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aCnA7uLjH1DTmziig/bHYN/RYBvPLp2sEgcxkyAgbczHdBp//KuutWdtPepBhpTTweKmh8VsVA0SAPHrnBXD3DCe3TcdNK1SKikKx92Ddq7AVRri3wY2YSeqPKhXd6dreqbBGs3sJ2cNvEdcmT5o+Q8rrzcvfEE43QKNs93W5jTiXytV/JbzCr9CanDD64aR65VeD07KdFvy615veeLxVqrRut8gZkg9iRZ7ZUVAycOFOWihd2UKnZjtOdWHYrV4HItBEuv3lWAfjCn2J7sV28g+i3hcXB9s5gtsfYVZDG/VvXd5XSxLAdjuXzQ3brd3u+maX4MZSFXIbr7uU2U2/30f9BGD9n/F064yH727IkLonyhXDkQSQWooDZivWbWkKiKHLN1iK2xf4EWhw2qgQab5mEXW4v4rfjSLr6OYWjKtvYz7fZTY+oLovObuxKHbg7/AOOs0BxmqRx4B3f37ZOSREPimpNRYzQDtsmQd6LHISOrVZG37yRPaTM2IOUU69MAqQEo8+OnASYGoMoZ/UfA9DrynZWzOFKWXStheTRfkTTxSeOBYiDECH8oX+Z3Pu+X+LfflDrxmINsIwJoxvSEAstzKzRD/uBshpm8t9d4n6It+YfOPJVERlTid+ANW0VujTXV3VhPRXmm23WdWCqLk3kjBoSwZm5vR8LvMzcGU+k3xl99hIGkH3z/iVoploSda7kS9ROWdJFvsnYfw0yzcW6qdoOZRmPsT3RXTluV5YZFd2Vmqn5r++rTtmI8feuFD4WGXcFXkAvz9r1HzEoIRz7seCnNwoQykuH+C0Sw6w2GKWFRHm/junyARuM8iZgFHYVxFqXRNu7Lhx0visKUZDkhJgIWxmGSbROIfEB4RoD6uAXPUpL4W+bu5MOORwnwKrGAo5gRsk3A8gEaz2gcJzSJDXiSZa6e3y5W+viRN6+Ow8N5dURy+N3ddWXHwp0NoKl59WFElSsR78e0z/gssKNhG27YC9hNYaUae3fjQD0VfNfLXVlMoAVXKi67N25MRuQC/u7deIe411BMwMJwxcLOdu+KbYA61ZiPj9C9hGA/g/l4JJG8Pv2NHyaDL/IRviv3Nh/7/pZB4bwwMBZPw1j4T9mkZhwvVPMqxuFCObXSbAg6lmNfseDq/dk/M2ycRjQmkOQBMkTNU4tD83CivX/WOG2oj2OZxwyNhUrjdGFncVIdDGvW+5uDYJ0lVyM5VMcBVYpemL4WV3Htw4BCE7sroV98scef7+rDaTN9/k/NZimNLqwDgD3J6Jif68cj6irmeainUZ5DypQ6lwUF9m926TfEk5M8JxQdxWBIwPmFuK6HmS7ODw2ymNCE8hIiSoY0TeKU8aJIFEHDjMUCLpa1ojjCWx14GRVlCYtTmvKSRNYigCwjOS9LJbI4DuM040WZrJZEeRolohpToCwEPydIy2UZS6KYCjpIqOplJEplmewCpUkmsZFI0UuymClY1Qma4eFjUUaNjlHRfaK7EQHVuaCZpLpveERdlMmOQHeB3bIedAQSKfci7uDbEfjIb+CU1VBn8K3xud/c7nbM92Lg3Ru8H998VAssAkJoFImijgIMSRpGDOSCGpBFcZJlpgJA5IsyYipAFJKIJLGlABmoY5hYCkCSjLLQlP8wwhieWwpAU5pniSl/CqEztMUfZzSVYmCKhpzlrCX7NItaomfA7IhasidJEhFmy54kIYZcS/R5SEliSZ5ADhDloSX5GDKNJLLknoa54peSepglLMz/UqlDipOmOc0suUMhZCL9kqcxDYmUPCExwBNT9HGWpGmWWsZPIJXKo8wUfgZWyFJT9nkMws9N0QPLQxaHtu1neCNKZsqehGjoxBQ+gVQuk1xXpp+yKJXaJqWP69xK0Er6WRIp4rXrisM8zizhQ7KYZLbZx+BYEmLLHrxIGqW21UcJ90G21QMlodIlpb/gHYhS4L9KAXIKXpzZ4icZRIgk7RM/S9MIxITShwQzykJT+EMSMwZ+wZQ+CQn4/sQUPuRvYGG5bfngIGhmuf4h3sEg2SLZl6dxxEzhA2qWmKLPGKNSDlLyKAVb8CABIh2W4fEzDD626CErT1lsiZ5kacgyS/QRbgCzzX5IwN3R2LZ7AqEN/GBL9AmoamSLngHFafTXmn4ahxCnLNHHeNUJN0G3y6dgjCkRhg/+HXTHlH2Ct7EwU/KUgZVLK1Tci9KWw88zmkgb0byD0Juzlt0TgncZmaIfpuBGZZDOtGLlLElN6UONJLcCfhyxluwjvFsrbZs9pWEr3AMRMKIjtt1DLiT9kRZ+BiWxbfdRlII+UFv2DARkWz2FUehfLfk4hPCc5pbo0f/EWZ/kI4IOVhg9uNY0lXLWtgMxIGSWzwfusTy1jJ7EGd6/Y4ofUjudKSqnCTaSS4NQ8RIieZ4SU/YJpFOxLXoIKJRmlujBimLL7sHwE0v0cQahPbclD7lMknQkn+FlS5bkwUlmytVRLT4Wt9O8jIHHYLbkIchESrtUB9BL5ariXyV8YGXCTcsM+EBiFMZ90oeULAInKHJ9YEKYJ6b0MQeACGwHfHAjtvAzcKUy51FeExxGSKyAn9GMEjvVg9QB72mzcn1I6ilpxfsIGojsVB9SNnAulvwhwqjBBNHkA6nE9vuQhOOwo60CIea5dsIHnU9oRFu+H3hMUtv+oQ1IZmwlyCEwEmopAbghSthfnPXhvVmx7frBcye0z/7jPMcEiCsA2h2RHla7zwxFEpsaEOMtgNQK+nh1XmynfJjjUOkRqM7IEypT7caCIO5IL5HqkAs6SU0doNBAZrl+SMehgdDUAFCAOCZt909wENzK+GMW6YGYCl40TJjtA9I8T6g91oPBCiV24IdEBuTaHukxSBtaPoAAH3Vy+FflfAmJOefMnI/JEUlb/GNvX99DgbWNt9YOsvYVR9ZOd3MzdHPB1X0x3ZTN6+fe+Q7HNUe4RvIZ9+HxfUtyxoUjtHfz8CJc/5+J8wei4oyfQRCrRrzGkFP22XW9UTmt9XkSrDUUZJt1JVZR9UhgP+F/hqJMb26QXFrXq81N7dnH7RyXOM02zV6le2NX/Xh9aN3UYd3rdMnv1HBsfbL2PR0aV4Mctm4Vkt/Gm6/2lUqqD7ag+44PPj6P1b7HynmhFUrxy95sIze1i0f4s7a6KWrdA/eJuVkJi6yLVLqMmS6UdL/sSe6Jrcdf9oBnDo24q9QOpg4A8NVv08Q57Fpthy6INS6CC9Aa12jrUWx5uc6xBmYn7EC9dKeBsf+79m1mOwOJY+haHeCsDgDMP74eF2C66j2YLoK7autuW6i1e2xdnLZll+nsvTjXfMwl2uXyvfH9/tA8Qnuqd5kvHubDpd4DhNfyFM0VPPwgNJoK3pKCmrrnEozFetfBkoEkNFAU8cWDhjVIXgBKFHgWd77bR6Z71ID3Zlrg9S1LflcFdEFs3N3z3s6nXz1wJJU+CL7Y1KKTc7NbbYSLW36UewE/gBW4E3gPVX3nlV+W0+qmqk1Y87zrnnPP2y1fRABT4Kt/3CTAm3FN7+6Vum3t7ehXY3Fy2sDcrBYKyxYWBQjF2iIMWnacR7zbx7xNaAnToNp26mRdTTacImtvoqAz4DrqB/x3y06ERcX3QFtnCZrLAEQLgXrYguhz42Du0fZ2OWbXwRa50iD3fnOFlCR/9gPJkPsA+w/v2/fKK1za4MOtu8TLNqu+7IFiBU5GNUcg62L6D2HSwJfI5SlU3W/6NAj2ogxAEQS4M0aLM/TNiSEOCd3Gru9+22d+QB1wi8lkrZy/xBFwyPZhq+C6Wv9rVQ8EBTuYc/gdR48Rkqt/e8es2Qw+BxQxQGrnQNHcstSBYl0oHZxx56OkEXi7Y28a0OeRqkl9p9h6Vw3BafUIalLhHac3pfTOR9PFyXQxxMcDrh+ju+oE/w7vqgMXPd+eg3/UAsQfhVEqyNoVWGfFWvdYQCLBAf/djXYAJrDkyivypgL+25EJR8iv/trn1zbIBuR7U1exGpFzVmNFB4HL1WJs1UMq8MFR9/Ppamyaca/yzkRF04SFKSEGV31x1FcdP5LRCbEESJ9r13tZP5TlvG2wHETC95qsvvNO7bGCJlqOBcnU28KxhNdR0dLyMbJSg0lVl85KUhrIv215amIcV+cZhPbem6dcHE/2FYDJtXZqO7ZGL0WgOaqfHnqSW9eYRTY/Hh0XuCtb6O94WAQPuF+mGIp7AYbA3z5yCnMstdFE3AdNMhs0aeojpAUOEvG2mMVt3VwuN60+l9PqbrGYeMX9ogI7AUvG/zShmm+quiohG6m9f4mDMtjGfDEpHZeUrCfHYgC1Ua5UOKmuxuG6vxw4FDfGQflbGF8obJ9g3PvpKDv81N10ok31+NNRDFyGFkeDePjJB/dxAg5LPR8Q5Plsszv4NIyxoOdKuuNPx8fxSRYdDOAh+vNP+KUnNOGvhL8mJyQEbD1nBpTfO15KJ7o8GCzRfS7bvtPyn8frySg8sT0n+L+DnpvVGikdWx6S7ggfGXxr0/cg5PGAUgga8M69pze/hx92jx8O8Yngk7iLbtnX3WP7hrgAnX0naUJkUYNMxN9g4m9NOGYSNSckEJS5QpdNwaxLgdZJflNbgzVyYBUmA1hE5ZEm9UQUDHXBAelsKO1Mibh3chupdjMZssZP/+6ECObfR8fNXeh6vuPWPOQky5ob03f1cWC8B0MVB5KGnr7IRKu53PimXqwa+mG08Rj1mHRvprXtIbGkfZ3Iwx2kRd4AULoGaNhx/PQ3T911p7GoxBveAklhSxNFoT4QLTvhrMqbH1lXBfbMfNnXDOuD3LIgMNJWqPeqzTh5fe+/IfomIkyNFNjC3r3WmN9LKUkyzv9J5cDNmlItdCV+EDDAVqxr5ZosQ1y6oNIAvKlSd5kDdQIhh7CvZhVl7fkyCEuvpgsQ1BpCDXJjsagPvHFVDyFAf4Skdl3ilVqQc67Le8h03pQPNUgCd2gBy4rpVE8s7bVkJYKR6sOXx3W2