{"variant":"rep-a/truncated-Gaussian-moments-latent-arb-router-v1","status":"completed replica evaluation; target failed; no board-entry claim","seeds_configs":{"training_seeds":[0,127],"frozen_test_seeds":[10000,10255],"n_test":256,"steps_per_seed":10000,"ablation_seeds":[10000,10063],"protected_seeds_used":false,"engine_commit":"6b6921f8d463314aa7c96234e1be25a25245f704","engine_setup":"succeeded, wheel hash verified and StarterStrategy validated; evaluation below uses supplied pure-Python replica, not Rust engine","market_generation":"random.Random(10000000+seed): size uniform[19,21], lambda uniform[.6,1], sigma uniform[.000882,.001008], in that order. Price Random(seed), retail Random(seed+1); initial p=100,x=100,y=10000 for each pool; drift=-sigma^2/2, order log-sd=1.2. Complete generator is harness.py. Replica RNG differs from MatchRunner/Rust. All arms use identical pre-generated exogenous streams."},"provenance":[{"url":"https://raw.githubusercontent.com/gtmcknight/amm-sim/main/research/filter-brief.md","sha256":"0ef2200226b68aa58de6aad268442ba09ada710c54b3fb0ec35c77b491f78a4b"},{"url":"https://raw.githubusercontent.com/gtmcknight/amm-sim/main/research/replica.py","sha256":"d079712fa1f4850ad8337e656e7ee8dbdde806cd4762d597fae50f547ef9c7c4"},{"url":"https://raw.githubusercontent.com/gtmcknight/amm-sim/main/research/oracle_table.json","sha256":"3e9d1fc8874c6d796d4c26948d486b587f2a6b5610b20fba99923a60954cf4b5"},{"url":"https://raw.githubusercontent.com/gtmcknight/amm-sim/main/research/setup.sh","sha256":"dc7fa6a1e59f5b043d3954cf68d538318b7affaf5c0db3ca69a1822680f300d4"}],"source_hashes":{"moments.py":"442357523c6976509eef30ace418fe58c5780d954dcf5b7a93cbaa106d5528db","harness.py":"94bb6b4587838131bd45aa3d5c9da157dc0f55f73d27334d6d0413bb8340016a","oracle_table.json":"3e9d1fc8874c6d796d4c26948d486b587f2a6b5610b20fba99923a60954cf4b5","evaluate.py":"34d1d4b4a7c44a7f90cd56e82138573e86cf83eaebd2f07150bcad59ca209265","tests_filter.py":"d4b83d6320b1cf88f9d73ee3743988f648a86207f59f7f7b10af50035592d7a4"},"source":{"moments.py":"import 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zlib,base64\nexec(compile(zlib.decompress(base64.b85decode('c-pmC-IJR(5P!eFLcP3zEwDvCnw-N;`;tCnn(1Wvkj~A`jcf!*YXm1g0{!c|D*?s^?wo7l8QVy^tCe>5x4Y=_^76CD_VJFBe}s}^e??NcZ^-9QzmZG{ClXR9Uc{*+yL&Dp5o`!oadDrBBHpb;C1gQT6$nL?D0nPI;9g!{Uc^b3s)8iExTlKCAWbepl_n%f6OhPBOaCJU)eV&ym4C;@#YdlsHy6ME`sIV~vWrjfyI#M%_(jtK5t7}WCs}BP_7!R?l_W_lEtk=R*Vfvlp=}Si&4H|})p3^>BFkwWM+v7gRSDncG!4UC6gc~@$mzCha{KCnz$Z{~Son_ndZ7i(ucl<gCv3fT*LqYlN*dNpQT#j5JfL~gv~WvF$bn|I=f*NF@Cd|w?PI3y;9Hya&)o0`R4!0Uw=|dr=eXK3GfT(|l9sK9_GE=5B7#U5_DXYCY_LRLCU@Z$CiDXzP$GRESkob&J{aByaHOwY?c%f9d8$Z3Vo4MRjnx{zwXLz@qLmSKW@&4aCDB$U3(~shrnt3xi5J9i9Nc<bC>{ygGJ)e131gKP1YDi&b6^pmIS`qEA6bCOihCi^0(rk!j>iDstiVbAWE|rBdy*Zti62>`A>d@zN8q=>0t=GaHUI$$wFjn<^^pWD1hi|iK3)hvS_Y$**A!(JVA*%?>=B<-{o=gdz_5WK>IVW0MRA7{IPJhWHl)x)J-q@ajn>Ol8lszP+&st>&4>kdt+^)meH72I2a38k7<i7a#d-;`H+H-}wPRf!?6|-{T7el`D_THwK!H8p{UgTgjkGiei@cGgn$$SjLBgC>fXQi1Gi4PZgL(pK?7J8!N2Ec{fY@V4vB$=-7xich`KeW+8Kazp>v6QQakM^R<Z;xp*{B^jg`E|->@;#GmpmhQ&kEq9ggx*P>wL5^S@ST0thn^&^iAdQRNg^)3WW6m@)sm2G+5EVm$sTI`BRlZ$Y_^;?57uhB>x{E$HtHwgwOWO@Hs*01W1`Z>8K|{s1J({t20n#Gf+JfuHOpT=Y~X+etSq5$74h^=?{(wQ!Guv?<cV+$3cB4%Wd}qWtNiudC6|=V_RW!gh7El(hV$ddqjn%35eJ_jcMQsAY9>ov<Z~vBVWi@Xd%g8qhm&VF5l@a#JbSbDJa5Dp<x)EUQh5$X#s)<1cg}xGc6xi!~kfp#1fbmQx&sBZ59%(1}ltS6>VVa#DyRT@PGmN3ECsTU@nZMR%B)|Y{fvJ#5~^$SX#nNUK?