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port_zb64 as Python source; pass params.port_vector as W to strat_init(S,W,x0,y0) and strat_after(S,W,is_buy,amount_x,amount_y,timestamp,reserve_x,reserve_y), with fresh float64 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candidate and T constants were evaluated on the supplied scalar market replica. IA is the frozen policy from the IA artifact; the CMA mean began at its restart weights.","Continuous positive T declarations and two anchor coefficients were searched in log scale; integer, zero, cap, and minimum-gate values were fixed. The anchor count cap stayed at 674.","Replica price and retail RNG streams differ from the Rust engine; Gate B measures transfer on the specified seeds.","Real candidate and IA were each run alone against 30 bps VanillaStrategy with identical configurations; no strategies shared a market.","Market configurations use MatchRunner draw order and ranges with each confirmation market seed supplied to the generator.","This item reports a measured policy and does not claim a top-10 leaderboard result."]}