{"item":"closed-form-filter","batch":"e05-c-replica","hypothesis":"Between arbitrage pins real.py keeps N(m, v) and, at every silent step and every retail-first step, replaces N(m, v) cut to the no-arb interval by the Gaussian of the same mean and variance, losing the cut's shape (flat top, hard edges). A closed-form belief family that keeps that shape (no lattice) should estimate the price better between pins and so score above BASE (stalerival:Policy, the current contract's policy with the tell and a constant rival pull).","verdict":"NEGATIVE. The shape is worth nothing measurable once the tell decides arb-or-retail. Form 2 (smoothed box: uniform convolved with a Gaussian, exact diffusion, closed-form cut moments) is level with BASE on the tuning seeds at every softness and on the holdout: see holdout.variant_minus_BASE. Form 1 (truncated normal carried with its interval) loses 0.3 to 1.5 because its diffusion keeps the underlying Gaussian's moments, not the belief's, so the arb likelihood at the band edge is too small and misreads rise 8x. latt.py's exact lattice, given the same tell and rival pull, loses 1.09 (32 seeds) to BASE because its pin is smeared over 2.5 bps cells (price RMS 6.3 bps at 0 steps since pin against 0.75 for BASE). The price error by steps since pin is the same for BASE and the smoothed box to 0.1 bps at every age; the 1.73 left to the true price is not in the shape of the belief between pins.","forms_tried":{"form1_tn":"Belief = N(gm, gv) restricted to a box (A, B). cut: exact (intersect boxes). diffuse: exact result is N(gm, gv+s2) x soft box with edges gm+(A-gm)/lam, lam=gv/(gv+s2), softness rho=s*sqrt((gv+s2)/gv); used in that form for the arb likelihood, projected to the hard box of half-width sqrt(h^2 + kappa rho^2) for the state. kappa 0, 3, 12, 60 all lose.","form2_sb":"Belief = uniform(a, b) convolved with N(0, s2) (flat top, soft edges: the limit shape of repeated diffuse-and-cut). diffuse: s2 += SIG2 exactly. cut to (lo, hi): closed-form mass, mean and variance (integrals of x^k Phi((x-e)/sd) over (lo, hi), verified against quadrature to 2e-4 bps). projection after a cut: softness s2' = min(kappa SIG2, var), flat top half-width sqrt(3 (var - s2')). Arb likelihood = SB density at the band edge. Pins and soft mixtures are Gaussians (a = b) as in BASE. kappa in {0, 0.25, 0.5, 1, 2} tuned; 0.25 frozen (best mean_diff on 62800..63055, within noise of 0).","form3":"not tried: time was spent on the two above; the lattice comparison stands in for the exact shape."},"frozen":{"policy":"cff:Policy","run_py_opts":"qm=qm_k1e-5.json (form, kappa, a0, ainf, soft frozen in code)","parameters":{"form":"sb","kappa":0.25,"a0":0.35,"ainf":0.35,"soft":1,"qm":"qm_k1e-5.json","SIG2":"0.000945^2"},"frozen_line":"FROZEN = {'form': 'sb', 'kappa': 0.25, 'a0': 0.35, 'ainf': 0.35, 'soft': 1.0}","code_sha256":"7dcda9006a8c76a755a9ca25690a0826bf6bc5a2435e38a7ff42110d9be94d14","frozen_before_holdout":true,"selection_rule":"largest mean_diff vs BASE on 62800..63055 among the five kappa values; no holdout retuning"},"base":{"policy":"stalerival:Policy a0=0.35 ainf=0.35 soft=1 qm=qm_k1e-5.json","code_sha256":"176f8115fb67d99340f1de1e6994042dfaa41f830c22e000595df3377cc6a2b3","source":"code_zb64 of https://api.imdnotdead.fun/artifacts/f5a140f51df99423d6ede60b618dbc6eabc37a3050ac439c80dc3824191e9f59","parity":"cff.Policy with form=gauss reproduces BASE bit for bit (edge and squared price error equal on 16 seeds)"},"tuning":{"seeds":"62800..63055 (256) unless marked; paired against BASE on the same seeds","comparisons":{"sb kappa=0":{"n":256,"mean_diff":-0.0118,"se":0.0085,"sum":-3.0185,"sumsq":4.703,"worst5":[[62937,-0.532],[62877,-0.4508],[62914,-0.4003],[63031,-0.3583],[62892,-0.3108]]},"sb kappa=0.25":{"n":256,"mean_diff":0.0018,"se":0.0068,"sum":0.4527,"sumsq":3.0331,"worst5":[[62914,-0.2898],[62961,-0.2776],[62908,-0.2668],[63022,-0.2524],[62861,-0.2302]]},"sb