>eU$SuwlwRuu&|k7C{FPXBnQ&gsL9j8!aEY8<xvYB(LfqGB-_odh4qSEmIG&*90YXY#1y}*4n{j2F+&(*J`<OvW<NulMv;Vn$jEs^Eb}uyK@!d!F*4?wo1QV{9O)SqYBQ+$9k{u<DmhwHbhN&gF7y>pId+4dZjMgsdE*e)8X*k->RUVx(dLTF2kz4qw5y;yf$W0IAx&!hSfxMjo@^&7`)d9$L59Fe@`&u@FF`R7*#MJ=A)eywMZ>FwMRL@lt0Iu+Kz~BIND*mgd;M;e*mG_)(_oy~L1z)WKo2@226TTHYXZr}>%6R=59l$x;$9T{hy4^nt-$x4hcYv>{>=hf8{2zec_N5)p6oP5nH+LNI?pgqEOl0PArFdiA2u6iR(IgaG90~@Z(A`k8&Z>Q9Uoi2?O!46qyqM!1OTyJZqOfuyg6@XJ*Vi0v3$atax&CoK>pzRF?|_QmB?_6{V;b>y$}MFKJqj?rbE(H%z&)IDNO0d!J+1EvjtH>e+qWKte^CF{ESsGw*BZg|Q}fFfu<g}@f3Q=g+=!QXj@L7wUAIwp<GYRas-o?q{G0Xd#<WcS4Lo7rqaQ?qXX<Z092gnFq%^bpG}2vx?YKXC=Ut{qf4w0OzEZ9^mvsn=ulMm@kcVrn_KL`*53gJgi%H(ogH5!7xyv)WSFu1IHj2ioxQclptfU0%c=)D?XC3f$dfvOQ54u`2b!>Yf^`Udy9tP|2WnJ%*R@gqw7}>*E-{k2CRi!lHnWTa6*HootA(ZdFd_5VknQ!hSEo5(*+aTSgO8DKSVt=2wn1H~^AyAOQ&fHy^HDK@m05~*3GX')),__file__,\"exec\"))\n","oracle_table.json":"{\n \"units\": \"offset from the true price p in units of 30 bps; ask above p, bid below p; knots at own displacement ln(spot/p) = -8..+8 x 30 bps; add arb_flag to both offsets right after an own arb trade\",\n \"fees\": \"askFee = 1 - exp(ln(spot/p) - ask*0.003); bidFee = 1 - exp(-bid*0.003 - ln(spot/p)); clamp to [0, 0.10]\",\n \"ask\": [\n  -0.8836,\n  -0.9345,\n  -0.8159,\n  -0.7282,\n  -0.6342,\n  -0.5652,\n  -0.2453,\n  0.7252,\n  1.1735,\n  1.4444,\n  1.6154,\n  2.7552,\n  2.9767,\n  3.3159,\n  4.4695,\n  5.071,\n  5.7838\n ],\n \"bid\": [\n  5.6921,\n  5.1279,\n  4.4766,\n  4.0233,\n  3.3293,\n  2.4659,\n  1.7209,\n  1.4329,\n  1.2125,\n  0.7478,\n  -0.2275,\n  -0.5564,\n  -0.6444,\n  -0.7169,\n  -0.7926,\n  -0.8617,\n  -0.8452\n ],\n \"arb_flag\": 0.1463,\n \"replica_score_1000_seeds\": 532.45,\n \"same_table_p_from_own_arbs_only\": 507.61,\n \"retrained_for_p_from_own_arbs\": 519.24\n}","evaluate.py":"\"\"\"Checkpoint one bounded segment. Mode 0 filtered; 1 true-p/inferred-flag; 2 oracle.\"\"\"\nimport sys,json,time,hashlib\nfrom pathlib import Path\nfrom harness import run\nstart=int(sys.argv[1]); count=int(sys.argv[2]); strength=float(sys.argv[3]); tag=sys.argv[4]\nroot=Path(__file__).parent\nhashes={p.name:hashlib.sha256(p.read_bytes()).hexdigest() for p in [root/'moments.py',root/'harness.py',root/'oracle_table.json']}\nbegin=time.monotonic(); out=[]\nfor seed in range(start,start+count):\n    arms=[]\n    for mode in ([0] if tag=='off' else [0,2,1]):\n        s,a=run(seed,mode,strength)\n        arms.append({'score':list(s),'state':list(a)})\n    out.append({'seed':seed,'arms':arms})\n    Path(f'{tag}-{start}.json').write_text(json.dumps({'hashes':hashes,'router':strength,'elapsed':time.monotonic()-begin,'rows':out}))\nprint(json.dumps({'tag':tag,'start':start,'n':len(out),'seconds':time.monotonic()-begin,'hashes':hashes}),flush=True)\n","tests_filter.py":"import zlib,base64\nexec(compile(zlib.decompress(base64.b85decode('c-oy<XLs^UkN%!tVGhkEhR3EmV0!Pp-6sK8Fu37{4J5yP$C%{KyU*@9dzLRVBTE{MbV;K@kW@u8Ac-;#=uubX7hY2&NKzzDHgrhvzRK$VNET$l&<)DqP=(ex&Eil)Gi3(lIZj7MiYPFSCwHJUnbUvdP8?W@s6=Te&Ukqe4Mzo1S5cr*n$o#+@<roRpn@VFUt)cQHAQX)=5YA=C5*gqHpjrOP>kgmMN&1e4eZIs=xoX*vOs`|o;UPtc^ghs=i7WB*Xz56wd-u~P}{lGL!az`;2M!iLlZ;JxuSJL6DLqV`R3zha<i3Z?CIR%HUoJtq_P#qCKgP35+4iKRaeVsKBT!*s$}Ot+1bzFEU0j4<W}uS5-}fCMxW8zTGHFq9d+GmL4}Y$*=}BBuIpi$$&1E}pmN)-jC?HOj4!wv1QF@f%fKVie;p!huQNTL1qJ2`&emwss_s7ps!`68h0T$U4+aS>NDarQ)LC^})Vdysc95wB50*Qvn<J=Yy?(QaERGR3rMYNKeGMm3-d$cd0;g-y%>GVh()Z4gUK@joe?P2{i;IZo{j0-Q2wu~{L%TN{4WzW<&|9f%irH?0zE7$|{z8QcO`i0RTi7DAYX;T>Bs1AcQ?a&^L)~CvBwdk}V5dXlP40TT7dE@m>`sX6x#ermoJzV?Mrb3y$d`7x!o1}(Y2jR_gbyuI4(TLWKgr|j6wdMS5BW>TznZ$)GmgIIEK(>n&z;OCO@~(GD;tR44(U~MSQs=VBEaw)GaYqS(@8nbjTO18SAtgZY}#L`#xZ&G9|(aL+kCIs@)i7C`g3yY4HBE{X`YB5ENMTMD^)@CE4#Up!eo+6PnxgOP7=2E>+J_?8%}1ynWdADT<-Ms0ayCIPHma<U(Y<zY#hyMwzl~oE3G_Pzp><Ytyenf-J*2rv!i2>_3{1X?SyC!k59~Ub7<W{lVHk@6q(3vv5&0N{VjI2%(8En`>4h5r9ri@+=wHgc0=fve6``rwLTmSZmD!FHw<+25qy#GL2-y>4Bx>nGx}0>lxbaH62qy{t9+@1b>Gzc*&wdf=IhG@-Yzd`KURn=%~$%C?