MzWlrmkQfTJHMj0bsRQqHK98sXVs1b0cFdv4yi+KiJi+4fGr0MSIHQLI0RH/rzYc9999FXe5tMCwroCKo16oOGpBYV0BRdJeKJK2SUwVFOsFandLd6q/T+0u6R71d6jpj+xFuya/L3MFWv/F20cxY3cO/9/W0ENyVEKsdOiaDABWeXhkbwUWWT/m9z6W9ZtiVg6M/7qA21D3/y9Y1yucvhNpnuF1flSTd9ck/BHl8/3Z/wJ/Khtm","code_sha256":"cb6d945511e1e10af78baa64c2a6cb25806f0998403ce6440654bfc3c70092d9","port_zb64":"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numbers in gates, speed, search, holdout and real are local measurements from this leg unless explicitly marked reference or upstream. Paired differences are strategy edge in Y units; SE is sample standard deviation divided by sqrt(n). No leaderboard result is claimed.","Frozen Solidity differs from START only in weight and constant numerical literals. The port is the exact START port, parameterized by returned params. Search reused no training seed block; all 16 population members share each generation block as requested. Monitor seeds are deliberately reused, and excluded from holdout.","W layout: [1], params.scaling.feature_multipliers (19), feature_offsets (19), W1 row-major (152), b1 (8), W2 row-major (16), b2 (2), ANCHOR_COUNT_CAP, ANCHOR_LINEAR_K, ANCHOR_QUADRATIC_K, then port.TH_VALUES with named entries replaced from params.constants. NW=217; use fresh zeros(40) state each market. First 13 features retain the exact port built-in scaling.","Each real-engine arm ran separately against the 30 bps VanillaStrategy with canonical MatchRunner configuration distributions and seed draw order. The IB and START first 64-seed reference blocks were run during training, without informing search or selection. Retail/arb deltas use independent arb-volume-checked reconstruction of the real engine steps, initialized at each arm BASE_FEE. by_quartile conditions are equal-count ascending quartiles of each named market configuration value.","No synthetic stress trajectories were included in Gate A or C. Each records the first 2000 afterSwap callbacks of 50 full replica markets, then replays identical canonical WAD inputs through the EVM and port. Gates cover market sequences, not arbitrary adversarial inputs.","The KIT simulator template was adapted by importing START strat_init/strat_after instead of KIT aliases and omitting its standalone CLI; market mathematics were unchanged. Embedded Python port requires numpy and numba, and the pinned kit simulator provides market replication. The supplied setup succeeded on Linux; replica_only is false.","No monitored ES mean beat START; START was retained."]}