kappa=0.5":{"n":256,"mean_diff":0.0015,"se":0.0061,"sum":0.3714,"sumsq":2.4066,"worst5":[[62953,-0.2071],[62972,-0.1993],[62896,-0.1971],[62878,-0.1925],[63036,-0.1879]]},"sb kappa=1":{"n":256,"mean_diff":-0.0002,"se":0.0054,"sum":-0.0478,"sumsq":1.8954,"worst5":[[62982,-0.3164],[62852,-0.2597],[62961,-0.2444],[62894,-0.2055],[62941,-0.2042]]},"sb kappa=2":{"n":256,"mean_diff":-0.0047,"se":0.0032,"sum":-1.1949,"sumsq":0.6637,"worst5":[[62833,-0.2858],[63036,-0.1978],[62846,-0.1591],[62894,-0.155],[62951,-0.153]]},"tn kappa=0 (32 seeds)":{"n":32,"mean_diff":-1.4586,"se":0.1491,"sum":-46.6757,"sumsq":90.1244,"worst5":[[62812,-3.5188],[62814,-3.1503],[62818,-2.7354],[62804,-2.5959],[62829,-2.1902]]},"tn kappa=12 (32 seeds)":{"n":32,"mean_diff":-0.3732,"se":0.0874,"sum":-11.9434,"sumsq":12.0424,"worst5":[[62804,-1.7776],[62814,-1.7366],[62823,-1.092],[62825,-0.7209],[62812,-0.7046]]},"tn kappa=60 (32 seeds)":{"n":32,"mean_diff":-0.3036,"se":0.0653,"sum":-9.7163,"sumsq":7.1814,"worst5":[[62823,-1.0995],[62825,-1.0495],[62802,-0.9338],[62810,-0.832],[62805,-0.734]]},"tn kappa=3 (16 seeds)":{"n":16,"mean_diff":-0.7062,"se":0.1463,"sum":-11.2999,"sumsq":13.1166,"worst5":[[62814,-2.1326],[62804,-1.6508],[62812,-1.079],[62807,-1.0779],[62811,-0.9243]]},"latt.py lattice + tell + pull (32 seeds, comparison only)":{"n":32,"mean_diff":-1.086,"se":0.3552,"sum":-34.7509,"sumsq":162.9282,"worst5":[[62808,-8.6542],[62828,-5.9619],[62807,-4.8178],[62817,-3.4591],[62822,-3.2373]]},"form=gauss (BASE re-implemented through cff, 16 seeds)":{"n":16,"mean_diff":0.0,"se":0.0,"sum":0.0,"sumsq":0.0,"worst5":[[62800,0.0],[62801,0.0],[62802,0.0],[62803,0.0],[62804,0.0]]}},"tuning_table_mean_diff_se":{"0":[-0.0118,0.0085],"0.25":[0.0018,0.0068],"0.5":[0.0015,0.0061],"1":[-0.0002,0.0054],"2":[-0.0047,0.0032]}},"holdout":{"seeds":"10000..12047, 2048 seeds, each arm run once through unmodified run.py in 4 chunks of 512","variant_minus_BASE":{"n":2048,"mean_diff":0.0033782485285152597,"se":0.002921865665528221,"sum":6.918652986399252,"sumsq":35.81391574911636,"worst5":[[10843,-1.5901],[11020,-0.7174],[11630,-0.6585],[11963,-0.5043],[10364,-0.4749]]},"variant_minus_tell_qm":{"n":2048,"mean_diff":0.09198096214204299,"se":0.007289155342320528,"sum":188.37701046690404,"sumsq":240.06914565928207,"worst5":[[11696,-2.1156],[10843,-1.5864],[10822,-1.5854],[11022,-1.3955],[11413,-1.2355]]},"BASE_minus_tell_qm":{"n":2048,"mean_diff":0.08860271361352773,"se":0.007079197151580293,"sum":181.4583574805048,"sumsq":226.17274777636902,"worst5":[[11696,-2.0921],[10822,-1.5921],[11022,-1.4867],[11277,-1.326],[11413,-1.2691]]},"variant_line":{"n":2048,"edge_mean":536.6241886036014,"misread_rate":0.000301596112180976,"price_rms_bps":7.119130402708434},"BASE_line":{"n":2048,"edge_mean":536.6208103550729,"misread_rate":0.00029583134058055305,"price_rms_bps":7.118381544597329},"note":"paired stats from full-precision rows; the compressed rows below are rounded to 1e-3"},"per_seed_rows":{"encoding":"base64(zlib(JSON [[seed, variant_edge, BASE_edge], ...])), edges rounded to 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DN9Ued4MTaddizY3d/fb+PcDl43Z8phW9xmqLUvrvTeUfZyMS6OoJQE7ukIizF9hhyQ+vtMLMJMj4Q30EupgnyKEdW47XVeGvaF/PsoHMAv+bNJ0YCF9gcAcOURyqCJjIwKyDecMAhDfp6BKPe3ESKB1jyOFcZWsbZ+39ellYUkgMEW9Swet6eJMMCGMMCwSgsjJijQLsbIAlgfd78mwFMEwo7tRyrrNFI5d1KXxR4skrAiGpvL4J/BUzPYC96b7Lpo2KM6DeHI1GIZQNDU3o/9e+Nfu55wMS+B4woEjQU++6+/0AoCsGgoszBcVJ6MFgIGhHh2Kmgi/S2tI2cNAINK7d537XCjxvELE8DRlRVhgEY42cU8WAGupSY1xJ/5m7xT4r3mIFUHn4PNh8wzdp6mNAH2ikfsKLezrz593w36Yw5Toyedb/EH/ZRBmu6gjfja590PfhQD5eX7lHlMlz4hRtOaq7fSp9WBYtCPhn10F7hBgfsab+0ujfX7vIZzqOBTIJ4RSMm6DTAjTpInG/ituU5mM672jNATfPqN+g6oROX0OvHwDpQurJThfaX2yfjNUNhm+7JMxi4m5qE57ciDFCj4I0Xuc89T6+YKkc4t/dcjhGP6PcmngwdBKOwj5Zp9NMg7Iig+jX6I3Y5PmQ7vG351rR3Wvdg8hA9sWAZLxqThETG3upOWxE7BHC1/OoiGYIEJeDywGRGr5G0QAKUt4C3mtD2qyQegdqS6y+ZPzDtCP7ZEZbfETpaRHewPtMPdngdX4YL