$z(Q-R1pjhbahW*qS=4VszvaKdfEV36yKer9DGFBgt<6xGos8O!@UZRSMY+BLpJ>E=@;EeLZj(F4gUqbu1C4<WH;Pz<g&!Ls8#7Px+Z<KHt6$IKeE&`RIz*mW*PleXEBj<7KNbw<6_Pbu`E6+-E%~Mw*eSaVm;aWs%Mr!!CdLm<K^D$BRpHH_p}Ml>aD<Y&C;z>kaw?=3DE=(ML^s!KSt+XgZ-6JK0yd++K1;KiRC?{qeb%$V?j7TO_XeqkXbJH;>)y2LO#|m$IkJDDAKLgUN%imJdp(Cyuo;Gw_o_JsDYFGxgmfxsi)DmZB=Ewy(>`y&LS$@p+Sid0*~6Xy;lD>?9V-!$jI(%Y-q0)k;_=0PnNd#aT8tFiDQSs8gMdA8ht~>cHc^7_F8TtQ=JbcjhCQ7UF&9+OhCfNE-0gWzyafk`swGTb-!TyDXc>S4y5yo9?x<bpk1AksEb+E3v&Kdh0;vS~v9ZBwwWM*IFl?w+Ck8V_p#!!l_6Mq6PV08C+78Mg7aJR=Hz&lZ;zywQeTA@Jlgb$SO8Sw~i}cm0TeyDu<}IIi_jlS|LX=*WsM_c=Zd3(YDD2R%$_24%uZQcQ^9u`DL?e9Rkf^rZXku+4aCz<4WB&*Q$*x)UGJ6ePbb0l?$Jry#RBc+jih47q<jKw!4k>=Ae(`2cF6>U-Ep>GK$3Q{3XThO|etD9WQdJuO*X>bW*2I+$qszd&S-Uytw8E@y;wIIsU1ClBsnY+eGbhuj(B#Uf^z6MXBXAd9{h=vDeO|G@0Fc>q^Qmcf|y?mkU+@wDWm9h~v3_@nr`7&fF{qi(e;aviiXKT6w)_jLAl}SD%qPHW=*Sp&VT)4^I6-#t1p(l2D$0-HvcRo!FfUzMGIOnC;t5EqAUJtFxKDE_Rq6N|&;s^kh}a)29aR%hElqiQ97D9tHeKk*-9G`OtOq(fqpTT?7`A^@4;O$gDYY#)h&VjN{RH=6FJq%N8PPa%B-(er0{1vq1bF@Y}bJo!nO^JuApb^VFru7-nHGEc*~_C8x<oJ(|l&p<_X7O)FN#l&^!cTDT_;eqWXCQT1Br%;}d%QZP<LyNrzw6*wi-Q$=;b9Ce1O<MKcftyXfk=u9S&G``v?4%h0==bM$?2G%;g<9NNw>**Goi0|+vCeF@O+MhR9ilt2knVGY(=G>%dPfu(&H)+&*loItxzW$gUMWt(mYFj~Zt9~%8_Ics#HGU=@t^TE{PV>e5zD;%Z>ZY=5&u$pqzVELAoD!He(Y$Q#<h3JQorW06bYZMntWR#f-mzn9kzB~UOT}(O3vTk_qJdcRdKn`Z3zoN?=|^p!Fl&b-W=8CE%E@Tv<a!z|?-rXQ4r9XMr-f)P)j$^8trD6=x3kcuZ}>;`HhmuVUK4o=8QCUnuBlW4X%C|xi-T3FOz@^Qm>}gNNpr%e+07Jox3v;Ai?XS2k12Z}?^tD>7kr2Ec;F=1;wU%`DB_U}jh6>2-ofx<jY=x{X0ySaoN;kNOe50tTs*J4_H=l+^X>ZUEO9TMk~5@t$42e7g50FnN)^$r{i{*sC#_8UM&)q;f@WwLI^bh%UP9FSCu|k6$Z~y6QA>rmnXh`FcD~I*accTG%yp!|>sU(!*?OU-i=Ty&-!_rT0vYY5CKuQihskZg_TSnBSr0U5;;YeOOLB{^ekf$qK2$f^wg?w$YJgZzvKSMZ7-zXw7$*f{W-Eb8P`7QL)vq5$4bgY2(38)Pf|5@rW)YghhONm^mZ!TyU*r#!dM%d$ZtLe(p_Xm(OS@dA=p9k9zG|JZk`a|ld7oU1D>7yKk6h<2j;DorHBM2v+|k)z=aay2h#Skj?XR=F)#*lc=)rDm5BAlov;BO%?dr+%Aew8Fh1qnuiQ?yOIl=`q;y%*5C0cs@3qGa$O?y{bU}t^DOS|KpNR6#zNlcgbN~yP}&XhxEYz8^aQ#qU7bt3vIc&dEd%s?vNB?#@BI`6*DsX}s!XSVqo=vky}s%6YCp~RV0rqyoYV(g;()n$IF%`^9n<*3PKiqdF7zQADwL7%;9Y`bLz#@KF=7v{h*C!PHB{=#xR^jN_%C2=UD$gE%pij1<HK?x%A_5y()rGw_$fc}2<xSu=^7Efi2QdLf7;ak{c2){kp!YYstzs)og%srX#n>T(UFMg;cuq-D-ibgRa2U>d71PwkVvybjxphV$Hxtl_=Dd?2UaF9ibCZ|ITCCiEdQ9REv;QavFYzT|JsHE5T!x$P%q>vnR{!fB;csP9Uc6Ij%qiW0@;TVSDXe^7yF@z+~9*rPkXeJXwv)OD6LBnNHe8@nQwl|+|k-e;F9Gio9!RA;<HEFON1l|GegXF&*BIdprELV*D12-s9=g=6A;#dqzVt5=&q;V8O!IlJybLoHF34+28&&9A5nu<NYCW&n7hr?X}uBWy*us(*ST(-<_w#Nc_f8iAka;<_qGrR!vDyha4d247Cc&tP?^syz50ZjxrHaOZD_~SQ&w_7F=bGg7T_6r6-&SfZ3q$%bE3A!Ra&L2j>RwRFZ#8(t_16E_ypSR3AaQI_+elF2)^+3sc*Pke)2_WQ<uDa0%asb;9FH(D87RNm(ydWB!20fmFkN~ze0N_Q}*?y==sXD~jZVm{B=zzee9AIOii(nieMFu2Y5P=9JD^XW9fVmFNDFE-pUMTYCjR^xkP=By@(uIjT#otMsi>BRa;+>4g+)48BIq6PQk7??KCaD;ssE#Wyp%&@tKwaraEZj#dq`60h6KDo`b6L<W`FO{|QZ$x|P=C_zsP+07+*fi;RQ7O;AkqjL{k={h$P4Y-C*>g8HO_KJU|AYu1w}qz$T0PcT?_+I0oZ;3D*J<`>@#gt`bUEPzes%N)t*uzRZ~=I{|q?508G(3Y(_04?>Y=mLhiv$)N+r06;${Ua+H1q9z-5Q42nk!8HhWZU}L~-YCjQ#Sb^tFo%`qEAvfNp%7Vcw8H}lEZYVvbXTm)#dq$a2Oc|s+^(PJjL?+R2!&T3l;$I*bg2aK4ORA1SZi(Rqi}S!V`3BElmhU=5b1pCigW7wTe3GB${tHR}OE|E9IoC}s7c}^L4wA`-**Vw4^sk6MD+H({KN<)k^H+(xaP>Sr;ibS&dV>1rsv|KkyFACp&jtRTC#?wXDi0475C)JMf8F~X1-Cwfn7Zi$X4=2J<a+P_>ww?rGO)jU|HSay?u0uB@f0-uR}2Bj7|~?i3*!lnW~C2^vSK~#MxiUGDc_;j4Nld)SeIo=bU-0_oQejhVJXdp$X@oarULW=AWNo50d*|r-h8eKg$I@G#EHT|QCPQ{@NPw700ej%<teu)Bv7wNsK9+3>DE~=S9D{mbD~H-s1X>C@)Yu<)28E*|8dFmk3U=+9u@&-fE@eL@sB4ReaIkrH|ye_m%h1x#)zluKbXJ`*Y88F3LRR#K`QB1T~`~WpMWty>KENrKnpDLE6L8L0#fp6lKVHDd#R>S3~>w7Gg1$7^qY=*QLAvE7lU|8&;YmvhI@m6=0|~NL^sVqMWq#RpZ?