAWUhF12kRSUr020CEKbkyWP1SiyBCL+m8rwwuCnPwFgojYxIpLqb0wgPhLgqXSJRGSWlklvogQVvogpcBkWAryWTMCkBRCJCRmjtChem753lvVAF5De6t1qF/Bi1KRKweFs/sw1QA0/H8MB0gF96XYztBL2UAlw+/kFCo5AehBnjN/OuvgUBOkCAnsLPZch1TXCg7mW2QzZZ0Roe+CmEqioW7oZHEZ2YsEcoahuO6BzzvEBQ1cmtkHX3A99wbonPcTqYB5YhrVyK+UUY2wC9XqTGD0pLfJ21RX/SA7sYYIyJLx1y4AZ9tqxAz6F1ytzRxRlPwSPl0m6zV7WVm2CkwpMQ0gL80fG4VJNo9VeypTm9lWjxT4Zn+pury1NTHdDxiTq1kRoNLtQ65//H9/9TZ+8rx/36/tP+r94f19x+/nv+P684/dHVxbwRSAEA6+5MduVHS8zYqoyMpgWNPDRKzIKpkt7/Jv8iTWIylnYPVh3+eYmsabiHkvP9TdNhDfAlpjOYp/whPYqFe0QxQqid37hV98Scy+d/UZgWXYrKbgJETj/EEZeoYbmDBrPLfHuJQtLsgEDPiXyjioQ9+/AYKXDQF0y9YJLDNqFH4ZEMH08GjcUaGmc8/ARnx1zZxLGiz8fbRvBYRCZMOYvFdj1I8wRe7ETyy0MBPrflRJ8CgKWhV/Jvz/Ue0JUGPbS8CEY+5X16qSZNiQbs4nfOkEFeUhxzwLok2iA+AvvehwlJEJGCGAX4hSdQDf50/QAIShUQ8jQbgO4IurKmSa0fYITBJIqPcM5GZyMYj8T59LQOTJO1OFxx19yEqfxM/jXnFkYb7xxjNNwlpPP/6jF1doWN0daUA4j7QqTXJ+Ml12KkAxfd46aAZeqf44yqgl9YS6xp7SAaA1WzwbJFQDaBUY0mmiKBZx7g+wmpS7/9FLOEGi9ji8zSmsUPfHk040fSBOidLNvf9W3xJL7C9XdzSYhi+Cw9xDynGvXs8GgcEMzdM8PHEB9+Aot2FY0gvmF05hhg7zeSjPBFuJ1FWIPgnCniqWrSc4F8D/42ChuRj4TeQcsjbGguZXZZHpTAqobuyWUnlOHzBSoe8rt7REIviEKNpg1vUfoZIaonnpcwrh+WvkiQmfLLvoCzpY1lo4wWaChTA04365e9SXIX0YQpC5RPmd64t9PnmfrPFF/D81Q8Y7+GLObeVrA+0bk00xdtgizOeBr7DP/axm/64wB850KQxeljx4Qk+GMegV7noZoePUuUvqeWLniWeGaBRohIk98SwRtGrY12+ohwc6APkRkh+Od0skxskGOkeE0hFHTf50hfNHl+YgGd36fe/UFLr0sKLPiQXX8TSgOhSlkNJgIT2IHFTWCLJDGqUUn+YZkoKqeaVN11kYRJsG35jJZHgZ137eciUp+QuGh8ofRQGMoUYph3Fl2h7+86Va/MQOBr4P76TZHkK9Yt7dQtrPxBgjQiP2oq9Xjks2sqX9AB/nEs/PaZBtWy/E1vBFyOZJyaIXhlKZxB7n6DP5iH78d2jKr4JaWM7QQdvFF6GuDjvgJhXuMdFUl4JhM+FqYpxgw6fqbRb2rTMawsaDcf8Dr0aileBdidqOe+QRp4pWIphrYSM8g8wmo11KYAFQoCIlR/24f4IH47UDQadORhGj5xFNBf+XeZ40d5f3u3qp25X7TbUV33VV33VV33VV33VV33VV33VV33VV33VV33VV33VV33VV33VV33VV33VV33VV33VV33VV33VV33VV33V17/p+l8BPH7Z","code_sha256":"98fdd3ee6c681809e66907e2f434d57a9292a55f4be6a33a057f51712b1c959f","notes":"Gas = whole tx in pyrevm (21000 intrinsic + 6-word calldata + execution, cold storage each call), filter only, no fee tables. Parity: with latt.py's own silent cap (30) max err is 0.023 bps mean, 0.089 sd, 3.6e-4 pi over 53268 callbacks; with the brief's cap 12 the 11 callbacks with dt-1>12 and their follow-ons give the 17.9 bps max; 99.8% of calls are within 0.01 bps. Where gas goes: the convolution. Under viaIR without assembly a memory-array multiply-add in a loop costs ~160 gas (b2.sol); unrolling the symmetric kernel with constant weights halved the total, yet one step over a ~23-cell no-arb interval x 33 taps still costs over 100k: dt=1 calls run ~210k typical, 518k peak, and each silent step adds roughly 75-100k. Same-step calls (dt=0) cost 25.1k. Packed slots carry a sentinel bit so no SSTORE goes zero to nonzero (22.1k each). Coarser cells, truncated kernels and silent caps cut gas 2-4x and break parity (5 bps cells: 24 bps max err); none reaches 170k. A full port would need the tables (8 MB JSON, compression item) plus a filter of ~120k or less: SWAR packing (4 x 64-bit lanes a word, one MUL per 4 cells), precomputed multi-step silent operators or a quasi-stationary shape, or a parametric belief as real.py."}