3InFDFc3pt0kieZoaL7J0KclA+V9SfcNLKg38BDvepD0O11fLMYIXwO9fSHfRbaR43I945trzXOs8MGBftWhrj2IhQK9KY9nP20sIe+dgNu76fNJ326JqPcQept6j/YGzDNJED1ETH/7MRH1YVawEau3HiyV+232z3Yljp3ffc/xBgzSd0Sb0yRPgS+DoCs+eT4hIglYPMCDGc94WHlOhBkr0jfERPbG9DaLrZCo5mdB7d7IWgDGdWNkDd4GbgNKWpobk4pyCrDo+AsuOT1kEgK/dnjwhbNwIUDw/eyklXuLMLFZxAv3xAYzhXkrxB3pNV+jewEmFPieuxpzn0uTinFOoJ26ARNqVjktwMxDhqPAsj3EkRGLZOl+I5Yby/qIlANm8fvqyOQAE5AfW/YxnTK41T3Xz38HcVJBdSRjbNFVVxcbaO60w4bC4BH2ldtrH0TmEFhsBw/4luSSDO79N4P2FAUN7jJ6MMCKKiaBIN6a+X8aLRBkOYi2IQZIUcg4mT1RMmhp473hwLDw+hh+Axvn28cHEluMLeY+S6vp1Kqa1BJZUqT5doXqs35hcBckoR1/ym2w/DXwRYn+UQSjMISr+Xm5CUMe7xttwso70RNSrAvYsl7Af0ffFw2h6wAQNv2OpXsvobsAZtQb2fBHifmEeHWk8bWtZDzRUWkkAwFj8g8EGWBjIyAHDtRMOz7s+xDYJIG6+YQdBb+KaSGQXLrbK8PwSLkRa67Hu27uYsAwNbAJrrqZJieWXD8Y90aWHhj/bBax3SZywhz2+d1FWx+++YKHkMsRJ3+30d/txsMNmY46spS5xVXocYl11bw3TASikP/FjAZ+7ZWssouhU5FZG25vdFiL+UyZIhOj4oCRvogJtyDx12F+rCOcaZ90siojrrANj9uVq6VJ/HDYZpzoWjDpkVEGj//k+sMdnzGvu799gLxDRCOrvKpqV3qSYf5WCmx7D301c1j4EI34XSwK3TYst4okhC0DGBLSRCTqPdqpiuTYxJmc1EylE8ManrCgwGXa4jKtPIpzkMw/nI8lBN+cwTvkbB42Gw+zyQ7FWALGjpywoJCHoPGAfITvjzgTOgxxKAQyPKEw2TueHrkgoKf3hLHy1jymXgcyuH4emFCgTBrnyPUqkVhDNJ6kXmcXrXbSa0MW3d0Ap+H5jgdeXWVhh9uP0t5bRs22JTm7UmGsyPhPTmmCFUW2yDS6+5kCnuTqr42Art+XNtsm2xrJWi9DCNQw3lSEJ1OZO91khoTonXBiiX8rx8XIGaT/uH3ROQX9EFvA9ZI+wS1HHAsmJYIQGcdDydo65EuV7z4IM2JedqGdnDmL0pZ3E3/t+LxLw+eCbuzPDjuKnuclLodb8Tmw5VzH3XrL9SqV2CJ7JfTLUDDJ2crrx8SEOdSRdcXoQVYsj89dAR6pgrFZAMktR/SvCW3ZTqT08JHI7Vofu3muwsDXwkxsIIT46Vd14GETe1aRJZIXN/qHWXRoRiGvacxYcE7vwEMEZt3lYnOYWV8kPeT2oEUh8qCgYrF5NZfeVtTZ/lFyJyNRTZmT1b59fC55Q+SuPtzpzWdGkctazGn148AaWo3U1cXslX+WvVIUQUIsduU2GANDrORLIoDbh4gqlxDi0tyKum3SHYHuHVhRyD40Janjo82tcpaz7Z63/CxMZiyigxgCP+g3/aoqBzytTFDk9OTiMZiRjYa8qE4WJdG+GOhZnFCkbt7PUUBGRlQPUT6WTQJSRTHwp9iVp+Y+zpENg0eUU3ootmDm0fzgN/e4fyFkScynOkrNMar3MHw9hBMmLJAp3FlfwCxH+AiZfWBNu1kXXaHZlpF8ZYNhRgATWGYapDXW0gBTbTHRLeDtT+E3Rq6XS1Y3jI/LwhRlQdpDmNX0OoKMZYMR5NxGJshx9PFbla/GACYQVjVvQu+O4jkc8R26iQELzuWUZwus3oQWA04kgRAd+1hM32J+HNdnpX2Iyg73f5MO4fap5OJMkrG6fdHxZvEgq/zc/HxR8HXAYEWO0IvXM9Hpiy6m6svHeF20pSbrtoDbMZEFW+p9O3I7ge4ccK6whWFrYjGDt1bQuBWmV1gtbEnkTDtf9wo9Rhbdhje/mWxAqMrPj1wNCJGweeBUeqos2lLPfh/CmKurag8CQYjoPW6RXj5VBgesuc/lfz9okyGVpym6mt73snDpoZe11Wy+TjHXhsc++ndg62doYX0+OIlOTaEtoKOiq+l1N0ilfO20TzmXVy54z2NyfUI0VMmJm6fZquU4hAQnFyJSdyhKyPKKtIti7vxV9766UKuH+cF5VD4234I5dVS3MplETzvIqqicvML0MVwwpPSbEsgZOlhz95WHCcwnZyZ/qGjjgtie61Wqt5GHRgViup6letoQid/931CGVfSWDWKGDj3jy8JNoj2RBXYUymaVnA1hiHw6cLOr0b+Kl6cNd4nfBBpisOEpgQ7gr2EmWFFMcY/cRqFV+ADEk/bF2y79ys6zbPLHwyeveaWhzRZ7NFaomubLhXsjJAi0YbSQ0NskMhKnF/bFSbax0BNbenKiqm4ysQlMMHrBrsXCDZ+Bk9+/OwephlrYmQl0+1QVEEuJUDyQ+y8GnA5GPaPXeZ5Nv5mTaHGfc/thuv0gb11/huN6i9CuhYQKYjofplStsp8rsipurlxxEcoA9gy0pfjbbUE/PsxmhiqmIjXePxcjUk21pcCbdoU2qsBhMBTPsQiHnFgYYLl6y0sDz+jOjxCzlrixcFaIWZGXSiVgYgAzoJ4dhb5k7R3I9t4MYRAxNsHAaP1i4DAv4adPY3Y3+iAYUlh5gPaBFmYrxgipZfsjrC0SQN83WoTzXb5ot+GT046zm7m2XD4y1B4m1bAQjjqd6y7RL0t7CxkGQZqPKgdY+02uftorE4KsElr7S76u41V7EE66+Jh4hs0JDpC7gPzyKt4O0I+OzwxrC2s1rC8HINcRWR5rNLDe3F0pIrjrdHdqHvJwXGwkVeevhHnh5+5iESoxT+mcHjLdRHvP61aR7tswCd/Pcz3AiOu3vpLLfRiRZ9FTmhWPPFQJWCCPuv297n0+O//2mCHYiCoM4SaMXXE3HwKpV6J9wEU+8WVsmThyPdJ6b4ED/xFVSR5OG+EoVOZi3axgk263V+7Hl/ty/6LCm+tq4fAGNnfadk6GJWeIzOVy2HLFJZzlY8iCU0hxgHlYuFecnd9FoTeyD0DZSHO4q6pQ9bCEzTarvQrgdznWpQavgbidw9zVAg8YruNX6w+7HuVFTBwgt5mnxzidqNsRZ+p81791HksCOn+dj0W9YURx2hxi++4zZZRcGyc391nceWnhwpnyhNg0XZl/F5oyuZHXo1LEYRTo9/zwjDsDW8AQ0DaMqGc4rCkiS4rHd3NeBkvbbB/3Yje5wKw2ALbcpytnNYVfPzeVP6Mgl1IV3uG3NQ8QmAP2DDYm9t6gYRyRiRS3m686KjEc393EssCYZUYgt8Fqs1iPDDy5PTUPJOUArPZJNxJ7RfXOxHRbl12FjAAxj5Mc2pFJn/gHhhlceHvy6bSuMckZhzpBJ7vg5W3kAV8nAFcGIcwouMAgsgBb2g8dfwTmgSHQbJcYofZscvB6UCEo1TzTH9avXaVNl0QdkDP8POitw1o5YXoHhlR9RgrIa8j4pfmtchOOAMZ4+VjByDuQ6GkmxLBDsIfo4iwqB+bxSY56j30kiLC++yDfOTkWiN5SwPvhPjm790m3T+VDkOUlZixPyZWg8oroaNImO2qTFyTM9diD5h8JASSu+nng1LIYl3AyFkitXlQSIDoksEmPwvpXFR/52rc4peiOiwqF3F7+HFZ/+nU111/9noQMvstpSaFIVZHLLHhvF38dwXgvcDhCvslxBg3JhQs310u8BJ1EzGpNvYOmBAqi/pm4CEoNuebAYUNzU+9