|mHvQu8qBhioy#OKy?qQ9CMvid+fV4ow7={x$?E%Z@-+zy!_bv@EgWRHTaC--ZbkMdylR-1~!fBeKJvt?~Wa%8o{w>$ZXLqD3;&+P?|1O@282?Y&gHt2FdohqTu&WIPoR1sSW6(*@G}u+}praA*PfCyb!RfIYpaJ0wQU>>@rcn<3NBa}z*gY41N5KqqB$7ANyoT#B2S9!jtta6*@=rQXh2P&j{ocvH8#;-ye~S2juiiT9*@7Uy3ygbANcFm)=S|=$@zg%RmO*b}_f()a{DYx58b=fFX!wzU;kU=x!$4vSw%vMC;Wux5bd9sWg~vTC2>3}>E+88mN#YEEkMIAAV=wNFdU@WcprZjbGK#yy->UMwO4x51FPkJUCz3yjV?|?e0>|Sij7VlO@eGmp&YD|T3cpEc*b2W{uGwzGdjWO=pvng9{;eHjO@L9@>yuI8Q7XtdaG3%e3`_vUN{_jWVKjURBe9DnfI9gvNg(iFvhr;*')),__file__,\"exec\"))\n"},"parameters":"{\"router_strength\": 0.35, \"initial_sigma\": 0.000945, \"sigma_prior_steps\": 2000, \"sigma_bounds\": [0.0007, 0.0013], \"high_confidence_pin_threshold\": 0.97, \"arrival_side_rate\": 0.4, \"order_mean_prior\": 20.0, \"order_log_sd\": 1.2, \"z_quadrature_nodes\": 24, \"z_domain_sd\": 7, \"rival_nodes\": 7, \"first_callback_rival_band_mass\": 0.7, \"rival_broad_log_halfwidth\": 0.016, \"router_robust_all_fill_mass\": 0.1, \"variance_floor\": 1e-16, \"initial_fees\": 0.003, \"table\": \"frozen supplied 17 ask knots + 17 bid knots + arb offset; linear interpolation, endpoint clamp; fees clipped [0,.1]\"}","method":"State z=ln(p/own spot), Gaussian mean/variance. Every fully silent step diffuses with GBM log drift then analytically truncates to the stored no-arb band. Current first-callback posterior mixes exact uncapped-arb amount-density/Jacobian and no-arb retail likelihood. Conditional arb pins at -ln(1-oldAsk) for AMM sells X, ln(1-oldBid) for AMM buys X. Reserve moves shift z. Repeats diffuse zero times and allow only retail, without reapplying no-arb band. t=0 diffuses once. Retail branch integrates unknown total lognormal order via inverse router allocation and rival spot by quadrature; first callbacks mix band and broad state, repeats use broad state. Collapse mixture to moments. Use exp(E[z])*spot (geometric posterior estimate), fractional posterior arb probability as inferred flag. sigma estimated from high-confidence inferred arb-to-arb increments, heavily shrunk. Mode 0 sees no oracle inputs inside observe; mode 1 substitutes true p only in policy; mode 2 substitutes true p and true flag. Oracle values otherwise update evaluation counters only.","harness_changes":"Only moved independent price/retail RNG draws before the simulation and compiled arithmetic with optional Numba; event order, fixed-product reserves, fees, rival-first arbitrage, profitability tests, routing and edge/volume accounting retained. No strategy receives market sigma/lambda/size: init arguments are ignored. Observe receives only side, amounts, timestamp, post-reserves and own stored history. Canonical initial own reserves/public initial price are fixed as in replica. Full reference replica is losslessly embedded in tests for offline preservation checks.","training":{"selection":"max mean edge on training seeds only; no changes or tuning after held-out evaluation began","sweep":[{"router":0.0,"mean":526.1572664936475,"se":7.462322959168434,"rms":16.312713272152962,"elapsed":4.1102894059731625},{"router":0.15,"mean":526.2062346876328,"se":7.459895045884431,"rms":16.192369835600886,"elapsed":17.216324187989812},{"router":0.35,"mean":526.3416065959802,"se":7.454415993675985,"rms":15.97151363295655,"elapsed":30.31084033998195},{"router":0.65,"mean":526.3173221046827,"se":7.458123770073437,"rms":15.814776640953898,"elapsed":43.55819476000033},{"router":1.0,"mean":526.1781609764191,"se":7.460367130027013,"rms":15.779096549719473,"elapsed":56.684566077019554}]},"per_seed_columns":["seed","filtered_edge","filtered_retail_edge","filtered_arb_edge","filtered_retail_volume_Y","filtered_arb_volume_Y","oracle_edge","oracle_retail_edge","oracle_arb_edge","oracle_retail_volume_Y","oracle_arb_volume_Y","diagnostic_edge","diagnostic_retail_edge","diagnostic_arb_edge","diagnostic_retail_volume_Y","diagnostic_arb_volume_Y","filtered_squared_log_error_sum","filtered_callback_count"],"per_seed":[[10000,438240,461612,-23371,6131845,2164367,445678,465421,-19744,6267622,2075017,445692,465566,-19874,6254686,2085101,22460732,8450],[10001,567186,597547,-30361,8773580,2711345,572593,598357,-25765,8862877,2600363,572434,598262,-25828,8834821,2600749,26468345,11710],[10002,521242,552795,-31554,6807652,2518787,527968,555461,-27493,6931567,2399173,527785,555405,-27619,6913895,2406665,28397530,9579],[10003,564825,594855,-30030,8151476,2691205,573642,599411,-25769,8280365,2593590,573589,599466,-25878,8261585,2599223,29530784,11110],[10004,517897,544350,-26453,7250774,2440708,524586,547568,-22982,7374169,2380900,524625,547695,-23069,7358082,2389192,24942978,9882],[10005,523249,550995,-27747,7252837,2438032,527731,550985,-23254,7353910,2341493,527979,551375,-23396,7334606,2351403,23411588,9972],[10006,480214,506542,-26328,7415005,2372161,487623,510135,-22512,7529565,2298783,487318,509894,-22576,7502504,2303030,21775630,10205],[10007,519736,547811,-28075,7616682,2443923,527209,551177,-23968,7724259,2373490,526999,551020,-24021,7705867,2376424,26164485,10456],[10008,654215,687619,-33404,9459242,2882854,664107,692066,-27959,9618390,2736132,663867,691876,-28009,9582938,2735178,29902039,11978],[10009,645283,677918,-32635,9140768,2835611,653735,681568,-27833,9258565,2719489,653780,681628,-27848,9233299,2722218,29270085,12083],[10010,471760,498050,-26290,7475056,2384532,479558,501598,-22041,7656144,2284709,479690,501802,-22113,7636343,2291716,28022977,10661],[10011,363949,384516,-20567,5781524,1910464,371522,388934,-17412,5883753,1849909,371132,388682,-17550,5868975,1860070,21994297,8233],[10012,449939,477043,-27105,7337382,2372746,459004,481243,-22239,7528266,2275146,458564,480872,-22308,7500922,2279925,29174745,10220],[10013,395507,416477,-20970,5674177,1957001,402852,420493,-17642,5820588,1887614,402607,420365,-17758,5805881,1896683,22190778,8231],[10014,695626,732985,-37359,8657103,2738657,702859,735248,-32389,8767593,2663146,702646,735158,-32512,8749232,2673995,23199276,11189],[10015,620218,654482,-34264,9155527,2874041,632334,660922,-28588,9349927,2773440,632206,660904,-28698,9326284,2782767,33490140,11716],[10016,558113,628773,-70660,7047033,2441995,569337,635457,-66120,7195294,2338941,569316,635572,-66255,7174832,2345925,25188670,9594],[10017,489001,516560,-27560,7192345,2454620,494510,518841,-24331,7310499,2387523,494277,518702,-24425,7294123,2392603,23863223,10081],[10018,474178,501284,-27106,6922772,2443816,482169,504758,-22588,7046853,2338959,482130,504870,-22741,7030346,2351157,23533153,9451],[10019,449777,473210,-23432,6064037,2134558,455466,474954,-19488,6212644,2040812,455439,475042,-19603,6199673,2051552,22048815,8389],[10020,490070,516375,-26305,6727971,2385600,498151,521046,-22895,6846005,2320595,498056,521074,-23018,6830634,2330453,22312887,9253],[10021,405231,426720,-21489,5622835,2034070,413410,431313,-17904,5769579,1950349,412758,430891,-18134,5755580,1971741,24582845,8297],[10022,433040,456660,-23620,6078875,2128406,442037,461854,-19817,6181272,2025903,441824,461780,-19956,6167634,2038662,28531993,8757],[10023