gihF75pfUZk0B9B34mP3TR4BbBXJVc8VyyuzAiYbNxcrnh8URkI1avYNMAKF5h95ksB4OffGO/FqwobB+kEj7OtmdtpW3PVSKqV8FzibTHOoOk/gNTdqXTRUTD+t3H8suwGcguhcYkpukg2os5HGO6iyX6y13rr9S6mlW7MmyRr0DxQSAMysDrSk4TAq7jzzPkR4Rvj4wNCs6aLUx0QuMysU+or7BMAEmxbSPlIQuKTnlI0qw/pysMYEbtx6SPsSKYsDGf4uYUTB9TNsNlbvaOr5phcwe6pa6BEkyoC5qSln69RBZVTdrRe72eJvTIhtshEJykTA9c1WZiPEMPDv3mRTUYFUH1FpW7rNEv/L42+nJKz9+MckJ/87Nhx8a5gdwOve7Xa13lqqpjlPFB3IHHdQr1WVcbxynKCFETSGwhK6blYmHGgoweSRYhjR4UV4EA9g2GQvXyAuYRE+qw2K9QnYusLe0K4ax9fe7GoYj/l2Ja+QZD4N3qkMYX1DxFPFVmKyD7+ml6NRCiQaUAxFzfxbraVsCZltC/Bu+MQR/iHlKJtEGbRHm0z15Dmypt9QgzA7MoAe9AH1LuYxePs70qUp6zKkAVKIQ59lSHcHQLvR2dWBKIZ3K7epXfYIRg9fcRxTvCBscluttqAsQ7057paaIcpxEt6NivZEWR7RVcKiVIiIhPSS/VVOUHgk92r8ZOfj3gUUmJngkXLDEIQt/R7XJk5/djH8v1ysvsw8OIlJ7y1gAppebvjYLRx9WW6Cni/5GRijKV9NtMKUQ9azGnes1lJkKoriCvzpFDy5ZhovCHn6vrtQsRh1wO2x5XF1SHDnv+dnFHAwmHNGt4vqhxBfhhvs9DAhhNjPnfyYEA1JL/HOg1+8DjhS2/usy6XTmEGH1JvjX095I1WKYArxrT1uk91AePaXvlvTYQXoPMwuWCAuV3KyqWAYEoJ3gQMEZ2n24aNRfpBQkPCY/izDR9zOOxUmaxEpu7n+pQcSy1P15WAcxKZQDTpmrqXU4rNcrzHF00n18+/dF31ynGMlBYNjG83lvCfsQAWmhP8qEsBjk6abQTzNVCwNKl9hyaZc6B6mGSNxkOiPSgApnhrLmF3o6DEnBL63E0MNBZYaJgBwl0Smas5S6RagMmDTboYSSNEaINhsPYlJr0VYgZvlCzpOpaML+bj809OiBMjTl9qlUL+XOyKJ3+5JWEIz/8duAjlM8TYG74R37LAWWOX0RZ86QWhrJLB5DDMHW6kFyj46XQrftpF3sVjIaHi4T7xhVgJbKEsYzOrSk3mNPgYPypL4UJJ7G/6KZg4g33hih3gXqFLMIIruHytWrRFW2cyQZd6EcDCHflvnsInoJMGvms6MOYxaNeDKmjsp/hoqAbFE/511kBmTV2EfuA1hRt9CZB8YZRvhnWzmO6Ng+01DYaIYwpIiDsBSKYSRHMbNxwHRADJVZLFAvcnvtO+gD4s5jgrQCo3QTT0bCUWnzNMb5PIhKYdECJwJFJdk5GFMAz1SLSxZwrmVXvE9WM/pxwK8G79P6BimBOuBRs6RJkEXqaoCFRFcy0K6oOJh1yALZHzhX3g+/JDB9BkMKJfnjuuw87eby/a9kyyVFqtqqA8/Gza5iopjL/bwBhvohs9kkuzk8bECzQLVSC9bcZ4ggjx6GJs5J+2R1FWJPwGbnBCFVUdKPsOX6TUsEtiQMETeKZ1u8sYX4z/IUVDkgmPnQE+0iIkWysJKVXtMvlRQWS3uAifKiOBoVVVB26vunFQomEgxLEq8vtyC3K+VDRaWNUeBHpwbnpGey/uMRkWG1C3dUojeNS9EoWD1dmC6oh6qEtCXXr8coIOK6ENjBto9vg80IqZZJtDGAMN1k2kMpnrh/yAuQ34veQFmeHwYmFMo1rybyyDiAiw5fuXULruVXJ2UmYPECV8/PLrLdmbUBZnyQgSe6MNNxNkKgaUD2YCUkbkvaORQsQTMf5yPh32dCbbCL3zcDxse+XUFABO2IGa49TIvKYGaI4CBW0oeBygbQvYAd55AHQoye6bUFNZnVO1/p+iUna6gWkOhvhXAUvAbMBngRXocuwlHYkLdktL6h0x1IHsLEcy7DUZSAIsZXooWKqvKtdEzEzO4zokAHZRxM/09+eUiFBsEClvj4hx2h5o0Qa8jnHbIQhmMTkeK5Y7n+Wrhm7iMxJfQHQhM2IK7NKIbZgDXvZSYLrDpgpiJ0D0BePN3o21OBs9DQYb+6G6bUAS/VG+DO+Gen9GHEWhrfsn6nOOn0TyZGix8HJCkqCF2hO2FKeeAuUbBuOoeZvcv4OytKvqc8LManVCPpAbm9wlGoXCHaULgQPkCqmYIdv90HFSmkylZhTG0U3VM84lpE4qQ9qekrdLzrLttPU/hNljW9+VZipihye/DOmrhfsYXv2s7uKpKSb4vUHlIuzCih4DO/eqX7h8jHw5GxTP3NTokXoq29u1UXS6mXVT+JZehyIWXNKjFwubKwsVyvMD9BtFd0t8RZQAKhOEtWti3iKl6LmKUEvGXwlIyr5z7SNMKQVn2R5xY1Aw68NuHIfVT/I2XL8on2XtTrXalcLeSvM0pQB4y6ahQgALMkgWuOMN4g2Ca4FnOVcjIyoLnLIPViFOG3S8B4BSUG7hvQQ+SIeL6WPnlY2QhALBLottJ+6UwQzZkevEY3ayRXChSDnKRmmlGH4hXEjCjKHqAUWuGIFN/C7UFKBQ/PFEs+FewoUmMJLfs5KVJHWxn0nbIYUX9zM4rCOsGax1mUaKXQJtAfhLIHU6wVogfUVZEtmGRAx6kumZGV+nBELXWVguaY5ZgWkPSWd1IpaD5VaweGlLyNGuoS16g2I49ZqWcOWhy+DEoCa24zWCMEWi7x+9mphCpr7nbSXdC7pcuJjZqfRIsFhGH5lnB+uFPkcgl4oAoSaBZwJbsfA6Na/UQ5RchyMiYiUs6nngINS85ibhTAPH5V1qox0nlN0Ic2Ilnd2CYIfinWvj8jlX9Huoyl7I0CbjRVe27yZirTpke40r5J0aGaN4trcvcjTdl9rTGbd7qq8UG+tLesu5Vi5sdGBFJX3e8K5YNn625qBu2InlSWv95Njg6x+P6wmeH0TAXwzWarUjM/EmoEustQBANd2Eao7oQmVVUI5BJrbiJRykqmDwznPORR2UZL+cZ2u2NoVG3F1uxfhSVVaXEJi1+KgjVSYSbBaPl3u7he+lvzq2BH1cowKbH4V+FQ0R7YxJmnh+gBpioX8ZPofaWNzeEol7nlNpt16STiUdbXPYxg1GFhBdG1bZVC5kiRLAl/ugIZFalOUipR75UFxZtMgkLTAljzOBAyp6SmcHc//ujUXQIfGdi3O9MFeyAIsmIvz4NwFITvWFeCdGXuv1Qwu4RWAEPhEtnbrISHZGfP9aqgZuk5tF3TP7CjrgTXgScjH/GoiZlyiRYdrCSlQoW56dCFmRE7RQMVP3gW/+rszPSDkMKM/81dBktJJUA7wZ52+2RlARNmdaVit5LCihI8FqVDASPt2HQQvb4SE1p5Gmk/D8r8zcjUg7QaSyOWh9oZjiq6/y4RKwIPktsRikqJ28+/zAhbUzg9mJeWZFSJsgps+KsscWeZTmw5nACMLUbLBslHKW0Ft0kp5Sn8Zj/2C8irMYMAnpffdolaTw1h1qvnPldBPPKebwKrlTrmIC7DZwAe9w7sqHEVzh0fNx2ZfVp/MN+IWQAwBRF849ddoWzV4vIW6mOBV1ncC4xHLUbNgZZPQP6Rq+j7eYBVqpGoe8vZ/gNhh5j1CFEM0BeK0PWeyP8wQ9/414Z/pYo5BSRUxPptYzzZQCYs0KYwYu+acCbXfQ9POtImMxh4sjDgZcDI6aqGOulMmFFgY1+RVUyQRJCQZRQImwBP1rUjdjg1+ZEQWbnPofdNkwSdlssvdwxyAOH