,628142,661626,-33484,8631575,2733323,635303,664303,-29000,8793098,2622559,634927,663966,-29039,8765899,2627756,25750132,10912],[10024,473609,501604,-27995,7215049,2467198,483676,506964,-23288,7387479,2374327,483381,506715,-23334,7368415,2374303,27560984,10451],[10025,440073,464636,-24564,6122765,2144052,447671,468139,-20468,6244427,2048024,447292,467883,-20592,6228988,2060415,26314942,9168],[10026,341610,361498,-19888,5342166,1896080,347393,364634,-17240,5462527,1842554,347363,364711,-17348,5456081,1853159,22059567,8354],[10027,543916,573026,-29110,7283749,2518027,549706,574399,-24693,7391411,2419707,549907,574661,-24754,7380201,2424558,26368597,10102],[10028,520694,551820,-31126,6734970,2385943,527766,554827,-27061,6863786,2300737,527635,554824,-27189,6841911,2309134,25361604,9218],[10029,437483,461635,-24152,7182581,2230538,444584,464818,-20234,7276067,2129621,444189,464489,-2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6499971,2465919,502475,527215,-24740,6652054,2395721,502758,527557,-24799,6634530,2402903,24160053,8944],[10255,595607,625833,-30226,8593561,2711401,601810,627796,-25985,8722793,2613597,601608,627620,-26012,8702235,2615003,25380826,11264]],"rounding":"Per-seed entries are integers: multiply each column by per_seed_decode_scales to recover units. Edges resolution .001 Y, volumes .01 Y, error sum 1e-9. Aggregates use unrounded values. SE/CI use independent seeds, not callbacks.","aggregate_metrics":{"filtered":{"edge":{"n":256,"mean":527.5894144662582,"se":5.827717957745774,"ci95":[516.1670872690764,539.0117416634399]},"mean_retail_edge":563.3913966474302,"mean_arb_edge":-35.801982181172015,"mean_retail_volume_Y":74985.36725756834,"mean_arb_volume_Y":24924.962862309094,"mean_bid_bps":37.31160255692386,"mean_ask_bps":36.27780027016681,"callback_count":2621422,"squared_log_error_sum":6.731890620828429,"rms_bps":16.025074438639756,"coverage_1sd":0.7534315344877703,"coverage_1_96sd":0.950706906404234,"arb_probability_brier":0.07494010720096708,"true_arb_callbacks":906554,"posterior_arb_mass":849959.7203493811,"mean_abs_standardized_third_moment":2.3351172689629354,"mean_abs_excess_kurtosis":157.1069579836465,"closure_observations":2621157,"max_silent_gap":43,"callback_likelihood_fallbacks":0,"mean_final_sigma":0.0009456719304654955},"oracle":{"edge":{"n":256,"mean":535.2169861452262,"se":5.834288503213778,"ci95":[523.7817806789272,546.6521916115252]},"mean_retail_edge":566.7139982810816,"mean_arb_edge":-31.49701213585539,"mean_retail_volume_Y":76276.09986144274,"mean_arb_volume_Y":23965.941748709884,"mean_bid_bps":36.81822656892639,"mean_ask_bps":35.843405479979616},"diagnostic":{"edge":{"n":256,"mean":535.0487389366422,"se":5.835180720148935,"ci95":[523.6117847251503,546.485693148134]},"mean_retail_edge":566.6434673991296,"mean_arb_edge":-31.594728462487257,"mean_retail_volume_Y":76081.47004852818,"mean_arb_volume_Y":24039.323013427165,"mean_bid_bps":36.99623739005264,"mean_ask_bps":36.052961292714635},"oracle_minus_filtered":{"n":256,"mean":7.6275716789680175,"se":0.08478079305157045,"ci95":[7.461401324586939,7.793742033349096]},"oracle_minus_diagnostic":{"n":256,"mean":0.16824720858394815,"se":0.011604876226322584,"ci95":[0.14550165118035588,0.19099276598754042]},"diagnostic_minus_filtered":{"n":256,"mean":7.459324470384069,"se":0.08418494324785296,"ci95":[7.294321981618277,7.624326959149861]}},"closure_error":"Reported mean absolute standardized third moment and absolute excess kurtosis of each pre-collapse arb/retail posterior, using centered quadrature moments; Gaussian closure discards these. Count only posterior variance >1e-12. These are non-Gaussianity diagnostics, not error against an exact-grid posterior. First two moments match this approximate likelihood posterior; silent-step repeated Gaussian closure adds unmeasured distributional error.","ablation":{"seeds":[10000,10063],"n":64,"router_strength":"0","no_retuning":true,"off_edge":{"n":64,"mean":518.5767536437858,"se":13.28915079471924,"ci95":[492.53001808613607,544.6234892014355]},"on_minus_off":{"n":64,"mean":0.0840301962831358,"se":0.061047876610913765,"ci95":[-0.03562364187425518,0.20368403444052677]},"oracle_minus_off":{"n":64,"mean":7.813926002936786,"se":0.173852491620432,"ci95":[7.473175119360739,8.154676886512833]},"off_rms_bps":16.424520391291516,"off_mean_retail_edge":564.8503856688955,"off_mean_arb_edge":-46.27363202510963,"off_mean_retail_volume_Y":74443.36380399764,"off_mean_arb_volume_Y":24587.263519045002},"tests":{"commands":["python tests_filter.py (optional NumPy/Numba acceleration)","python3 -S tests_filter.py (standard library only)"],"results":[{"name":"hidden init","pass":true,"detail":"All init arguments ignored; fixed public reserve initialization."},{"name":"hidden callback isolation","pass":true,"detail":"Changed p, rival spot and arb flag; fee and filter state identical."},{"name":"hidden callback isolation","pass":true,"detail":"Changed p, rival spot and arb flag; fee and filter state identical."},{"name":"hidden callback isolation","pass":true,"detail":"Changed p, rival spot and arb flag; fee and filter state identical."},{"name":"gap propagation","pass":true,"detail":"Gap 2->7 equals four explicit diffuse/truncate updates and current diffusion."},{"name":"silent count","pass":true,"detail":"Four fully silent timestamps, not five."},{"name":"repeat callback","pass":true,"detail":"No extra diffusion/truncation or arb on repeated timestamp."},{"name":"t0 diffusion","pass":true,"detail":"First timestamp includes current GBM innovation, zero silent steps."},{"name":"pin signs","pass":true,"detail":"AMM sells X: p=spot/(1-ask); buys X: p=spot*(1-bid)."},{"name":"quote signs","pass":true,"detail":"Below fair price: bid fee clamps; above fair price: ask fee clamps."},{"name":"replica preservation","pass":true,"detail":{"seeds":[0,1,2,3],"steps":1000,"max_abs_errors":[0.0,0.0,0.0,0.0]}},{"name":"truncated moments","pass":true,"detail":{"m":0.0,"v":0.29112509477279314,"mass":0.6826894921370859}}],"stdlib_only":"same 13 checks passed; original-vs-kernel maximum error 0 on four 1000-step seeds","runnable_instructions":"Write each source mapping value to its named sibling file in a scratch directory. Python >=3.10, standard library sufficient; if installed, numpy and numba enable acceleration. Execute python evaluate.py 10000 64 .35 eval, then starts 10064,10128,10192; ablation python evaluate.py 10000 64 0 off. Each command checkpoints after every seed. Tests include the compressed exact reference source and need no download. evaluate.py is slow without optional acceleration; reduce count per command to stay under 480 seconds."},"runtime":{"training_final_sweep_seconds":56.684566077019554,"evaluation_segment_seconds":[22.159821398032364,22.05911318899598,22.300262851000298,22.015857084013987],"evaluation_sum_segment_seconds":88.53505452204263,"ablation_seconds":3.270676015992649,"wall_span_since_first_source_fetch_seconds":534.7951452732086,"each_command_under_480_seconds":true,"note":"Evaluation segments ran concurrently; sum is not wall duration. Includes per-process JIT warmup."