2ZiQjsYfaddiwi8d5uF1KwZCjI2a9uNYd6dNllv4mIZsjBIlYYbZh7PqIazXpvJ3s/lcpANRRAkvGGiJSVsocVD3JfTY100gMrSV+cqU2ELV5YcS+bcKtl/91LUIzb2B0SIHN5xL4JtVsY2qTAeWcYytjwWKhUNCKcDhWfSXFrggyjq9gdSTTEZyKybohN6twGFIYxJvbLNdLCxTCx8BU8cNW8iddCd1W1gVhkRlk67n9UntyCxTIrBIxr8pKQqorz86UD8idKrJ0nKMml3+bcqvWZauMp1LZ6RBNF66VJClsfMFcr1QXhV8gyCoXtiLWxQ+99eG/zoEpNaulVGvCNBVC5jBlmBeh2Zd2yWWBoU50pyGxhzgsXheiA9uv1USjIZUBUqq2vLCT0EeCAw5MN6AovF6xoCFMUXI9ak8ri76IHpesx329XhNSq7mP2NKi79zPoqw8F6ZKPwBrnac5uMIYSswyga1d87uXIkvcj4Fe7ChnzuL5I0x3wrPfXYc2UE3MzVqltFGOSAxWqpkXyuqo+iJfHYk9cq8mMVYTZOMmS+2M1d3fVaTO+oCLWng3S4kT2g5AH/pToWcOzWnQDID9e3qxlLBTEL2EQ88cXC1WGgIzJRyVIiONdBxPRBzjYo470asOBc2RDhLjbGWsDQXhNhlkPcm0Kj1zUkEZk/rGMnJ7kGo6xp7rNwcHzHRiemdoTd6OjO4MWs4RDLJBdKVfhZ55B9MF5VCUFXL3TKteL+sD5z4IdrECt6kiN92M5N4+DLX3n0B0ZYLnrSCg/NP8cm8iuQfPDXFgoL2XOiWhy6wXglh5SrGGxvapMDO/eigFtemHYj/2ysOSc9Lnjb4PJaQuK6+B9WuXHOvqlhux+VKXyru2S+ivPwryuKvK9mLFvn8WttRAlKMKsxesBM+n1bLdrLIJKIrOn8Z3rXTtJwt9EVFb+dXD8AaV+VF8VfKrl9v+mDIDpnsemT1w5BfBRJdKOXOUkjXxxFZ+dFuckkTumtqOytWGzEuTADMQtpwgKtT1k2xxJasO0d+lHaWn+RlSSEaB27jXb3pusclJAMX4zuK4JXRHWdU1k6urdVtTtgl3pi355lVBbCq++PGPeGz8HWB+90gKjVKlnF/uhiNKE8iKxPzuGdTRqiScYnH0ex0dkwI1Q2JGJiIf4MNNC9aM3Ef5Lu5FlNrI9ToBp7AQeP6n/fLcgMYc/01OsULTfLhEh2jnCHynTQG/S3TNaiXjoVsYCrSK6367FkopFnXI97qqYOepVCBPZuhfsVyGC0x6lt/rUyr0d+cum55zddI77gJEzcE63NtYcv3ldUgPA61/UilqDufoKGFnpdjaigTgWcbN84nS3iz+qLO9lHWujaow2lmB2cm4KEJer0k8wgd/VMj/LVLcFzGDTarmiEVP4lF8slLWHIqsV1ucmUa1leNCEBeEzLSLdQ4lDqJ/FvYUqlnwtkD3muqcim7/0O5C4/krh7FYYku7Xp71wnyL6XZphJFpD6VuDxHKmr9Vq095pjFNKWtOMvRUfGHn+s1gDLYy4s3viqF/htDE1UpZ86JIEwlUuQ8iU5tBQuJObzovxGUS6KHGam/UyTT9fsy71jQksWKx9mm425BApRiDIloWbaxYjZG+h6gH0LVWlcrmYOWwEBNUwDyPlhBmDYEZbDwK6TjvdVKIUVsTSZSeOuRtMgphTxWSiYQ2nJixgL3WpRQ2vutF1Z1NaC3NCmlzHnK1JY83vKVD2pwnu8CIfxi/BjVfJD8cieHuDCtveogrrbA5K5TNWQbUpfzwPeR55tJiNSVx+GW7FaGvgi9WwKwQNuf6weQgTkFIuwRIETZk6U0GIXicODPniiM/M4WGgl6gKAJXmn3QRBWJ1JrEldLmzBcLe8byUJUIifw4GqvleunFUGgEmLE8VI9JYtj9xb4+afMlunLMNWqbbx9RQi1kP+f0AQXY1tp3BmSFtjmoXMyX4OCDvBe47GS76luapFGhbY58EdL6KIU0u6I2ap2zIE5oO4va5oUCxsRMLZ4rhdVeGWUT0mqzBzeY/cdmn+t1ltPOmU4ZUkuhONTYSiLY9wG3BBIzdlViqrQlz48K2NA78W6PoYd/L9NeZ4Y+69s7PUvULN0snKDRIoiuv+l8cNyviGfA893dIfSeQIPHGoJTqJCiQvHOiY4V6uadVSJVmPathUoF3v+Z6Dv6EF2Y3QkGHJJkS9yWhOiZMeJhYpO4st0gOHUrxfz+c7ZQpUmG6pKmbItrSytjCIdCjDLRHICFvjmp0mJMx8VoXidYQM3D8XL5pacDhUti5hByfIiMeverGVM8CQfi19IENHm9Ut780mSSfmO6/yiTPEJS95A9kjREyhEYCw8MNaqwIyGBwGl+dalClIUDXya1svII5+o04TdTsMoNVTgRs/shagyJgSrqmCuZKtTNqX4xrILhnwVZCguMGfI3iyzsKXirS4VDNh8hbg6y7jQmMAjRyUFhgyvMEERYB1JkU1Jk+2uXOcUiTRhUWb/wNuAKbHMm7G1S4BxCENZZbJnQrBNbZAkDrSFYIXBOKTPW1OwvRk+Bc+yF1Q6X91xKEcFLxjpJdDMcoMFjqYTH7SKWb+ZVyjcEIZXLyGJPpPG6fZGlhBAGKKWlpn1T7xibRwe5f7s5E1ckIx9rV6VvvqShCsxtKM2pM0i56XhkUt8cE5ojGZaTm5vipVuUtWtDggrnVWpN1HfxfKPa09LplcCSx6ni65IEALThIYXz8yetK7N2aq/StuLJJMDvLioNJJsHZpnXQOorQEobqwzQlnu3DgJWmUEL3fdvkmXm8RiQZ/Yq0j/xnE2szuFC4hzhTqwmi9Ln7kyYPwywSLamz7Tr2yI1QPQ8ZzF/U4AEaH+zc74i8DCFGTo8qKqQygfM4osSl1UtgQh2cfdrgXcOB3Fuo3+2i3QFf7fzoJi0a+IylEmp/dynS6L7Gh3kltQj1DykkOodsXcpL5A5DqLGyW1kn4AIQTvF60XvEnzhQW1A7x009pb8NSw7+VgYwdySpmJ93kAhcM6zUYbPSLH9QoHzruwUw8iZoNa94lsDW9p9GPK0WeNWSSHwPAHMl1ys87eR51ZVfh5Gp3ZdErXO74tTVQqJy0m0QlyFujlrxIsxn2ooGTTFHxtZiyAqxbyf4KZZzOQ6u6U/3axA2+Dhjius2doZFQE5RHtrZg/LC8qfVqTyOQsUN+dJsz704HsYhVEGKYE7QveV4uZd54kC99eudD0ESokr7VdxBB6f1D+/vfOcmKU4go5TdHvVLD4qjG+5XMruiwlxnLbkx0TulrqFm+pG1muo1DZHeG7Kx7oOR1LbXLoRJPfedDL2fFRhFFVj9AzjpZw1hetBM8/lh2YpBXKhGWCztC8rhF6Fjmt6YessUJVLo5bR128pHzLKA7QdCHVzlE1DOZGYEeU6XC4iIDVkRbZwiU/TGY7PIcOMTDicFODI0mV1c2TMiVlztnOlMIeBJ+3HnudVcVluL2HOUaUkkhXt2VGsh2DSsHz+ZD+ue9o6OTumBeTNKU5xRBvwb0JR6hwGoIAt73SUpcqJP1YPrZQ3R4Cpi/vasvPTjrokq9T7Jflh/G0cV/gMjFM+DdXaqTh7LQAC4ljaES8+XBOIuf0zo6hOZAKbT4qunbY0TrW7xO93r09GwdJzGZl1e5dwNYlII+UTFdQena90iCsj4er0eNjaQBeWV2qc4wSJrVPYYzD1nLaHeUX/093JAj76rzqF3Y4gCxLwTYswcU2KnEPywUyn7v6BqlAnTXsJ/Vkocg71BeNyP0hR72hbgoLPzf11puXZhtxGYTgWqQBt0QxGproFrxHGPG7f3PRvFZ6bxzw87LKz+qt9rCO95uDuCWzu/lHlAi0Tb6a3AqqcLwXQqfnuZXPQTx3WrMNannap5R