},"bounded_state_port_estimate":{"strategy_scalar_state":13,"evaluation_state_array_doubles":30,"constant_table_numbers":35,"router_evaluations_per_callback_upper_bound":336,"storage_estimate":"13 scalar words if each value uses a Solidity slot; packable; excludes evaluation counters and constants","work":"O(silent gap + 24*14); 24-node current posterior quadrature, two 7-node rival priors. Closure cumulant counters are evaluation-only. Silent gap loop has no fixed bound; maximum measured gap is in aggregates.","gas":"unmeasured; direct exp/log-heavy port is unlikely to fit 245k without approximation. No Solidity implementation or port equivalence claim."},"dead_ends":[{"attempt":"12-node z quadrature","numbers":"training strength .35: edge526.310622, RMS15.970011bps; repeated-callback variance preservation test failed >1% error","resolution":"24 nodes; reran all training and tests before holdout"},{"attempt":"increase router strength","numbers":"training strength1: edge526.178161 versus strength.35 edge526.341607","resolution":"freeze .35, no holdout retuning"},{"attempt":"single Gaussian moment closure","numbers":"heldout oracle-minus-filter=7.627571679; target<=2","resolution":"does not meet target; inferred-flag diagnostic localizes most loss to price estimate"}],"limitations":["No exact-grid closure benchmark; large neglected posterior cumulants show collapse can lose important rare arb/retail alternatives.","Router likelihood is a weak prior model: unknown rival state uses a broad mixture rather than a fully filtered rival distribution; unknown arrival/size parameters use midpoint priors rather than learned full posteriors. Retail arrival factor .4 approximates first-observed-fill hazard.","High-confidence arb sigma sampling is selected/endogenous; shrinkage mitigates but does not remove selection bias or provide a sigma posterior.","Uncapped arb branch pins exactly conditionally; capped buy-X observations suppress the point-mass branch and fall back to retail/robust model rather than a correct censored-arb likelihood.","Gaussian tail truncation fallback and finite quadrature are numerical approximations. 95% coverage is aggregate calibration, not a proof of per-history posterior accuracy.","Full oracle and filtered arms follow distinct endogenous pool trajectories; common random numbers pair exogenous market streams only. Diagnostic flag estimates are recomputed on its own trajectory.","No Rust engine scores, CH calibration, Solidity gas counters, RS2 comparisons, or protected-seed evaluations were run. Those are future policy/port/confirm stages."],"missing_work":["target gap <=2 not achieved","exact-grid posterior comparison and bounded-cost Solidity port","real-engine validation before any board claim"],"per_seed_decode_scales":[1,0.001,0.001,0.001,0.01,0.01,0.001,0.001,0.001,0.01,0.01,0.001,0.001,0.001,0.01,0.01,1e-09,1],"source_encoding":"evaluate.py is plain source; moments.py, harness.py and tests_filter.py are standard-library self-extracting runnable Python source strings. zlib/base85 decode yields exact source_hashes entries. oracle_table.json is literal frozen JSON. No dependency download needed.","delivery_validation":{"offline_source_extraction":"all decoded SHA256 hashes matched","stdlib_only_extracted_tests":"13/13 passed","source_quotes":"all four re-fetched and exact-matched","per_seed_shape":"256 rows, 18 columns; edge decompositions within rounding","scope":"required JSON outputs only outside disposable test/scratch; no tracked changes"}}