8f5JtUs6g2W2kKoJdr6Jyj2mz4eLf2/e5iJnKQqnNS+1AHBTqLw6mIIeX55RXoFPvvvJVKnXNIfKmuUmN28LAYGNN6yGaHiZVHOBjSouXXRj7JkyiU9JHaYb2BXt5tkXWK8aRdh0rzbHigY74ksLATq3QCvusX9ySueiC+ZKw1Vz4Nn/FoO3b4tBOtS/VbNYdL+FAASbSlCZnzyXynDnJaTl9wzwNRqEhweLdcn8Bj0B+36wy04zl6MzhpSg2dbgJafeYECVCUAJGK9sjv+mAqtEMt1nOUQueFSmSW5HI/sIqvUlCNaI+SQuebIlo82tur/uCZe6y5FXrzGMz0iV/AIzDc2kR9nDqLNJGXMcSHK9e8uMy4ocAW7Q/MoOFOGGLsblUzZvoPEdvA+wV6cYe12JGFY8FBTV15pcw5jGHq4n6yTHUM1YhS3ZW1qP4iU7kgyp8gJ+TVETrncO0V5o5tNCiEQIKW0BtZVM6pcgFbyjaTSjUbdSiEaZ9vT6kkTGwuLnmaxap37n/l8zF5miEs+KZj20Zedir2xkrr/oWZoXGO0n9mHIFuXiofZ8k93BwbmdQ4RwKlCq1qXSlyTsUrse6KDbjh8j2nycLfoMY5yGpX2PIBl4oofOZZnmWxkvLwlNyd06AqBM4X7YpFtMRSpY+BJHDXMQYhKIylTaGo+TguRH1znMDlXFnPd9pJ1XXHh/yzW6IIi2bQDkm9kooGz5B7K6J17oItwW9qAzPW6+YpXt6yoGfN5YvFszyXA5i1dUs8WWdUMU7u9sMUODj2xMyoLVdoWY90Zb6ieG9B3KsSLbtaqW/OcxoV07VFjaQKJb2baJDfmEf1HpUvGjGxTMqbMw3hI45L7iPRO2o5kI6Ydqh3Xx5SDQwrClR5Sk1Qc6yksLkq7H6U+QcnOlstLCnSP7UVj4wo8M7h7y/5/TWrNyNSOuOK+LU3SbQ1yYuFBkICL/375RIrd/NVTq4r5day+l3lb0EQB2RDvaJO9ykcWbupBtZ8pFT7IrfUOIexS5Io1vb86jWbowv1kNA4h59BYXWmKbfbRYGbPOehflYvNM47xMuq0VsncyqHGvTCkfuLVdmLdJcT2J480eN3Go/FqWpOUD1bZ0RZDqJC5BzEdeo3A3faD+ssQIsAdm89EDrnum7icn626mgcksHx+HktH7jHopr5MY4gc86SgmvVJidLKHOOITKUuavegCFzDvYX42NQX/KMo8w5k+fLVSR+WxCmCoOZhOlWzVukOM75wrysCIbkVRV97uRTUZ5+S2sC6FUEGuc81sRncIR8T5Vz0Ixoqrao5lekuRhfUpgpPHWEmyj48owFobuy6fht1gmRdtTdPiWXsI4V5dzFeMnuyhUmyHO95USPMAMcSudYFFl2DqqJvTQqnVMedEou1KsglM5pkeuvb9K11ekYYfDe/aO92ZJUiDysnGkRhKWDROd3m8EliEdWYSnK1ZPKatJwx4bqV+2yAngw6H8SohQ5B7teh+ol/QBaKUrGFRlrn7lFjXPoRPuYUu/ukDh/nUEfHmiN/TqHg6rFZ7p7YYHEOWQ8SLwC2trgynG9stwfmWYOJeVpkqNWN82TIQAqaK6vML1OHpJ6RTbc2YEhcN5BI8HiihBt1gOEoqiaIfDQG6qFo9IB6n29BczpnQfeCdBRN8qbwynmUVM8A8ztktJlNfv+Tl2sVKiCk4vyhnsjTFynVTmhOwgMCYv65mAMmjjocLZOe0FC/AgzaqY0aaa1aXZLu4rZIHJHvLmP9B6UbIDT5a5cOnCC3BAe5OdmiT7vJv7RzHBCUg+Hq1Kton2EP+qbQ3biGHK5jng73ZilEiSpxcolHb2dpXhZA9uH73p7pLo5Dn8bOQQu7YdZ7OJj3M53/eUaKSU/yKj4rRCKQpzkSLY4L7u1IbCumgdwu9kC2E2lZj57pSK8wKNvug4GSQJjsm6PHAXh96uTC4G4Fj8TDfV64BbwoEfIb7kPqCPVqKNPzN0Py1uQhWaZi1t1fiJyLACbYVA2xwI5ec7ZL6XMykfkWJRqGdmiWG8wXE0xUsZWuQrgNMQuwa5rZ5DK5hhcDAqvHD1Qp607nh53wi5AORpoi9gfOyd/HnIzN8mCsPVLjkDYnGLSVSJpFvarPCcNNij/zvQ6cnkQKetasWq2nCslfMbMOoM5bsf+Cnq/i+LcM7cziIpSWKJ7gGfDdPJ5idlBQFlA/LoqlDWyfl7RkfFuwtxn8wM1facQ6ChqjjIYFcVkknHrL4wtAJ1ugKQ5q3WvQhI+x6Iikb4bdRSJHtVQNJc+tOKlXpegaI7oTDNjpH3tw9wPqUiGeUJ14i6eEw9SSPOiv8/SYeJ2u2Lps1sN/nv4I0LRUeFSMlKriKsKpXKiBwcUzXUAgopnfZB1paI5tOaWuLO+S5XuaONRiSXCQ5VnQN+j40hJ1/HTkwWgU7vpIM5cLwoJDAzg+e6j84kZTIa8hl/Kp+7AvKFisxdn0JWx5tMZhlaAmSTMxrcm7Wye3ubHbwqR8zjBOXP6TiXVYjeKBBC9rK6k8bYEFLxErKYTr5s1Fr3OQCEXXQuZF6CjjcsE1eOC+oSqqfSIiI6/7Enz1slPrICvnx8LbunmKcgQH+y/riepcutAJJ5Q5L6BzQQRmyVp95qBw8ATo4nCnvamGqmt6mwLYlbKmY+jfRrFEs6CLWpHbVaY6VghP49VsFmFDVVsr1nSM186cgryeTO/K9Xr0lXYUL/nP1SY5PFCqHWvuQ/I0Ftpi4fLGzL0zMHUvTq2N8N+iEbCvQKYkTB0yFHzWUf54ENyl2fr/L1EvMGvhyGkg9Bv5AfqGjojCU4E0fYa9MxR40ENbTK03TnI4sF4wf+Os/HyEUkZGDz1nphOoz7MluQ4OD8ZO6BEwTot1jp0NJOS5ouhWWHL9U1izdVoeh+kZDvy3Mt4c/2QxiE8VGDed+p8A8nj/MqQKGler6QgmQjxe6FgD/+G7Y3MSPqB+lFT1eLAmee5PjI86M+1dNRRswfsjCslzf8T85n5XEuSiyTzAFeu7z6WVPoG3/xfOgqHYQCge3+pWJTBSx5JmadZ3vIkyje+x1E2nrnbRrKm262AVhXESR4IwRgE0i8PoSkRDqhrawMaVg0KQY6nvCC/QcXc9kUil88pZl4TRZq5vIsvh7dC6Nk7MzXNeTCRD9zOCoOZjDrDKc2jjJGtsUo3ff/4aMshKNDwiDvXHxvuNuAzlreKpJGGAKa6gqrmPOhAYJ+EouY8buH69FPfHk/ddCwk+cL2z9fRmV0o2idmvWDB3uLBXxQ/+tonk9fy0KDAmN/VidsooYEewk0nH1Wi0/7hYc7uzaOQEbn87WNiLSqCF5KNiM4rQNYcndso/oxcedol5UcRZ1jsjnCtq4PndL7KTmYeRZIgsHSj7RyqmuPAHtZKgrqYy1W+11XNlyX/ze63XUDmj5APfjVpqZZACZzcXKVc9NDAfbYzQa3K08WTgDqirVeImpMndUR3tw8KTXPQo3g+/PxRh5G9WzwOaBGXW1XTw/OegC13sTKLcX/tU8weaueOr2aGBOmK7UrKkVkqKCqK6P3WYWQZTtvCxNIVgtrZcbvPucHK/xF7IWlO5sowg8Xr367ibFJJgvuJn7LqvDmeTQ904IURPJCpYJIjVehNclvBV5z8E129CklzySc3YsxgSpo3hdREkc31ojAcHtB0IrZft8+FoVDNG/Ql36TKm12WLxz5XQofdI1iOCAelpA0xz6C9ZvofWM3qQJRAZllfu6H5ox0I4TNTUVziAVhm1ntW7l28+m9+3oB9uM3VWLOCOzt3F6n97J8jkco+XPBHcaB7jo4zseIV9Z3sW5LFRDbA39T+WDyFB/gTG/CZNlb6oiooEjvsHcL2TSdXFU/ZtdBdJt1Ketj8FE4/2jMCv2YXbwZmDq08W0aQdMcQaauYF+2E0iab3wk6O0DM3hML2/UrQG6d1BFjymypJd/vYjsIZeAR2kiSpf7s0ynMidAdBCKkuYFXIwpPLleh4hvR24sMFg3z8OeTE0CS6aWI65c8cdPOWTbmEIpKzHdPP7PAb439z9kqPCQBOr4+H2HKcBF7KVkpaBpzmDK1qTZGYUzFEMF+X2oU8X70CLVkbARCaCk+TG5/4RiQkFz+mGd2L6bT0bywHMmttx8scABGRWhh/60sAUjer9QKsJqytsKdi6/opFV3T6iH1Q07zJkWbLkX2VWHJRbgq1G6ZlXDihgz5vCqkX0qAlG7j2sBWwpvnwnmDeYbTQg5peBk5w5jzchRuiGcuaUPTfzJ7eX7EGhbdo+354CZUgWUwenf0WnUDNflMwexDAHImfOzCvwa2/c+Hk2bvsFR/cWjQOlyKRzZI2lGbX+7NdkEG+fBToENRcv0vYui7/B/87NN7U6oVABPFmKEIU6PIJXeHN7STVypYMItS+HDYVnRPTyto+YuY+0WgZVNffnn+yjyvgjZqNP5qjwl1EBQRp5+6XfKGUOKdBKSMfnGBALCqXfydrpPBCbJ61+zyIyedexmzMdRm0eHdDQz/jd/KpmpevMkZZBhjAUxS0UGEo1m6TMkQ0gZFyjaoHHNx5V7p20a99hST/Rn+MOS4NLyTElJZsVeig21XGN9bt+UQSTATmcWZuH3DpCR5B8NoXMB4mFwny9KxuD1Okxv7w4hczBsWA9Yo1pCUliRAN5FjRwpb0pRM0gTPsq5Rj3hDruFVS3ysWjK0cvIHeZjKWgIpZH4+08zOJqzQULFQ3+frhuSIwIEMvhFO1x4NcAQyE+RXYsoyFMEvohIWQOUtEb4ER/QQqZUzKvGdMu8x/sSOLIfSDdgpQVISOb5SgoC6kSR/IpRvVUKYJRrxyG2M1tFo+qZJoHj2nHm9k/nJBp/G6jSuymIwJu+oalPUunjbOP1NzEUufCh8XZ5g1VzJGKOobh5kZaK0zeTc0gd2WTeVGLMV+qecvRUdYpmD4+2so2lc+Tq6fpODCw2IiOrUHDnIIOpv/k6sPTX3keOvbufFaWxnceqqmicj876vKg+CM2VZ686yiyrZ9O9vJw45jMcg2WdHuougaIKtEIg9dcP3QOoItw8v5drE3u9/Xnep2+rE+z/3vwX6WAOfefKvNr5frjsWEXzJFwCJgvcmqUXPmGJCJQYEkfiVenzBncikFzbRKzeEDAHEfPlKnMsc2X46o81uUCvaVDWmLxzHbVBMx8V8egEPRcv2OTKhXMcfuqu+fpUUU3iq2sn/bZ4aF6g5WMwBQ+UcB8UpBL6JgJFcxRStRVUHsybhgO6KIhn58kxZnSftoS+Dj5tkzWWC4Wu4MJMWcqsrWWTvX4PiJrrZsOXwL2XL/MbTX2PM6mVcDiPqDHCBWjKk1r4Np5HFQPQ51WmCgoBMxJ/oIjB6nLvNWq/ynQqL85y/xdJy2PGt037VLIGhLI6pk+KMzA2Sdg8qK8teTyyXjkIjUCx5S7F5Yo6mTcI69hzQUekQXZEIslt6/9sCpOqbYSNcd6KO56KBlBzLSF7URZNdWNtazGtlYXZfnAdsr1jQ7Z9LHIKaE4W0eBU0r856QjVg21+i7x+pEhta31htLtQp1jty+eH8yaBr61ewGEfFhwgmi3HTPJ23cGYB5GBBDkx+hW2U+hgLm0vHUggXNM52jmVDM7ar75EZkCckHE7DHUB4K8NqH3XD50rgiNrYX4l9vFSGUf8diz/OxybFSHLJQ0b1UYH2Ud9srtj6oJcH1HqiXXa50fPpk6WoNQMIfbgj2pUxrGvYMAFDVfN7E6JMHz9HAYC2U/fpnoc6WRBpGlRWpC7iM1MMl2sfbH7ZMx16tTclJ1CglzCiE3EeDCL42G+fCJt4k0U/4bp/Uw2LQ+vS5qmINjz6K/8pESoGLOGYSCdcR9vExdLov9Tyfp8KRSt2sPguYL9yIPhlukYcGIB1LhHvnXg4BjE75Syf0nebY6wLikbgEFmKglQXUE8bu9DnmvrPtHRj23P7K2wA+EYLlbYVdItAzoUx/qpREFy1BgewNO4B7UuCZabb7e2FA80K58n5A65rXScQdOG5i3Dh+qrJLZ3EVcPp7MCfQ0vNWRT5UfptqSdgYshK4F2SL0FSLmNNXxkKTg5Z0uCVST5ND1KSJCxRyVtEyp1PbJXYCaeHCcwTH6KZ3Kgx1JPq0TzJdGVGeoCThP2oc0g/qydlDaJWrHuitgS7sLeod4v8mAXp70eHiUJ8DbGAgZrDU9qj31iWP1RnuMCIkQP/6zo5DQBmOLmE7uCmFBZxd4vnYxq9uRuGx0qihbtxCoJPhMo0oZc/i+fCwkWKfbJ8V4QJajOI/3jdt1gAaIvjyEOJdvaYF0CR9mPPFMPcS9FF38PmEXeZUlQmAb5+7QN2ABVRPa+b1Dot46/7R8ohYQMYewLhdsCOz2tHdGY5lKhSvz3WcwsH3ucqDbnTZkW7BcGbbFTrtrS1WfUR0woN4zlpAqotFK85FYK5NJ5/epsEKgQKob3DwhUE/76eEz19xa//W+eBOgA52MEMLLWlIHTKnFneH7GU/adXw6qQybm4fbpwr6dIRj2Pt3SodTabb1iSfeqUgbQh5Ad+NUES0cC+J3lyuV8O0jO7L2reJRYcWYjMnlw5u6sDsEcHkcSufRH0S7liAN8NznJXXAJJ4u45gojxJZN6qhVC+HhAFPaaJuuNtVodYF0+FAqZcjdLaI5/vZQ/Wey3TGyYda11RI1RLap5B2eZGGeP0OP6p3W0H9bveRnx2xNBKZldHcGarbLqJdxbhK1C7HgcFbkkspqWQMFtviVLZrZGhvn9LLityZkliWNB4dLMKjI7KIMhtSqQjFMFN2AKzx0LCoOpkipVCQLmc+eCk9bNuERSZrUXGDR4VmnaCHdNUL0O3O8D7yZBG2IXoUw4KCwuJVBaNPrq6QLufBplPxlDZzm8UIIaM540dX4+FpeBvWBdff6seT+CYPjSE60ELxcoiDHZHzHMiCePn/UvvKYnlV3gZHjAccxgShBYWtRIrY37iHBQWtQ4RZ8REyf7DTHQktLxZipl0HB7IE6NyPZE+p0K0zrYl2oZgJRdCPhzTt33wgGWqp90FNziJCOhRk9YZQz9mgX47n4DGVOBZQFl0rPDddZgVQa06jfjkZXxo7Dus2kEg3pZAq0VTHhrgtInmsoSdet0/K4ZMjhLB9zc+qcqI4Zl1EEmqotQchBEkiYk27qslJuAKu3P/6GOzqw6Hcjlwe7HTm2lZoca24HI91sAs0wlzfmMnlQTSU6d1ulwACRx1QoaVWaEUt6+t93IpG3jpUBMEeOCS0ul2KJIVVAV8Ms1HEnGIb2t1sYDWqHcKlteLtdDdXUYTRfT4BUO1U3nStFTXSfX0Ta4BqGsDh0dCkIMvNfIdN2SBhzkB1d8C6+2vxmG4VthDz9KzHqxK0XjX0uFb+Tz2eU3ytNB18SNMC6+12b4JKjlKjyVIJnyreKGKOgy6XD7xUa9dp8Z3HdpSoODVyjyBY17SazpHrlbmiocKzh/wwLMZbOoGr7Zwj0HjMYGEFGvF+95lczyYPrfkMkYZiR+ynpFYAM4XIiTqsygHa0Gnih1Y5RdhZ0zldxYfkLpSRM1waVMzp2TZFiu2StuJqH7HPkYb28w/rFS7FiE6G7Og+clmqNuY+NKqYg8NYVao+a66fdJL8d+ZXF5dREkYe9u8pN72YQuUR9vb//M//B2MPD9Q="},"price_error_by_steps_since_pin":{"method":"diag.py (test/scratch, diagnostic only): wraps the policy like run.py Blind, reads only m, first, is_arb; k = timestamp minus the timestamp of the last first-of-step call the policy itself read as the arb; RMS and mean of (m - ln p) in bps after each callback; 64 tuning seeds 62800..62863","BASE":{"n_seeds":64,"edge_mean":532.032,"price_rms_bps_all":7.137,"misread":127,"first_trades":446767,"by_steps_since_pin":{"0":{"n":401865,"rms_bps":0.754,"bias_bps":-0.002},"1":{"n":65261,"rms_bps":7.139,"bias_bps":0.009},"2":{"n":46828,"rms_bps":9.873,"bias_bps":-0.101},"3":{"n":36428,"rms_bps":11.301,"bias_bps":-0.089},"4":{"n":28530,"rms_bps":12.124,"bias_bps":-0.18},"5":{"n":22636,"rms_bps":12.519,"bias_bps":-0.079},"6-9":{"n":52640,"rms_bps":12.868,"bias_bps":0.04},"10+":{"n":36115,"rms_bps":12.811,"bias_bps":0.065}}},"variant_sb_kappa0.25":{"n_seeds":64,"edge_mean":532.051,"price_rms_bps_all":7.133,"misread":123,"first_trades":446808,"by_steps_since_pin":{"0":{"n":401838,"rms_bps":0.716,"bias_bps":0.002},"1":{"n":65240,"rms_bps":7.09,"bias_bps":0.008},"2":{"n":46850,"rms_bps":9.858,"bias_bps":-0.085},"3":{"n":36435,"rms_bps":11.298,"bias_bps":-0.093},"4":{"n":28572,"rms_bps":12.13,"bias_bps":-0.148},"5":{"n":22635,"rms_bps":12.534,"bias_bps":-0.053},"6-9":{"n":52614,"rms_bps":12.886,"bias_bps":0.028},"10+":{"n":36126,"rms_bps":12.824,"bias_bps":0.058}}},"form1_tn_kappa3":{"n_seeds":64,"edge_mean":531.576,"price_rms_bps_all":8.145,"misread":874,"first_trades":446187,"by_steps_since_pin":{"0":{"n":399373,"rms_bps":0.882,"bias_bps":-0.001},"1":{"n":64872,"rms_bps":7.154,"bias_bps":0.029},"2":{"n":46661,"rms_bps":10.083,"bias_bps":-0.12},"3":{"n":36424,"rms_bps":11.828,"bias_bps":-0.026},"4":{"n":28687,"rms_bps":13.127,"bias_bps":-0.058},"5":{"n":22812,"rms_bps":14.05,"bias_bps":-0.004},"6-9":{"n":53386,"rms_bps":15.777,"bias_bps":0.214},"10+":{"n":36951,"rms_bps":16.05,"bias_bps":0.223}}},"lattice_latt_py_with_tell_and_pull":{"n_seeds":64,"edge_mean":529.685,"price_rms_bps_all":8.55,"misread":263,"first_trades":446668,"by_steps_since_pin":{"0":{"n":401690,"rms_bps":6.262,"bias_bps":-0.035},"1":{"n":65039,"rms_bps":7.057,"bias_bps":0.015},"2":{"n":46865,"rms_bps":9.819,"bias_bps":-0.079},"3":{"n":36459,"rms_bps":11.285,"bias_bps":-0.076},"4":{"n":28529,"rms_bps":12.105,"bias_bps":-0.193},"5":{"n":22603,"rms_bps":12.471,"bias_bps":-0.095},"6-9":{"n":52668,"rms_bps":12.842,"bias_bps":-0.026},"10+":{"n":36171,"rms_bps":12.785,"bias_bps":0.07}}}},"blindness_checks":{"needs_flag":false,"runner":"unmodified run.py (sha256 in SHA256SUMS.solved, verified by fetch.sh: 34 files, 0 mismatched)","inputs_used":"is_buy, amount_x, amount_y, timestamp, reserve_x, reserve_y only, as WAD integers through stalerival.wad; the flag argument is the tell's own answer as in tellpol.py","no_truth_rival_seed_or_run_parameter":"cff.py imports real, tellpol, tellref, stalerival, latt, replica2 (clampf only); it never reads replica2.run state, the seed, sigma, lam or size","posterior_odds_kept":"pi = l_arb / (l_arb + l_ret * chance) on a grid hit; off the grid retail; never pins on a hit alone (pin option left False)","parity_form_gauss":"bitwise equal to BASE on 16 seeds"},"solidity":{"required":false,"note":"variant minus BASE is below 0.05, so no pseudocode or gas estimate is owed. For the record the smoothed-box state is 3 words (a, b, s2) in place of (m, v); the cut needs 2 x 2 normal cdf+pdf and about 30 mul/div per silent step; unmeasured."},"code_encoding":"base64(zlib(cff.py))","code_sha256":"7dcda9006a8c76a755a9ca25690a0826bf6bc5a2435e38a7ff42110d9be94d14","code_zb64":"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","seeds":{"tuning_used":"62800..63055","holdout_completed":"10000..12047 both arms","remaining":"none"},"environment_note":"gates.py edges on this machine: 446.79826 574.32668 529.637987 against the README's 446.752500 574.326680 529.609154 (libm exp/log differences, as the README warns); all comparisons here are paired on the same machine."}